{"id":20256,"date":"2020-07-04T21:58:19","date_gmt":"2020-07-04T13:58:19","guid":{"rendered":"https:\/\/swarma.org\/?p=20256"},"modified":"2020-07-04T21:58:19","modified_gmt":"2020-07-04T13:58:19","slug":"%e2%80%8bgpt-gnn-%e5%9b%be%e5%bd%a2%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%9a%84%e7%94%9f%e6%88%90%e6%80%a7%e9%a2%84%e8%ae%ad%e7%bb%83-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba%e6%96%87","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20256","title":{"rendered":"\u200bGPT-GNN: \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u751f\u6210\u6027\u9884\u8bad\u7ec3 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901235\u7bc7"},"content":{"rendered":"
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<\/span><\/h2>\n
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\u672c\u6587\u7531\u673a\u5668\u7ffb\u8bd1\uff0c\u4ec5\u4f9b\u53c2\u8003\uff0c\u611f\u5174\u8da3\u8bf7\u67e5\u9605\u8bba\u6587\u539f\u6587<\/section>\n<\/section>\n<\/blockquote>\n

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


<\/h2>\n
    \n
  • \n

    GPT-GNN: \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u751f\u6210\u6027\u9884\u8bad\u7ec3\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7f51\u7edc\u4e0a SIR \u4f20\u67d3\u75c5\u7684\u95ed\u73af\u63a8\u65ad\u3001\u9884\u6d4b\u4e0e\u63a7\u5236\u6846\u67b6\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u8d22\u5bcc\u5206\u5e03\u7684\u975e\u666e\u904d\u6027\u53cd\u6620\u5728\u8d22\u5bcc\u51dd\u805a\u4e34\u754c\u6027\u9644\u8fd1\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u57fa\u4e8e K-Means-LSTM \u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u786e\u8bca\u75c5\u4f8b\u6570\u9884\u6d4b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u751f\u6b96\u6570 R_0 \u80fd\u544a\u8bc9\u6211\u4eec\u4ec0\u4e48\uff0c\u4e0d\u80fd\u544a\u8bc9\u6211\u4eec\u4ec0\u4e48\u5173\u4e8e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u52a8\u529b\u5b66\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u57ce\u5e02\u7f51\u7edc\u5316\u57fa\u7840\u8bbe\u65bd\u7684\u590d\u539f\u529b: \u4ee5\u4f9b\u6c34\u7cfb\u7edf\u4e3a\u4f8b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5e7c\u513f\u793e\u4ea4\u5a92\u4f53\u9700\u6c42\u4e0e\u9700\u6c42\u7684\u63a2\u7d22\u6027\u7814\u7a76\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4e0d\u540c\u65f6\u95f4\u91cd\u5efa\u9897\u7c92\u7ec6\u80de\u5bf9\u5c0f\u8111\u5df4\u752b\u6d1b\u592b\u7728\u773c\u6761\u4ef6\u53cd\u5c04\u7684\u5f71\u54cd\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u8d85\u7a33\u5b9a\u65cb\u8f6c\u78c1\u5bf9\u6d41\u4e2d\u7684\u8dc3\u8fc1\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7ea0\u7f20\u5d4c\u5165\u9012\u5f52\u7f51\u7edc\u4f53\u7cfb\u7ed3\u6784: \u5f20\u91cf\u6f5c\u5728\u72b6\u6001\u4f20\u64ad\u4e0e\u6df7\u6c8c\u9884\u6d4b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u80bf\u7624\u8bf1\u5bfc\u8840\u7ba1\u751f\u6210\u4e2d\u7684\u4e8c\u7ef4\u5b64\u5b50\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7528\u5fae\u578b\u62c9\u4f38\u6d41\u53d8\u4eea\u7814\u7a76\u8f6f\u3001\u6d3b\u6027\u7269\u8d28\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u63a2\u7d22\u591a\u5c3a\u5ea6\u86cb\u767d\u8d28\u529b\u5b66\u548c\u7ec4\u88c5\u7684\u5149\u954a\u65b9\u6cd5\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u53d1\u5149\u4e8c\u6781\u7ba1\u6fc0\u53d1\u4e0a\u8f6c\u6362\u663e\u5fae\u955c: \u4e00\u4e2a\u5b9a\u91cf\u7684\u8bc4\u4f30\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5206\u5e03\u5f0f\u540c\u610f\u53ca\u5176\u5bf9\u793e\u4f1a\u7f51\u7edc\u9690\u79c1\u548c\u53ef\u89c2\u5bdf\u6027\u7684\u5f71\u54cd\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5f02\u6784\u7f51\u7edc\u7cfb\u7edf\u7684\u8054\u5408\u7f51\u7edc\u98ce\u9669\u8bc4\u4f30\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u516c\u5171\u4ea4\u901a\u7684\u65e0\u6e90 Wi-Fi \u76d1\u6d4b: \u4ee5 Madeira Island \u4e3a\u4f8b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    Canarytrap: \u68c0\u6d4b\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u4e2d\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u7684\u6570\u636e\u6ee5\u7528\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5c40\u90e8\u7f51\u7edf\u8ba1\u91cf\u7684\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    21\u9b54\u517d\u4e16\u754c\u7684\u5ba2\u6237\u6d41\u5931\u9884\u6d4b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u9500\u552e\u6027\u522b: \u662f\u4ec0\u4e48\u51b3\u5b9a\u4e86\u9500\u552e\u7387\u548c\u53d7\u6b22\u8fce\u7a0b\u5ea6? \u5bf911500\u4efd\u5728\u7ebf\u6863\u6848\u7684\u5206\u6790\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u8ba1\u7b97\u6c42\u7231: \u901a\u8fc7\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u7406\u89e3\u5728\u7ebf\u7ea6\u4f1a\u7684\u8fdb\u5316\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u793e\u533a\u68c0\u6d4b\u548c\u4fe1\u606f\u6e17\u900f\u5728\u4e00\u4e2a\u51e0\u4f55\u8bbe\u7f6e\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4efb\u610f\u57df\u4e0a\u5e26\u6f5c\u53d8\u91cf\u7684\u7cbe\u786e\u63a8\u7406\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u6301\u7eed\u6f14\u5316\u7f51\u7edc\u4e2d\u6301\u4e45\u6d3b\u52a8\u7684\u6316\u6398\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4e0d\u53ef\u903e\u8d8a: \u591a\u7ef4\u590d\u6742\u7f51\u7edc\u4e2d\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u7684\u865a\u5047\u8def\u5f84\u7684\u907f\u514d\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5b8c\u5168\u91c7\u7528\u53cd\u9988\u4e0b\u5f71\u54cd\u529b\u6700\u5927\u5316\u81ea\u9002\u5e94\u95f4\u9699\u7684\u4f18\u5316\u754c \uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7f51\u7edc\u8282\u70b9\u611f\u77e5\u5d4c\u5165\u7684\u793e\u533a\u7ed3\u6784\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u901a\u8fc7\u5c0f\u9053\u6d88\u606f\u5b66\u4e60: \u566a\u97f3\u7684\u5f71\u54cd\u548c\u793e\u4f1a\u7f51\u7edc\u7684\u5e7f\u5ea6\u548c\u6df1\u5ea6\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7edf\u4e00\u8d4b\u503c\u4e0b\u7684\u5206\u6563\u7ade\u4e89\u5f3a\u76d7: \u652f\u914d\u8fd8\u662f\u5220\u9664\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4e0e\u4f60\u7684\u793e\u533a\u4fdd\u6301\u8054\u7cfb: \u96c6\u7fa4\u4e4b\u95f4\u7684\u6865\u6881\u89e6\u53d1\u4e86\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u6269\u5f20\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4e00\u79cd\u7528\u4e8e\u6d41\u884c\u75c5\u63a7\u5236\u7684\u9690\u79c1\u4fdd\u62a4\u6d4b\u8bd5\u4f18\u5316\u7b97\u6cd5\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4eba\u53e3\u8fc1\u79fb\u548c\u95f4\u65ad\u5c01\u9501\u5bf9 SARS-CoV-2\u4f20\u64ad\u7684\u5f71\u54cd\u4eba\u53e3\u8fc1\u79fb\u548c\u95f4\u65ad\u5c01\u9501\u5bf9 SARS-CoV-2\u4f20\u64ad\u7684\u5f71\u54cd\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u9ad8\u9636\u7d2f\u79ef\u91cf\u7684\u5806\u79ef\u4fee\u6b63\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4ece\u968f\u673a\u77e9\u9635\u7406\u8bba\u5230\u6cca\u677e\u6da8\u843d\u7684\u6d77\u6d0b\u8868\u9762\u6e29\u5ea6\u5168\u7403\u76f8\u5173\u77e9\u9635\u8c31\uff1b
    <\/span><\/h2>\n<\/li>\n<\/ul>\n


    <\/p>\n


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    <\/p>\n

    GPT-GNN: \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u751f\u6210\u6027\u9884\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

    GPT-GNN: Generative Pre-Training of Graph Neural Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15437<\/span><\/p>\n

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

    Ziniu Hu,Yuxiao Dong,Kuansan Wang,Kai-Wei Chang,Yizhou Sun<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabeled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph. We factorize the likelihood of the graph generation into two components: 1) Attribute Generation and 2) Edge Generation. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on the billion-scale Open Academic Graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u56fe\u5f62\u795e\u7ecf\u7f51\u7edc(gnn)\u5df2\u88ab\u8bc1\u660e\u662f\u5efa\u6a21\u56fe\u5f62\u7ed3\u6784\u5316\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\u3002\u7136\u800c\uff0c\u8bad\u7ec3 gnn \u901a\u5e38\u9700\u8981\u5927\u91cf\u7684\u7279\u5b9a\u4e8e\u4efb\u52a1\u7684\u6807\u8bb0\u6570\u636e\uff0c\u8fd9\u901a\u5e38\u662f\u975e\u5e38\u6602\u8d35\u7684\u83b7\u5f97\u3002\u51cf\u5c11\u6807\u8bb0\u52aa\u529b\u7684\u4e00\u4e2a\u6709\u6548\u65b9\u6cd5\u662f\u5bf9\u672a\u6807\u8bb0\u6570\u636e\u9884\u5148\u8bad\u7ec3\u4e00\u4e2a\u8868\u8fbe\u6027\u7684 GNN \u6a21\u578b\uff0c\u7136\u540e\u5c06\u5b66\u4e60\u6a21\u578b\u8f6c\u79fb\u5230\u53ea\u6709\u5c11\u91cf\u6807\u8bb0\u7684\u4e0b\u6e38\u4efb\u52a1\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86 GPT-GNN \u6846\u67b6\u6765\u901a\u8fc7\u751f\u6210\u9884\u8bad\u7ec3\u6765\u521d\u59cb\u5316 gnn\u3002Gpt-GNN \u5f15\u5165\u4e86\u4e00\u4e2a\u81ea\u76d1\u7763\u7684\u5c5e\u6027\u56fe\u751f\u6210\u4efb\u52a1\u6765\u9884\u5148\u8bad\u7ec3\u4e00\u4e2a GNN\uff0c\u4f7f\u5176\u80fd\u591f\u6355\u83b7\u56fe\u7684\u7ed3\u6784\u548c\u8bed\u4e49\u7279\u6027\u3002\u6211\u4eec\u5c06\u56fe\u751f\u6210\u7684\u53ef\u80fd\u6027\u5206\u89e3\u4e3a\u4e24\u4e2a\u90e8\u5206: 1)\u5c5e\u6027\u751f\u6210\u548c2)\u8fb9\u751f\u6210\u3002\u901a\u8fc7\u5bf9\u4e24\u4e2a\u7ec4\u4ef6\u8fdb\u884c\u5efa\u6a21\uff0cGPT-GNN \u6355\u83b7\u4e86\u751f\u6210\u8fc7\u7a0b\u4e2d\u8282\u70b9\u5c5e\u6027\u548c\u56fe\u7ed3\u6784\u4e4b\u95f4\u7684\u5185\u5728\u4f9d\u8d56\u6027\u3002\u5bf9\u6570\u5341\u4ebf\u89c4\u6a21\u7684 Open Academic Graph \u548c Amazon \u63a8\u8350\u6570\u636e\u8fdb\u884c\u7684\u7efc\u5408\u5b9e\u9a8c\u8868\u660e\uff0cGPT-GNN \u5728\u5404\u79cd\u4e0b\u6e38\u4efb\u52a1\u4e2d\uff0c\u672a\u7ecf\u9884\u5148\u57f9\u8bad\u7684\u6027\u80fd\u660e\u663e\u4f18\u4e8e\u6700\u5148\u8fdb\u7684 GNN \u6a21\u578b\uff0c\u8fbe\u52309.1%\u3002<\/span><\/p>\n

    \n
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    <\/p>\n


    <\/p>\n

    \n
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    <\/p>\n

    \u7f51\u7edc\u4e0a SIR \u4f20\u67d3\u75c5\u7684<\/strong><\/span><\/p>\n

    \u95ed\u73af\u63a8\u65ad\u3001\u9884\u6d4b\u4e0e\u63a7\u5236\u6846\u67b6<\/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

    A Closed-Loop Framework for Inference, Prediction and Control of SIR Epidemics on Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16185<\/span><\/p>\n

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

    Ashish R. Hota,Jaydeep Godbole,Pradhuman Bhariya,Philip E Par\u00e9<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, such as high risk individuals are more likely to undergo testing and infected individuals potentially act as asymptomatic carriers of the disease. We then present two tractable optimization problems to evaluate the trade-off between controlling the growth-rate of the epidemic and the cost of non-pharmaceutical interventions (NPIs). Our results provide compelling insights for policy-makers, including the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u57fa\u4e8e\u6b63\u5728\u8fdb\u884c\u7684\u5927\u6d41\u884c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u95ed\u73af\u6846\u67b6\uff0c\u5b83\u7ed3\u5408\u4e86\u6d4b\u8bd5\u6570\u636e\u7684\u63a8\u65ad\uff0c\u5b66\u4e60\u52a8\u6001\u53c2\u6570\u548c\u6700\u4f18\u8d44\u6e90\u5206\u914d\u6765\u63a7\u5236\u7f51\u7edc\u4e0a\u6613\u611f-\u611f\u67d3-\u5eb7\u590d(SIR)\u6d41\u884c\u75c5\u7684\u4f20\u64ad\u3002\u6211\u4eec\u7684\u6846\u67b6\u7ed3\u5408\u4e86\u6d4b\u8bd5\u6570\u636e\u4e2d\u7684\u51e0\u4e2a\u5173\u952e\u56e0\u7d20\uff0c\u4f8b\u5982\u9ad8\u5371\u4eba\u7fa4\u66f4\u6709\u53ef\u80fd\u63a5\u53d7\u6d4b\u8bd5\uff0c\u611f\u67d3\u8005\u53ef\u80fd\u662f\u65e0\u75c7\u72b6\u7684\u75be\u75c5\u643a\u5e26\u8005\u3002\u7136\u540e\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e24\u4e2a\u6613\u4e8e\u5904\u7406\u7684\u4f18\u5316\u95ee\u9898\u6765\u8bc4\u4f30\u63a7\u5236\u75ab\u60c5\u7684\u589e\u957f\u7387\u548c\u975e\u836f\u7269\u5e72\u9884\u7684\u6210\u672c\u4e4b\u95f4\u7684\u6743\u8861\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u4e3a\u51b3\u7b56\u8005\u63d0\u4f9b\u4e86\u4ee4\u4eba\u4fe1\u670d\u7684\u89c1\u89e3\uff0c\u5305\u62ec\u65e9\u671f\u8bd5\u9a8c\u7684\u91cd\u8981\u6027\uff0c\u4ee5\u53ca\u5982\u679c\u975e\u8425\u5229\u6027\u533b\u7597\u673a\u6784\u8fc7\u65e9\u64a4\u9500\uff0c\u7b2c\u4e8c\u6ce2\u611f\u67d3\u7684\u51fa\u73b0\u3002<\/span><\/p>\n


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

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    <\/p>\n

    \n
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    \u8d22\u5bcc\u5206\u5e03\u7684\u975e\u666e\u904d\u6027<\/strong><\/span><\/p>\n

    \u53cd\u6620\u5728\u8d22\u5bcc\u51dd\u805a\u4e34\u754c\u6027\u9644\u8fd1<\/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

    The non-universality of wealth distribution tails near wealth condensation criticality<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15008<\/span><\/p>\n

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

    Sam L. Polk,Bruce M. Boghosian<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this work, we modify the Affine Wealth Model of wealth distributions to examine the effects of nonconstant redistribution on the very wealthy. Previous studies of this model, restricted to flat redistribution schemes, have demonstrated the presence of a phase transition to a partially wealth-condensed state, or “partial oligarchy”, at the critical value of an order parameter. These studies have also indicated the presence of an exponential tail in wealth distribution precisely at criticality. Away from criticality, the tail was observed to be Gaussian. In this work, we generalize the flat redistribution within the Affine Wealth Model to allow for an essentially arbitrary redistribution policy. We show that the exponential tail observed near criticality in prior work is in fact a special case of a much broader class of critical, slower-than-Gaussian decays that depend sensitively on the corresponding asymptotic behavior of the progressive redistribution model used. We thereby demonstrate that the functional form of the tail of the wealth distribution of a near-critical society is not universal in nature, but rather is entirely determined by the specifics of public policy decisions. This is significant because most major economies today are observed to be near-critical.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u4fee\u6539\u4e86\u8d22\u5bcc\u5206\u914d\u7684\u4eff\u5c04\u8d22\u5bcc\u6a21\u578b\uff0c\u4ee5\u68c0\u9a8c\u975e\u5e38\u5bcc\u6709\u7684\u975e\u5e38\u91cd\u65b0\u5206\u914d\u7684\u5f71\u54cd\u3002\u4ee5\u5f80\u5bf9\u8fd9\u4e00\u6a21\u578b\u7684\u7814\u7a76\u4ec5\u9650\u4e8e\u6241\u5e73\u7684\u518d\u5206\u914d\u65b9\u6848\uff0c\u5df2\u7ecf\u8bc1\u660e\u5728\u4e00\u4e2a\u5e8f\u53c2\u6570\u7684\u4e34\u754c\u503c\u5904\u5b58\u5728\u5411\u90e8\u5206\u8d22\u5bcc\u6d53\u7f29\u72b6\u6001\u6216\u201c\u90e8\u5206\u5be1\u5934\u653f\u6cbb\u201d\u7684\u9636\u6bb5\u8f6c\u53d8\u3002\u8fd9\u4e9b\u7814\u7a76\u8fd8\u8868\u660e\uff0c\u5b58\u5728\u4e00\u4e2a\u6307\u6570\u5c3e\u5728\u8d22\u5bcc\u5206\u5e03\u6070\u597d\u5728\u4e34\u754c\u70b9\u3002\u8fdc\u79bb\u4e34\u754c\uff0c\u5c3e\u5df4\u88ab\u89c2\u5bdf\u5230\u4e3a\u9ad8\u65af\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u63a8\u5e7f\u4e86\u4eff\u5c04\u8d22\u5bcc\u6a21\u578b\u4e2d\u7684\u5e73\u9762\u518d\u5206\u914d\uff0c\u4ee5\u5141\u8bb8\u4e00\u4e2a\u672c\u8d28\u4e0a\u4efb\u610f\u7684\u518d\u5206\u914d\u653f\u7b56\u3002\u6211\u4eec\u8bc1\u660e\u4e86\u5728\u5148\u9a8c\u5de5\u4f5c\u4e2d\u89c2\u6d4b\u5230\u7684\u4e34\u754c\u9644\u8fd1\u7684\u6307\u6570\u5c3e\u5b9e\u9645\u4e0a\u662f\u4e00\u4e2a\u66f4\u5e7f\u6cdb\u7684\u4e34\u754c\uff0c\u6bd4\u9ad8\u65af\u8870\u53d8\u7684\u7279\u6b8a\u60c5\u51b5\uff0c\u654f\u611f\u5730\u4f9d\u8d56\u4e8e\u76f8\u5e94\u7684\u6e10\u8fd1\u884c\u4e3a\u7684\u6e10\u8fdb\u91cd\u65b0\u5206\u914d\u6a21\u578b\u4f7f\u7528\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8bc1\u660e\uff0c\u4e00\u4e2a\u8fd1\u4e4e\u4e34\u754c\u7684\u793e\u4f1a\u7684\u8d22\u5bcc\u5206\u914d\u5c3e\u90e8\u7684\u529f\u80fd\u5f62\u5f0f\u5728\u6027\u8d28\u4e0a\u5e76\u4e0d\u666e\u904d\uff0c\u800c\u662f\u5b8c\u5168\u53d6\u51b3\u4e8e\u516c\u5171\u653f\u7b56\u51b3\u5b9a\u7684\u5177\u4f53\u60c5\u51b5\u3002\u8fd9\u4e00\u70b9\u610f\u4e49\u91cd\u5927\uff0c\u56e0\u4e3a\u636e\u89c2\u5bdf\uff0c\u76ee\u524d\u5927\u591a\u6570\u4e3b\u8981\u7ecf\u6d4e\u4f53\u90fd\u63a5\u8fd1\u5371\u6025\u72b6\u6001\u3002<\/span><\/p>\n


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    \u57fa\u4e8e K-Means-LSTM \u7684<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u786e\u8bca\u75c5\u4f8b\u6570\u9884\u6d4b<\/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

    Prediction of the Number of COVID-19 Confirmed Cases Based on K-Means-LSTM<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.14752<\/span><\/p>\n

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

    Shashank Reddy Vadyala,Sai Nethra Betgeri,Eric A. Sherer,Amod Amritphale<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>COVID-19 is a pandemic disease that began to rapidly spread in the US with the first case detected on January 19, 2020, in Washington State. March 9, 2020, and then increased rapidly with total cases of 25,739 as of April 20, 2020. The Covid-19 pandemic is so unnerving that it is difficult to understand how any person is affected by the virus. Although most people with coronavirus 81%, according to the U.S. Centers for Disease Control and Prevention (CDC), will have little to mild symptoms, others may rely on a ventilator to breathe or not at all. SEIR models have broad applicability in predicting the outcome of the population with a variety of diseases. However, many researchers use these models without validating the necessary hypotheses. Far too many researchers often “overfit” the data by using too many predictor variables and small sample sizes to create models. Models thus developed are unlikely to stand validity check on a separate group of population and regions. The researcher remains unaware that overfitting has occurred, without attempting such validation. In the paper, we present a combination algorithm that combines similar days features selection based on the region using Xgboost, K Means, and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., K-Means-LSTM) for short-term COVID-19 cases forecasting in Louisana state USA. The weighted k-means algorithm based on extreme gradient boosting is used to evaluate the similarity between the forecasts and past days. The results show that the method with K-Means-LSTM has a higher accuracy with an RMSE of 601.20 whereas the SEIR model with an RMSE of 3615.83.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u662f\u4e00\u79cd\u5927\u6d41\u884c\u6027\u75be\u75c5\uff0c2020\u5e741\u670819\u65e5\u5728\u534e\u76db\u987f\u5dde\u53d1\u73b0\u7b2c\u4e00\u4f8b\u75c5\u4f8b\u540e\u5f00\u59cb\u5728\u7f8e\u56fd\u8fc5\u901f\u4f20\u64ad\u30022020\u5e743\u67089\u65e5\uff0c\u7136\u540e\u8fc5\u901f\u589e\u52a0\uff0c\u622a\u81f32020\u5e744\u670820\u65e5\uff0c\u5171\u670925,739\u4e2a\u75c5\u4f8b\u3002\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u662f\u5982\u6b64\u4ee4\u4eba\u4e0d\u5b89\uff0c\u4ee5\u81f3\u4e8e\u5f88\u96be\u7406\u89e3\u4efb\u4f55\u4eba\u662f\u5982\u4f55\u53d7\u5230\u8fd9\u79cd\u75c5\u6bd2\u7684\u5f71\u54cd\u7684\u3002\u6839\u636e\u7f8e\u56fd\u75be\u75c5\u63a7\u5236\u548c\u9884\u9632\u4e2d\u5fc3(CDC)\u7684\u6570\u636e\uff0c\u867d\u7136\u5927\u591a\u6570\u51a0\u72b6\u75c5\u6bd2\u611f\u67d3\u8005\u4e2d\u670981% \u51e0\u4e4e\u6ca1\u6709\u51fa\u73b0\u8f7b\u5fae\u75c7\u72b6\uff0c\u4f46\u5176\u4ed6\u4eba\u53ef\u80fd\u4f9d\u9760\u547c\u5438\u673a\u547c\u5438\u6216\u6839\u672c\u4e0d\u547c\u5438\u3002Seir \u6a21\u578b\u5728\u9884\u6d4b\u591a\u79cd\u75be\u75c5\u4eba\u7fa4\u7684\u9884\u540e\u65b9\u9762\u5177\u6709\u5e7f\u6cdb\u7684\u9002\u7528\u6027\u3002\u7136\u800c\uff0c\u8bb8\u591a\u7814\u7a76\u4eba\u5458\u4f7f\u7528\u8fd9\u4e9b\u6a21\u578b\u800c\u6ca1\u6709\u9a8c\u8bc1\u5fc5\u8981\u7684\u5047\u8bbe\u3002\u592a\u591a\u7684\u7814\u7a76\u4eba\u5458\u7ecf\u5e38\u4f7f\u7528\u8fc7\u591a\u7684\u9884\u6d4b\u53d8\u91cf\u548c\u5c0f\u6837\u672c\u91cf\u6765\u5efa\u7acb\u6a21\u578b\uff0c\u4ece\u800c\u201c\u8fc7\u5ea6\u62df\u5408\u201d\u6570\u636e\u3002\u8fd9\u6837\u5f00\u53d1\u51fa\u6765\u7684\u6a21\u578b\u4e0d\u5927\u53ef\u80fd\u5bf9\u5355\u72ec\u7684\u4e00\u7ec4\u4eba\u53e3\u548c\u533a\u57df\u8fdb\u884c\u6709\u6548\u6027\u68c0\u9a8c\u3002\u7814\u7a76\u4eba\u5458\u4ecd\u7136\u4e0d\u77e5\u9053\u8fc7\u62df\u5408\u5df2\u7ecf\u53d1\u751f\uff0c\u6ca1\u6709\u5c1d\u8bd5\u8fd9\u6837\u7684\u9a8c\u8bc1\u3002\u672c\u6587\u5229\u7528 Xgboost\u3001 k Means \u548c\u957f\u77ed\u671f\u8bb0\u5fc6(LSTM)\u795e\u7ecf\u7f51\u7edc\uff0c\u7ed3\u5408\u57fa\u4e8e\u533a\u57df\u7684\u76f8\u4f3c\u65e5\u7279\u5f81\u9009\u62e9\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u7ec4\u5408\u7b97\u6cd5\uff0c\u5efa\u7acb\u4e86 Louisana \u77ed\u671f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u9884\u6d4b\u6a21\u578b(\u5373 k-Means-LSTM)\u3002\u57fa\u4e8e\u6781\u503c\u68af\u5ea6\u63d0\u5347\u7684\u52a0\u6743 k \u5747\u503c\u7b97\u6cd5\u7528\u4e8e\u8bc4\u4f30\u9884\u62a5\u4e0e\u8fc7\u53bb\u51e0\u5929\u7684\u76f8\u4f3c\u6027\u3002\u7ed3\u679c\u8868\u660e\uff0cK-Means-LSTM \u65b9\u6cd5\u5177\u6709\u8f83\u9ad8\u7684\u7cbe\u5ea6\uff0cRMSE \u4e3a601.20\uff0c\u800c S<\/span><\/p>\n

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

    http:\/\/arxiv.org\/abs\/2006.14676<\/span><\/p>\n

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

    Clara L. Shaw,David A. Kennedy<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The reproductive number R_0 (and its value after initial disease emergence R) has long been used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some of these misconceptions, namely, how R changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7e41\u6b96\u6570 R_0 (\u53ca\u5176\u5728\u75be\u75c5\u521d\u53d1\u540e\u7684\u6570\u503c r)\u957f\u671f\u4ee5\u6765\u88ab\u7528\u6765\u9884\u6d4b\u75c5\u539f\u4f53\u5165\u4fb5\u7684\u53ef\u80fd\u6027\uff0c\u8861\u91cf\u75ab\u60c5\u7684\u6f5c\u5728\u4e25\u91cd\u7a0b\u5ea6\uff0c\u5e76\u56f4\u7ed5\u5e72\u9884\u5236\u5b9a\u653f\u7b56\u3002\u7136\u800c\uff0c\u7ecf\u5e38\u88ab\u5ffd\u89c6\u7684\u590d\u6742\u6027\u5df2\u7ecf\u5728\u5ea6\u91cf\u6807\u51c6\u7684\u4f7f\u7528\u65b9\u9762\u4ea7\u751f\u4e86\u6df7\u4e71\u3002\u8fd9\u5728\u65b0\u51fa\u73b0\u7684\u5927\u6d41\u884c\u75c5\u6bd2 SARS-CoV-2\u4e0a\u8868\u73b0\u5f97\u5c24\u4e3a\u660e\u663e\uff0cSARS-CoV-2\u662f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u75c5\u539f\u4f53\u3002\u901a\u8fc7\u5f15\u7528 R \u7684\u6570\u5b66\u5b9a\u4e49\u548c\u5f53\u524d\u6d41\u884c\u75c5\u7684\u4f8b\u5b50\uff0c\u6211\u4eec\u89e3\u51b3\u4e86\u5176\u4e2d\u4e00\u4e9b\u8bef\u89e3\uff0c\u5373 R \u968f\u65f6\u95f4\u7684\u53d8\u5316\uff0c\u968f\u7a7a\u95f4\u7684\u53d8\u5316\uff0c\u4ee5\u53ca\u4e0e\u6d41\u884c\u75c5\u89c4\u6a21\u7684\u5173\u7cfb\u3002\u6211\u4eec\u5e0c\u671b\uff0c\u66f4\u597d\u5730\u4e86\u89e3 R \u7684\u7528\u9014\u3001\u7ec6\u5fae\u5dee\u522b\u548c\u5c40\u9650\u6027\uff0c\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3 sars \u6d41\u884c\u75c5\u8513\u5ef6\u3001\u6d41\u884c\u75c5\u4e25\u91cd\u7a0b\u5ea6\uff0c\u4ee5\u53ca\u5728 SARS-CoV-2\u80cc\u666f\u4e0b\u5e72\u9884\u63aa\u65bd\u7684\u6548\u679c\u3002<\/span><\/p>\n


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    \u57ce\u5e02\u7f51\u7edc\u5316\u57fa\u7840\u8bbe\u65bd\u7684\u590d\u539f\u529b: <\/strong><\/span><\/p>\n

    \u4ee5\u4f9b\u6c34\u7cfb\u7edf\u4e3a\u4f8b<\/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

    Resilience in urban networked infrastructure: the case of Water Distribution Systems<\/span><\/h2>\n

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

    http:\/\/arxiv.<\/span>org\/abs\/2006.14622<\/span><\/p>\n

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

    Antonio Candelieri,Ilaria Giordani,Andrea Ponti,Francesco Archetti<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on net-wide measures of connectivity and the role of a single component. This paper has two objectives: first to show how a set of global measures can be obtained using techniques from network theory, in particular how the spectral analysis of the adjacency and Laplacian matrices and a similarity measure based on Jensen-Shannon divergence allows us to obtain a characteriza-tion of global connectivity which is both mathematically sound and operational. Second, how a clustering method in the subspace spanned by the l smallest eigen-vectors of the Laplacian matrix allows us to identify the edges of the network whose failure breaks down the network. Even if most of the analysis can be applied to a generic networked infrastructure, specific references will be made to Water Distribution Networks (WDN).<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u97e7\u6027\u662f\u6307\u7f51\u7edc\u57fa\u7840\u8bbe\u65bd\u5728\u67d0\u4e9b\u7ec4\u4ef6\u5931\u6548\u7684\u60c5\u51b5\u4e0b\u63d0\u4f9b\u670d\u52a1\u7684\u80fd\u529b: \u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u91cd\u70b9\u8ba8\u8bba\u97e7\u6027\u5982\u4f55\u65e2\u53d6\u51b3\u4e8e\u5168\u7f51\u8fde\u63a5\u5ea6\u91cf\uff0c\u53c8\u53d6\u51b3\u4e8e\u5355\u4e2a\u7ec4\u4ef6\u7684\u4f5c\u7528\u3002\u672c\u6587\u7684\u4e3b\u8981\u76ee\u7684\u6709\u4e24\u4e2a: \u7b2c\u4e00\uff0c\u5229\u7528\u7f51\u7edc\u7406\u8bba\u4e2d\u7684\u6280\u672f\u5f97\u5230\u4e00\u7ec4\u5168\u5c40\u6d4b\u5ea6\uff0c\u7279\u522b\u662f\u90bb\u63a5\u77e9\u9635\u548c\u62c9\u666e\u62c9\u65af\u77e9\u9635\u7684\u8c31\u5206\u6790\u4ee5\u53ca\u57fa\u4e8e Jensen-Shannon \u6563\u5ea6\u7684\u76f8\u4f3c\u6027\u6d4b\u5ea6\uff0c\u4f7f\u6211\u4eec\u5f97\u5230\u4e86\u5168\u5c40\u8fde\u901a\u6027\u7684\u4e00\u4e2a\u6570\u5b66\u4e0a\u53ef\u9760\u548c\u53ef\u64cd\u4f5c\u7684\u7279\u5f81\u3002\u5176\u6b21\uff0c\u901a\u8fc7\u5b50\u7a7a\u95f4\u4e2d\u6700\u5c0f\u7279\u5f81\u5411\u91cf\u7684\u805a\u7c7b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc6\u522b\u51fa\u7f51\u7edc\u7684\u8fb9\u7f18\uff0c\u800c\u8fd9\u4e9b\u8fb9\u7f18\u7684\u6545\u969c\u4f1a\u5bfc\u81f4\u7f51\u7edc\u7684\u5d29\u6e83\u3002\u8fd9\u4e9b\u7279\u5f81\u5411\u91cf\u662f Laplacian Matrix \u7684\u6700\u5c0f\u7279\u5f81\u5411\u91cf\u3002\u5373\u4f7f\u5927\u90e8\u5206\u5206\u6790\u53ef\u4ee5\u5e94\u7528\u4e8e\u4e00\u822c\u7684\u7f51\u7edc\u57fa\u7840\u8bbe\u65bd\uff0c\u4e5f\u5c06\u7279\u522b\u5f15\u7528\u6c34\u5206\u914d\u7f51\u7edc(WDN)\u3002<\/span><\/p>\n


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    \u5e7c\u513f\u793e\u4ea4\u5a92\u4f53\u9700\u6c42\u4e0e\u9700\u6c42\u7684\u63a2\u7d22\u6027\u7814\u7a76<\/strong><\/span><\/p>\n

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

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

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

    Exploratory Study of Young Children’s Social Media Needs and Requirements<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.14654<\/span><\/p>\n

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

    Di “Chelsea” Sun,Vaishnavi Melkote,Ahmed Sabbir Arif<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>As social media are becoming increasingly popular among young children, it is important to explore this population’s needs and requirements from these platforms. As a first step to this, we conducted an exploratory design workshop with children aged between ten and eleven years to find out about their social media needs and requirements. Through an analysis of the paper prototypes solicited from the workshop, here we discuss the social media features that are the most desired by this population.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u968f\u7740\u793e\u4ea4\u5a92\u4f53\u5728\u5e7c\u513f\u4e2d\u8d8a\u6765\u8d8a\u53d7\u6b22\u8fce\uff0c\u4ece\u8fd9\u4e9b\u5e73\u53f0\u4e2d\u63a2\u7d22\u8fd9\u4e00\u7fa4\u4f53\u7684\u9700\u6c42\u548c\u8981\u6c42\u975e\u5e38\u91cd\u8981\u3002\u4f5c\u4e3a\u7b2c\u4e00\u6b65\uff0c\u6211\u4eec\u4e3a10\u523011\u5c81\u7684\u513f\u7ae5\u7ec4\u7ec7\u4e86\u4e00\u6b21\u63a2\u7d22\u6027\u8bbe\u8ba1\u7814\u8ba8\u4f1a\uff0c\u4ee5\u4e86\u89e3\u4ed6\u4eec\u5bf9\u793e\u4ea4\u5a92\u4f53\u7684\u9700\u6c42\u548c\u8981\u6c42\u3002\u901a\u8fc7\u5bf9\u7814\u8ba8\u4f1a\u4e0a\u6536\u96c6\u5230\u7684\u7eb8\u4e0a\u539f\u578b\u7684\u5206\u6790\uff0c\u6211\u4eec\u5728\u8fd9\u91cc\u8ba8\u8bba\u4e86\u8fd9\u4e9b\u4eba\u6700\u60f3\u8981\u7684\u793e\u4ea4\u5a92\u4f53\u7279\u6027\u3002<\/span><\/p>\n


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    \u4e0d\u540c\u65f6\u95f4\u91cd\u5efa\u9897\u7c92\u7ec6\u80de\u5bf9\u5c0f\u8111<\/strong><\/span><\/p>\n

    \u5df4\u752b\u6d1b\u592b\u7728\u773c\u6761\u4ef6\u53cd\u5c04\u7684\u5f71\u54cd<\/strong><\/span><\/p>\n

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

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

    Effect of Diverse Temporal Recoding of Granule Cells on Pavlovian Eyeblink Conditioning in The Cerebellum<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.14933<\/span><\/p>\n

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

    Sang-Yoon Kim,Woochang Lim<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We consider the Pavlovian eyeblink conditioning (EBC) in the cerebellum via repeated presentation of paired conditioned stimulus (tone) and unconditioned stimulus (airpuff), and investigate the effect of diverse temporal recoding of granule (GR) cells on the EBC by varying the connection probability<\/span><\/nobr>pc from Golgi to GR cells. For an optimal value of <\/span><\/nobr>p\u2217c (=0.029), individual GR cells exhibit diverse spiking patterns which are well- or ill-matched with the unconditioned stimulus. Then, these diversely-recoded signals via parallel-fibers (PFs) from the GR cells are effectively depressed by the error-teaching signals via climbing fibers (CFs) from the inferior olive. Synaptic weights at well-matched PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while practically no LTD occurs at ill-matched PF-PC synapses. This kind of “effective” depression at PF-PC synapses coordinates firings of PCs effectively, which then exert effective inhibitory coordination on cerebellar nucleus neuron [which evokes conditioned response (CR; eyeblink)]. When the learning trial passes a threshold, acquisition of CR begins. In this case, the timing degree <\/span><\/nobr>Td of CR becomes good due to presence of ill-matched spiking group which plays a role of protection barrier for the timing. With further increase in the trial, strength <\/span><\/nobr>S of CR increases due to strong LTD in the well-matched spiking group. Thus, the learning efficiency degree <\/span><\/nobr>Le (taking into consideration both timing and strength of CR) for the CR increases with learning trial, and eventually it becomes saturated. By changing <\/span><\/nobr>pc from <\/span><\/nobr>p\u2217c, we also investigate the effect of diverse spiking patterns of GR cells on the EBC. It is this found that, the more diverse in temporal recoding of GR cells, the more effective in motor learning for the Pavlovian EBC.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8003\u8651\u4e86\u6761\u4ef6\u523a\u6fc0\u548c\u975e\u6761\u4ef6\u523a\u6fc0\u6210\u5bf9\u5448\u73b0\u65f6\u5c0f\u8111\u7eff\u6591\u4e2d\u7684\u5df4\u752b\u6d1b\u592b\u7728\u773c\u6761\u4ef6\u53cd\u5c04\uff0c\u5e76\u901a\u8fc7\u6539\u53d8\u8fde\u63a5\u6982\u7387\u7814\u7a76\u4e86\u9897\u7c92\u7ec6\u80de\u4e0d\u540c\u65f6\u95f4\u91cd\u5efa\u5bf9\u7eff\u6591\u7eff\u6591\u7684\u5f71\u54cd<\/span><\/nobr>pc \u4ece\u9ad8\u5c14\u57fa\u7ec6\u80de\u5230 GR \u7ec6\u80de<\/span><\/nobr>p\u2217c (=0.029),\u7ed3\u679c\u8868\u660e\uff0cGRcells \u5448\u73b0\u51fa\u4e0e\u975e\u6761\u4ef6\u523a\u6fc0\u76f8\u5339\u914d\u6216\u4e0d\u5339\u914d\u7684\u591a\u79cd\u523a\u6fc0\u6a21\u5f0f\u3002\u7136\u540e\uff0c\u8fd9\u4e9b\u591a\u6837\u91cd\u65b0\u7f16\u7801\u7684\u4fe1\u53f7\u901a\u8fc7\u5e76\u884c\u7ea4\u7ef4(PFs)\u4ece GR \u7ec6\u80de\u6709\u6548\u5730\u964d\u4f4e\u4e86\u9519\u8bef\u6559\u5b66\u4fe1\u53f7\u901a\u8fc7\u6500\u722c\u7ea4\u7ef4(CFs)\u4ece\u4e0b\u6a44\u6984\u3002\u6d3b\u8dc3\u7684 GR \u7ec6\u80de\u5728\u5339\u914d\u826f\u597d\u7684 PF-Purkinje \u7ec6\u80de(PC)\u7a81\u89e6\u4e0a\u7684\u7a81\u89e6\u91cd\u91cf\u901a\u8fc7\u5f3a\u70c8\u7684\u957f\u671f\u6291\u5236(LTD)\u800c\u5f3a\u70c8\u5730\u6291\u5236\uff0c\u800c\u5728\u4e0d\u5339\u914d\u7684 pf-PC \u7a81\u89e6\u4e0a\u51e0\u4e4e\u6ca1\u6709 LTD\u3002\u8fd9\u79cd PF-PC \u7a81\u89e6\u7684\u201c\u6709\u6548\u201d\u6291\u5236\u6709\u6548\u5730\u534f\u8c03\u4e86 pc \u7684\u89e6\u53d1\uff0c\u7136\u540e\u5728\u5c0f\u8111\u6838\u795e\u7ecf\u5143\u4e0a\u53d1\u6325\u6709\u6548\u7684\u6291\u5236\u6027\u534f\u8c03[\u5f15\u8d77\u6761\u4ef6\u53cd\u5e94(CR; eyeblink)]\u3002\u5f53\u5b66\u4e60\u8bd5\u9a8c\u8d85\u8fc7\u4e00\u4e2a\u9608\u503c\uff0c\u8ba4\u77e5\u53cd\u5e94\u7684\u4e60\u5f97\u5c31\u5f00\u59cb\u4e86\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u65f6\u95f4\u7684\u7a0b\u5ea6<\/span><\/nobr>Td \u7531\u4e8e\u5b58\u5728\u4e0d\u5339\u914d\u5c16\u5cf0\u7ec4\uff0cCR \u7684\u7a33\u5b9a\u6027\u53d8\u597d\uff0c\u8d77\u5230\u4e86\u5b9a\u65f6\u4fdd\u62a4\u5c4f\u969c\u7684\u4f5c\u7528\u3002\u968f\u7740\u8bd5\u9a8c\u529b\u5ea6\u7684\u8fdb\u4e00\u6b65\u52a0\u5927,<\/span><\/nobr>S \u7ed3\u679c\u8868\u660e\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u4e0e\u5bf9\u7167\u7ec4\u76f8\u6bd4\uff0c\u5b66\u4e60\u6548\u7387\u63d0\u9ad8<\/span><\/nobr>Le (\u8003\u8651\u5230 CR \u7684\u65f6\u95f4\u548c\u5f3a\u5ea6) \uff0c\u53d7\u8bd5\u8005\u7684 CR \u968f\u7740\u5b66\u4e60\u8bd5\u9a8c\u7684\u589e\u52a0\u800c\u589e\u52a0\uff0c\u6700\u7ec8\u8fbe\u5230\u9971\u548c\u3002\u901a\u8fc7\u6539\u53d8<\/span><\/nobr>pc \u4ece<\/span><\/nobr>p\u2217c,\u76ee\u7684: \u63a2\u8ba8 GR \u7ec6\u80de\u4e0d\u540c\u7684\u523a\u6fc0\u65b9\u5f0f\u5bf9 EBC \u7684\u5f71\u54cd\u3002\u7ed3\u679c\u53d1\u73b0\uff0cGR \u7ec6\u80de\u5728\u65f6\u95f4\u8bb0\u5fc6\u4e0a\u7684\u5dee\u5f02\u8d8a\u5927\uff0c\u5df4\u752b\u6d1b\u592b EBC \u7684\u8fd0\u52a8\u5b66\u4e60\u6548\u679c\u8d8a\u597d\u3002<\/span><\/p>\n


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    \u8d85\u7a33\u5b9a\u65cb\u8f6c\u78c1\u5bf9\u6d41\u4e2d\u7684\u8dc3\u8fc1<\/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

    Transitions in overstable rotating magnetoconvection<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.14646<\/span><\/p>\n

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

    Ankan Banerjee,Manojit Ghosh,Pinaki Pal<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The classical Rayleigh-B\u00e9nard convection (RBC) system is known to exhibit either subcritical or supercritical transition to convection in the presence or absence of rotation and\/or magnetic field. However, the simultaneous exhibition of subcritical and supercritical branches of convection in plane layer RBC depending on the initial conditions, has not been reported so far. Here, we report the phenomenon of simultaneous occurrence of subcritical and supercritical branches of convection in overstable RBC of electrically conducting low Prandtl number fluids (liquid metals) in the presence of an external uniform horizontal magnetic field and rotation about the vertical axis. Extensive three dimensional (3D) direct numerical simulations (DNS) and low dimensional modeling of the system, performed in the ranges<\/span><\/nobr>750\u2264Ta\u22643000 and <\/span><\/nobr>0<Q\u22641000 of the Taylor number (<\/span><\/nobr>Ta, strength of the Coriolis force) and the Chandrasekhar number (<\/span><\/nobr>Q, strength of the Lorenz force) respectively, establish the phenomenon convincingly. Detailed bifurcation analysis of a simple three dimensional model derived from the DNS data reveals that a supercritical Hopf bifurcation and a subcritical pitchfork bifurcation of the conduction state are responsible for this. The effect of Prandtl number on these transitions is also explored in detail.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7ecf\u5178\u7684 Rayleigh-B\u00e9nard \u5bf9\u6d41(RBC)\u7cfb\u7edf\u5728\u6709\u65e0\u65cb\u8f6c\u548c \/ \u6216\u78c1\u573a\u7684\u60c5\u51b5\u4e0b\uff0c\u8868\u73b0\u4e3a\u4e9a\u4e34\u754c\u6216\u8d85\u4e34\u754c\u7684\u5bf9\u6d41\u8fc7\u6e21\u3002\u7136\u800c\uff0c\u5728\u5e73\u9762\u5c42\u7ea2\u7ec6\u80de\u4e2d\uff0c\u7531\u4e8e\u521d\u59cb\u6761\u4ef6\u7684\u4e0d\u540c\uff0c\u4e9a\u4e34\u754c\u548c\u8d85\u4e34\u754c\u7ea2\u7ec6\u80de\u5bf9\u6d41\u7684\u8f66\u8f6e\u6218\u8fd8\u6ca1\u6709\u62a5\u9053\u3002\u672c\u6587\u62a5\u9053\u4e86\u5bfc\u7535\u4f4e\u666e\u6717\u7279\u6570\u6d41\u4f53(\u6db2\u6001\u91d1\u5c5e)\u5728\u5916\u52a0\u5747\u5300\u6c34\u5e73\u78c1\u573a\u548c\u7ed5\u5782\u76f4\u8f74\u65cb\u8f6c\u7684\u60c5\u51b5\u4e0b\uff0c\u8d85\u7a33\u5b9a\u7ea2\u7ec6\u80de\u4e2d\u4e9a\u4e34\u754c\u548c\u8d85\u4e34\u754c\u5bf9\u6d41\u652f\u540c\u65f6\u51fa\u73b0\u7684\u73b0\u8c61\u3002\u5e7f\u6cdb\u7684\u4e09\u7ef4(3D)\u76f4\u63a5\u6570\u503c\u6a21\u62df(DNS)\u548c\u4f4e\u7ef4\u5efa\u6a21\u7684\u7cfb\u7edf\uff0c\u5728\u8303\u56f4\u5185\u6267\u884c<\/span><\/nobr>750\u2264Ta\u22643000 \u53ca<\/span><\/nobr>0<Q\u22641000\uff08\u6cf0\u52d2\u6570<\/span><\/nobr>Ta,\uff08\u79d1\u91cc\u5965\u5229\u529b\u7684\u5f3a\u5ea6)\u548c\u94b1\u5fb7\u62c9\u585e\u5361\u6570<\/span><\/nobr>Q, \u5206\u522b\u7528\u6d1b\u4f26\u5179\u529b\u7684\u5f3a\u5ea6) \uff0c\u4ee4\u4eba\u4fe1\u670d\u5730\u5efa\u7acb\u4e86\u8fd9\u4e00\u73b0\u8c61\u3002\u6839\u636e DNS \u6570\u636e\uff0c\u5bf9\u4e00\u4e2a\u7b80\u5355\u7684\u4e09\u7ef4\u6a21\u578b\u8fdb\u884c\u4e86\u8be6\u7ec6\u7684\u5206\u53c9\u5206\u6790\uff0c\u7ed3\u679c\u663e\u793a\u8d85\u4e34\u754c\u970d\u666e\u592b\u5206\u5c94\u548c\u4e9a\u4e34\u754c\u53c9\u5f0f\u5206\u5c94\u5bfc\u81f4\u4e86\u8fd9\u79cd\u73b0\u8c61\u3002\u6587\u4e2d\u8fd8\u8be6\u7ec6\u8ba8\u8bba\u4e86\u666e\u6717\u7279\u6570\u5bf9\u8fd9\u4e9b\u8dc3\u8fc1\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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    \u7ea0\u7f20\u5d4c\u5165\u9012\u5f52\u7f51\u7edc\u4f53\u7cfb\u7ed3\u6784: <\/strong><\/span><\/p>\n

    \u5f20\u91cf\u6f5c\u5728\u72b6\u6001\u4f20\u64ad\u4e0e\u6df7\u6c8c\u9884\u6d4b<\/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

    Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.14698<\/span><\/p>\n

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

    Xiangyi Meng,Tong Yang<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Chaotic time series forecasting has been far less understood despite its tremendous potential in theory and real-world applications. Traditional statistical\/ML methods are inefficient to capture chaos in nonlinear dynamical systems, especially when the time difference<\/span><\/nobr>\u0394t between consecutive steps is so large that a trivial, ergodic local minimum would most likely be reached instead. Here, we introduce a new long-short-term-memory (LSTM)-based recurrent architecture by tensorizing the cell-state-to-state propagation therein, keeping the long-term memory feature of LSTM while simultaneously enhancing the learning of short-term nonlinear complexity. We stress that the global minima of chaos can be most efficiently reached by tensorization where all nonlinear terms, up to some polynomial order, are treated explicitly and weighted equally. The efficiency and generality of our architecture are systematically tested and confirmed by theoretical analysis and experimental results. In our design, we have explicitly used two different many-body entanglement structures—matrix product states (MPS) and the multiscale entanglement renormalization ansatz (MERA)—as physics-inspired tensor decomposition techniques, from which we find that MERA generally performs better than MPS, hence conjecturing that the learnability of chaos is determined not only by the number of free parameters but also the tensor complexity—recognized as how entanglement entropy scales with varying matricization of the tensor.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5c3d\u7ba1\u6df7\u6c8c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u5728\u7406\u8bba\u548c\u73b0\u5b9e\u5e94\u7528\u65b9\u9762\u6709\u7740\u5de8\u5927\u7684\u6f5c\u529b\uff0c\u4f46\u4eba\u4eec\u5bf9\u5b83\u7684\u4e86\u89e3\u8fd8\u8fdc\u8fdc\u4e0d\u591f\u3002\u4f20\u7edf\u7684\u7edf\u8ba1 \/ ml \u65b9\u6cd5\u5bf9\u975e\u7ebf\u6027\u52a8\u6001\u7cfb\u7edf\u7684\u6df7\u6c8c\u6355\u83b7\u6548\u7387\u5f88\u4f4e\uff0c\u5c24\u5176\u662f\u5728\u65f6\u95f4\u5dee\u65f6<\/span><\/nobr>\u0394t \u8fde\u7eed\u6b65\u957f\u4e4b\u95f4\u7684\u503c\u662f\u5982\u6b64\u4e4b\u5927\uff0c\u4ee5\u81f3\u4e8e\u6700\u6709\u53ef\u80fd\u8fbe\u5230\u96f6\u6563\u7684\u904d\u5386\u5c40\u90e8\u6781\u5c0f\u503c\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u57fa\u4e8e\u957f-\u77ed\u671f\u8bb0\u5fc6(LSTM)\u7684\u9012\u5f52\u7ed3\u6784\uff0c\u901a\u8fc7\u5f20\u91cf\u7ec6\u80de\u72b6\u6001\u5230\u72b6\u6001\u7684\u4f20\u64ad\uff0c\u4fdd\u6301\u4e86 LSTM \u7684\u957f\u671f\u8bb0\u5fc6\u7279\u6027\uff0c\u540c\u65f6\u589e\u5f3a\u4e86\u77ed\u671f\u975e\u7ebf\u6027\u590d\u6742\u6027\u7684\u5b66\u4e60\u3002\u6211\u4eec\u5f3a\u8c03\u6df7\u6c8c\u7684\u5168\u5c40\u6700\u5c0f\u53ef\u4ee5\u901a\u8fc7\u5f20\u91cf\u5316\u6700\u6709\u6548\u5730\u8fbe\u5230\uff0c\u5176\u4e2d\u6240\u6709\u7684\u975e\u7ebf\u6027\u9879\uff0c\u8fbe\u5230\u4e00\u5b9a\u7684\u591a\u9879\u5f0f\u6b21\u6570\uff0c\u662f\u663e\u5f0f\u5904\u7406\u548c\u52a0\u6743\u76f8\u7b49\u3002\u7406\u8bba\u5206\u6790\u548c\u5b9e\u9a8c\u7ed3\u679c\u7cfb\u7edf\u5730\u9a8c\u8bc1\u4e86\u8be5\u4f53\u7cfb\u7ed3\u6784\u7684\u6709\u6548\u6027\u548c\u901a\u7528\u6027\u3002\u5728\u6211\u4eec\u7684\u8bbe\u8ba1\u4e2d\uff0c\u6211\u4eec\u660e\u786e\u5730\u4f7f\u7528\u4e86\u4e24\u79cd\u4e0d\u540c\u7684\u591a\u4f53\u7ea0\u7f20\u7ed3\u6784\u2014\u2014\u77e9\u9635\u79ef\u6001(MPS)\u548c\u591a\u5c3a\u5ea6\u7ea0\u7f20\u91cd\u6574\u5316\u5047\u8bbe(MERA)\u2014\u2014\u4f5c\u4e3a\u7269\u7406\u5b66\u542f\u53d1\u7684\u5f20\u91cf\u5206\u89e3\u6280\u672f\uff0c\u4ece\u4e2d\u6211\u4eec\u53d1\u73b0 MERA \u7684\u6027\u80fd\u901a\u5e38\u4f18\u4e8e MPS\uff0c\u56e0\u6b64\u6211\u4eec\u63a8\u6d4b\u6df7\u6c8c\u5f20\u91cf\u7684\u53ef\u5b66\u6027\u4e0d\u4ec5\u53d6\u51b3\u4e8e\u81ea\u7531\u53c2\u6570\u7684\u4e2a\u6570\uff0c\u800c\u4e14\u8fd8\u53d6\u51b3\u4e8e\u590d\u6742\u5ea6\u2014\u2014\u88ab\u8ba4\u4e3a\u662f\u71b5\u4e0e\u5f20\u91cf\u7684\u4e0d\u540c\u77e9\u9635\u5c3a\u5ea6\u7684\u7ea0\u7f20\u7a0b\u5ea6\u3002<\/span><\/p>\n


    <\/p>\n

    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u80bf\u7624\u8bf1\u5bfc\u8840\u7ba1\u751f\u6210\u4e2d\u7684\u4e8c\u7ef4\u5b64\u5b50<\/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

    Two dimensional soliton in tumor induced angiogenesis<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16138<\/span><\/p>\n

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

    L. L. Bonilla,M. Carretero,F. Terragni<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Ensemble averages of a stochastic model show that, after a formation stage, the tips of active blood vessels in an angiogenic network form a moving two dimensional stable diffusive soliton, which advances toward sources of growth factor. Here we use methods of multiple scales to find the diffusive soliton as a solution of a deterministic equation for the mean density of active endothelial cells tips. We characterize the diffusive soliton shape in a general geometry, and find that its vector velocity and the trajectory of its center of mass along curvilinear coordinates solve appropriate collective coordinate equations. The vessel tip density predicted by the soliton compares well with that obtained by ensemble averages of simulations of the stochastic model.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u968f\u673a\u6a21\u5f0f\u7684\u7cfb\u7efc\u5e73\u5747\u8868\u660e\uff0c\u5728\u5f62\u6210\u9636\u6bb5\u4e4b\u540e\uff0c\u8840\u7ba1\u751f\u6210\u7f51\u7edc\u4e2d\u7684\u6d3b\u52a8\u8840\u7ba1\u5c16\u7aef\u5f62\u6210\u4e00\u4e2a\u79fb\u52a8\u7684\u4e8c\u7ef4\u7a33\u5b9a\u6269\u6563\u5b64\u5b50\uff0c\u5411\u751f\u957f\u56e0\u5b50\u6e90\u63a8\u8fdb\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528\u591a\u5c3a\u5ea6\u7684\u65b9\u6cd5\u6765\u5bfb\u627e\u6269\u6563\u5b64\u5b50\u4f5c\u4e3a\u4e00\u4e2a\u786e\u5b9a\u6027\u65b9\u7a0b\u7684\u89e3\u51b3\u65b9\u6848\u7684\u5e73\u5747\u5bc6\u5ea6\u7684\u6d3b\u8dc3\u5185\u76ae\u7ec6\u80de\u7684\u5c16\u7aef\u3002\u6211\u4eec\u5728\u4e00\u822c\u7684\u51e0\u4f55\u5f62\u72b6\u4e2d\u523b\u753b\u4e86\u6269\u6563\u5b64\u5b50\u7684\u5f62\u72b6\uff0c\u53d1\u73b0\u5b83\u7684\u77e2\u91cf\u901f\u5ea6\u548c\u5b83\u7684\u8d28\u5fc3\u6cbf\u66f2\u7ebf\u5750\u6807\u7cfb\u7684\u8f68\u8ff9\u53ef\u4ee5\u89e3\u51b3\u9002\u5f53\u7684\u96c6\u4f53\u5750\u6807\u65b9\u7a0b\u3002\u901a\u8fc7\u5bf9\u968f\u673a\u6a21\u578b\u7684\u6570\u503c\u6a21\u62df\uff0c\u5f97\u5230\u4e86\u5b64\u5b50\u8109\u51b2\u548c\u7cfb\u7efc\u5e73\u5747\u8109\u51b2\u7684\u8109\u51b2\u5c16\u7aef\u5bc6\u5ea6\u3002<\/span><\/p>\n


    <\/p>\n

    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u7528\u5fae\u578b\u62c9\u4f38\u6d41\u53d8\u4eea\u7814\u7a76\u8f6f\u3001\u6d3b\u6027\u7269\u8d28<\/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

    Investigation of Soft and Living Matter using a Micro-Extensional Rheometer<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15958<\/span><\/p>\n

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

    Sushil Dubey,Sukh Veer,Seshagiri Rao R V,Chirag Kalelkar,Pramod A Pullarkat<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Rheological properties of a material often require to be probed under extensional deformation. Examples include fibrous materials such as spider-silk, high-molecular weight polymer melts, and the contractile response of living cells. Such materials have strong molecular-level anisotropies which are either inherent or are induced by an imposed extension. However, unlike shear rheology, which is well-established, techniques to perform extensional rheology are currently under development and setups are often custom-designed for the problem under study. In this article, we present a versatile device that can be used to conduct extensional deformation studies of samples at microscopic scales with simultaneous imaging. We discuss the operational features of this device and present a number of applications.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u62c9\u4f38\u53d8\u5f62\u4e0b\uff0c\u6750\u6599\u7684\u6d41\u53d8\u7279\u6027\u5f80\u5f80\u9700\u8981\u63a2\u6d4b\u3002\u4f8b\u5b50\u5305\u62ec\u7ea4\u7ef4\u6750\u6599\uff0c\u5982\u8718\u86db\u4e1d\uff0c\u9ad8\u5206\u5b50\u91cf\u805a\u5408\u7269\u7194\u4f53\uff0c\u548c\u6d3b\u7ec6\u80de\u7684\u6536\u7f29\u53cd\u5e94\u3002\u8fd9\u7c7b\u6750\u6599\u5177\u6709\u5f3a\u70c8\u7684\u5206\u5b50\u6c34\u5e73\u5404\u5411\u5f02\u6027\uff0c\u8fd9\u4e9b\u5404\u5411\u5f02\u6027\u8981\u4e48\u662f\u56fa\u6709\u7684\uff0c\u8981\u4e48\u662f\u7531\u5916\u52a0\u7684\u5ef6\u4f38\u5f15\u8d77\u7684\u3002\u7136\u800c\uff0c\u4e0e\u5df2\u7ecf\u6210\u719f\u7684\u526a\u5207\u6d41\u53d8\u5b66\u4e0d\u540c\u7684\u662f\uff0c\u4f38\u5c55\u6d41\u53d8\u5b66\u7684\u5b9e\u65bd\u6280\u672f\u76ee\u524d\u6b63\u5728\u53d1\u5c55\u4e4b\u4e2d\uff0c\u800c\u4e14\u5f80\u5f80\u662f\u4e3a\u6240\u7814\u7a76\u7684\u95ee\u9898\u5b9a\u5236\u7684\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u591a\u529f\u80fd\u7684\u8bbe\u5907\uff0c\u53ef\u7528\u4e8e\u8fdb\u884c\u62c9\u4f38\u53d8\u5f62\u7814\u7a76\u7684\u6837\u54c1\u5728\u5fae\u89c2\u5c3a\u5ea6\u4e0e\u540c\u6b65\u6210\u50cf\u3002\u6211\u4eec\u8ba8\u8bba\u4e86\u8fd9\u79cd\u88c5\u7f6e\u7684\u5de5\u4f5c\u7279\u70b9\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u4e9b\u5e94\u7528\u3002<\/span><\/p>\n


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    <\/p>\n

    \n
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    <\/p>\n

    \u63a2\u7d22\u591a\u5c3a\u5ea6\u86cb\u767d\u8d28\u529b\u5b66<\/strong><\/span><\/p>\n

    \u548c\u7ec4\u88c5\u7684\u5149\u954a\u65b9\u6cd5<\/strong><\/span><\/p>\n

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

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

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

    Optical tweezers approaches for probing multiscale protein mechanics and assembly<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15841<\/span><\/p>\n

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

    Kathrin Lehmann,Marjan Shayegan,Gerhard A. Blab,Nancy R. Forde<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Multi-step assembly of individual protein building blocks is key to the formation of essential higher-order structures inside and outside of cells. Optical tweezers is a technique well suited to investigate the mechanics and dynamics of these structures at a variety of size scales. In this mini-review, we highlight experiments that have used optical tweezers to investigate protein assembly and mechanics, with a focus on the extracellular matrix protein collagen. These examples demonstrate how optical tweezers can be used to study mechanics across length scales, ranging from the single-molecule level to fibrils to protein networks. We discuss challenges in experimental design and interpretation, opportunities for integration with other experimental modalities, and applications of optical tweezers to current questions in protein mechanics and assembly.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5355\u4e2a\u86cb\u767d\u8d28\u6784\u5efa\u5757\u7684\u591a\u6b65\u7ec4\u88c5\u662f\u7ec6\u80de\u5185\u5916\u57fa\u672c\u9ad8\u9636\u7ed3\u6784\u5f62\u6210\u7684\u5173\u952e\u3002\u8fd9\u9879\u6280\u672f\u975e\u5e38\u9002\u5408\u4e8e\u5728\u4e0d\u540c\u5c3a\u5ea6\u4e0b\u7814\u7a76\u8fd9\u4e9b\u7ed3\u6784\u7684\u529b\u5b66\u548c\u52a8\u529b\u5b66\u5149\u954a\u3002\u5728\u8fd9\u4e2a\u5c0f\u5c0f\u7684\u56de\u987e\u4e2d\uff0c\u6211\u4eec\u91cd\u70b9\u4ecb\u7ecd\u4e86\u4e00\u4e9b\u5b9e\u9a8c\uff0c\u8fd9\u4e9b\u5b9e\u9a8c\u5229\u7528\u5149\u954a\u6765\u7814\u7a76\u86cb\u767d\u8d28\u7684\u7ec4\u88c5\u548c\u673a\u5236\uff0c\u91cd\u70b9\u662f\u7ec6\u80de\u5916\u95f4\u8d28\u86cb\u767d\u8d28\u80f6\u539f\u86cb\u767d\u3002\u8fd9\u4e9b\u4f8b\u5b50\u5c55\u793a\u4e86\u5149\u954a\u662f\u5982\u4f55\u88ab\u7528\u6765\u7814\u7a76\u4ece\u5355\u5206\u5b50\u6c34\u5e73\u5230\u7ea4\u7ef4\u5230\u86cb\u767d\u8d28\u7f51\u7edc\u7b49\u5404\u79cd\u957f\u5ea6\u5c3a\u5ea6\u7684\u529b\u5b66\u95ee\u9898\u7684\u3002\u6211\u4eec\u8ba8\u8bba\u4e86\u5b9e\u9a8c\u8bbe\u8ba1\u548c\u89e3\u91ca\u4e2d\u7684\u6311\u6218\uff0c\u4e0e\u5176\u4ed6\u5b9e\u9a8c\u6a21\u5f0f\u6574\u5408\u7684\u673a\u4f1a\uff0c\u4ee5\u53ca\u5149\u954a\u5728\u86cb\u767d\u8d28\u529b\u5b66\u548c\u7ec4\u88c5\u4e2d\u7684\u5e94\u7528\u3002<\/span><\/p>\n


    <\/p>\n

    \n
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    <\/p>\n

    \n
    \n
    <\/p>\n

    \u53d1\u5149\u4e8c\u6781\u7ba1\u6fc0\u53d1\u4e0a\u8f6c\u6362\u663e\u5fae\u955c:<\/strong><\/span><\/p>\n

    \u4e00\u4e2a\u5b9a\u91cf\u7684\u8bc4\u4f30<\/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

    Light-emitting diode excitation for upconversion microscopy: a quantitative assessment<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.15783<\/span><\/p>\n

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

    Yueying Cao,Xianlin Zheng,Simone De Camillis,Bingyang Shi,James A. Piper,Nicolle H. Packer,Yiqing Lu<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Lanthanide-based upconversion nanoparticles (UCNPs) generally require high power laser excitation. Here we report wide-field upconversion microscopy at single-nanoparticle sensitivity using incoherent excitation of a 970-nm light-emitting diode (LED). We show that due to its broad emission spectrum, LED excitation is about 3 times less effective for UCNPs and generates high background compared to laser illumination. To counter this, we use time-gated luminescence detection to eliminate the residual background from the LED source, so that individual UCNPs with high sensitizer (Yb3+) doping and inert shell protection become clearly identified under LED excitation at 1.18 W cm-2, as confirmed by correlated electron microscopy images. Hydrophilic UCNPs are obtained by polysaccharide coating via a facile ligand exchange protocol to demonstrate imaging of cellular uptake using LED excitation. These results suggest a viable approach to bypassing the limitations associated with high-power lasers when applying UCNPs and upconversion microscopy to life science research.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u9567\u7cfb\u5143\u7d20\u4e0a\u8f6c\u6362\u7eb3\u7c73\u7c92\u5b50(UCNPs)\u4e00\u822c\u9700\u8981\u9ad8\u529f\u7387\u6fc0\u5149\u6fc0\u53d1\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u62a5\u544a\u4e86\u4f7f\u7528970\u7eb3\u7c73\u53d1\u5149\u4e8c\u6781\u7ba1(LED)\u7684\u975e\u76f8\u5e72\u6fc0\u53d1\u5728\u5355\u4e2a\u7eb3\u7c73\u7c92\u5b50\u654f\u611f\u5ea6\u4e0b\u7684\u5bbd\u573a\u4e0a\u8f6c\u6362\u663e\u5fae\u955c\u3002\u6211\u4eec\u8868\u660e\uff0c\u7531\u4e8e\u5176\u5bbd\u53d1\u5c04\u5149\u8c31\uff0cLED \u6fc0\u53d1\u5bf9 UCNPs \u7684\u6709\u6548\u6027\u7ea6\u4f4e3\u500d\uff0c\u5e76\u4ea7\u751f\u9ad8\u80cc\u666f\u76f8\u6bd4\u6fc0\u5149\u7167\u660e\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\uff0c\u6211\u4eec\u4f7f\u7528\u65f6\u95f4\u95e8\u63a7\u53d1\u5149\u68c0\u6d4b\u6765\u6d88\u9664 LED \u5149\u6e90\u4e2d\u7684\u6b8b\u4f59\u80cc\u666f\uff0c\u4ee5\u4fbf\u57281.18 w cm-2\u7684 LED \u6fc0\u53d1\u4e0b\uff0c\u5728\u9ad8\u611f\u5149\u5242(Yb3 +)\u63ba\u6742\u548c\u60f0\u6027\u5916\u58f3\u4fdd\u62a4\u4f5c\u7528\u4e0b\uff0c\u4e2a\u4f53 UCNPs \u53ef\u4ee5\u6e05\u6670\u5730\u88ab\u8bc6\u522b\u51fa\u6765\uff0c\u8fd9\u4e00\u70b9\u5df2\u7ecf\u5f97\u5230\u4e86\u7535\u5b50\u663e\u5fae\u955c\u56fe\u50cf\u7684\u8bc1\u5b9e\u3002\u4eb2\u6c34\u6027 UCNPs \u662f\u7531\u591a\u7cd6\u6d82\u5c42\u901a\u8fc7\u4e00\u4e2a\u7b80\u4fbf\u7684\u914d\u4f53\u4ea4\u6362\u534f\u8bae\uff0c\u8bc1\u660e\u6210\u50cf\u7ec6\u80de\u6444\u53d6\u4f7f\u7528 LED \u6fc0\u52b1\u3002\u8fd9\u4e9b\u7ed3\u679c\u8868\u660e\uff0c\u5728\u5c06 UCNPs \u548c\u4e0a\u8f6c\u6362\u663e\u5fae\u955c\u5e94\u7528\u4e8e\u751f\u547d\u79d1\u5b66\u7814\u7a76\u65f6\uff0c\u4e00\u79cd\u53ef\u884c\u7684\u65b9\u6cd5\u53ef\u4ee5\u7ed5\u8fc7\u4e0e\u9ad8\u529f\u7387\u6fc0\u5149\u5668\u76f8\u5173\u7684\u9650\u5236\u3002<\/span><\/p>\n


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

    \n
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    \u5206\u5e03\u5f0f\u540c\u610f\u53ca\u5176\u5bf9<\/strong><\/span><\/p>\n

    \u793e\u4f1a\u7f51\u7edc\u9690\u79c1\u548c\u53ef\u89c2\u5bdf\u6027\u7684\u5f71\u54cd<\/strong><\/span><\/p>\n

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

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

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

    Distributed consent and its impact on privacy and observability in social networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16140<\/span><\/p>\n

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

    Juniper Lovato,Antoine Allard,Randall Harp,Laurent H\u00e9bert-Dufresne<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Personal data is not discrete in socially-networked digital environments. A single user who consents to allow access to their own profile can thereby expose the personal data of their network connections to non-consented access. The traditional (informed individual) consent model is therefore not appropriate in online social networks where informed consent may not be possible for all users affected by data processing and where information is shared and distributed across many nodes. Here, we introduce a model of “distributed consent” where individuals and groups can coordinate by giving consent conditional on that of their network connections. We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy. Distributed consent is of course not a silver bullet, since it does not follow data as it flows in and out of the system, but it is one of the most straightforward non-traditional models to implement and it better accommodates the fuzzy, distributed nature of online data.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u793e\u4f1a\u7f51\u7edc\u7684\u6570\u5b57\u73af\u5883\u4e2d\uff0c\u4e2a\u4eba\u6570\u636e\u662f\u4e0d\u79bb\u6563\u7684\u3002\u540c\u610f\u5141\u8bb8\u8bbf\u95ee\u81ea\u5df1\u914d\u7f6e\u6587\u4ef6\u7684\u5355\u4e2a\u7528\u6237\u53ef\u4ee5\u5c06\u5176\u7f51\u7edc\u8fde\u63a5\u7684\u4e2a\u4eba\u6570\u636e\u66b4\u9732\u7ed9\u672a\u7ecf\u540c\u610f\u7684\u8bbf\u95ee\u3002\u56e0\u6b64\uff0c\u4f20\u7edf\u7684(\u77e5\u60c5\u7684\u4e2a\u4eba)\u540c\u610f\u6a21\u5f0f\u4e0d\u9002\u7528\u4e8e\u5728\u7ebf\u793e\u4f1a\u7f51\u7edc\uff0c\u56e0\u4e3a\u53d7\u6570\u636e\u5904\u7406\u5f71\u54cd\u7684\u6240\u6709\u7528\u6237\u53ef\u80fd\u65e0\u6cd5\u83b7\u5f97\u77e5\u60c5\u540c\u610f\uff0c\u800c\u4e14\u4fe1\u606f\u662f\u5728\u8bb8\u591a\u8282\u70b9\u4e4b\u95f4\u5171\u4eab\u548c\u5206\u53d1\u7684\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4e00\u4e2a\u201c\u5206\u5e03\u5f0f\u540c\u610f\u201d\u7684\u6a21\u578b\uff0c\u5176\u4e2d\u4e2a\u4eba\u548c\u56e2\u4f53\u53ef\u4ee5\u534f\u8c03\uff0c\u7ed9\u4e88\u540c\u610f\u7684\u6761\u4ef6\uff0c\u4ed6\u4eec\u7684\u7f51\u7edc\u8fde\u63a5\u3002\u6211\u4eec\u5efa\u7acb\u4e86\u5206\u5e03\u5f0f\u540c\u610f\u5bf9\u793e\u4f1a\u7f51\u7edc\u53ef\u89c2\u6d4b\u6027\u7684\u5f71\u54cd\u6a21\u578b\uff0c\u53d1\u73b0\u5373\u4f7f\u662f\u6700\u7b80\u5355\u7684\u5206\u5e03\u5f0f\u540c\u610f\u6a21\u578b\u7684\u91c7\u7528\u7387\u76f8\u5bf9\u8f83\u4f4e\uff0c\u4e5f\u4f1a\u5141\u8bb8\u5728\u7ebf\u7f51\u7edc\u7684\u5b8f\u89c2\u5b50\u96c6\u4fdd\u6301\u5176\u8fde\u901a\u6027\u548c\u9690\u79c1\u6027\u3002\u5206\u5e03\u5f0f\u540c\u610f\u5f53\u7136\u4e0d\u662f\u7075\u4e39\u5999\u836f\uff0c\u56e0\u4e3a\u5b83\u4e0d\u9075\u5faa\u6570\u636e\u8fdb\u51fa\u7cfb\u7edf\uff0c\u4f46\u5b83\u662f\u6700\u76f4\u63a5\u7684\u975e\u4f20\u7edf\u6a21\u578b\u4e4b\u4e00\uff0c\u800c\u4e14\u5b83\u66f4\u597d\u5730\u9002\u5e94\u4e86\u5728\u7ebf\u6570\u636e\u7684\u6a21\u7cca\u6027\u548c\u5206\u5e03\u6027\u3002<\/span><\/p>\n


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    <\/p>\n

    \n
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    \u5f02\u6784\u7f51\u7edc\u7cfb\u7edf\u7684\u8054\u5408\u7f51\u7edc\u98ce\u9669\u8bc4\u4f30<\/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

    Joint Cyber Risk Assessment of Network Systems with Heterogeneous Components<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.16092<\/span><\/p>\n

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

    Gaofeng Da,Maochao Xu,Jingshi Zhang,Peng Zhao<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Cyber risks are the most common risks encountered by a modern network system. However, it is significantly difficult to assess the joint cyber risk owing to the network topology, risk propagation, and heterogeneities of components. In this paper, we propose a novel backward elimination approach for computing the joint cyber risk encountered by different types of components in a network system; moreover, explicit formulas are also presented. Certain specific network topologies including complete, star, and complete bi-partite topologies are studied. The effects of propagation depth and compromise probabilities on the joint cyber risk are analyzed using stochastic comparisons. The variances and correlations of cyber risks are examined by a simulation experiment. It was discovered that both variances and correlations change rapidly when the propagation depth increases from its initial value. Further, numerical examples are also presented.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u98ce\u9669\u662f\u73b0\u4ee3\u7f51\u7edc\u7cfb\u7edf\u9762\u4e34\u7684\u6700\u5e38\u89c1\u7684\u98ce\u9669\u3002\u7136\u800c\uff0c\u7531\u4e8e\u7f51\u7edc\u62d3\u6251\u3001\u98ce\u9669\u4f20\u64ad\u4ee5\u53ca\u7ec4\u4ef6\u7684\u4e0d\u5747\u5300\u6027\uff0c\u8054\u5408\u7f51\u7edc\u98ce\u9669\u7684\u8bc4\u4f30\u975e\u5e38\u56f0\u96be\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u8ba1\u7b97\u7f51\u7edc\u7cfb\u7edf\u4e2d\u4e0d\u540c\u7c7b\u578b\u7ec4\u4ef6\u9047\u5230\u7684\u8054\u5408\u7f51\u7edc\u98ce\u9669\u7684\u53cd\u5411\u6d88\u9664\u65b9\u6cd5\uff0c\u5e76\u7ed9\u51fa\u4e86\u663e\u5f0f\u516c\u5f0f\u3002\u7814\u7a76\u4e86\u67d0\u4e9b\u7279\u5b9a\u7684\u7f51\u7edc\u62d3\u6251\uff0c\u5305\u62ec\u5b8c\u5168\u62d3\u6251\u3001\u661f\u578b\u62d3\u6251\u548c\u5b8c\u5168\u4e8c\u5206\u62d3\u6251\u3002\u901a\u8fc7\u968f\u673a\u6bd4\u8f83\uff0c\u5206\u6790\u4e86\u4f20\u64ad\u6df1\u5ea6\u548c\u6298\u8877\u6982\u7387\u5bf9\u8054\u5408\u7f51\u7edc\u98ce\u9669\u7684\u5f71\u54cd\u3002\u901a\u8fc7\u6a21\u62df\u5b9e\u9a8c\u7814\u7a76\u4e86\u7f51\u7edc\u98ce\u9669\u7684\u65b9\u5dee\u548c\u76f8\u5173\u6027\u3002\u7814\u7a76\u53d1\u73b0\uff0c\u5f53\u4f20\u64ad\u6df1\u5ea6\u4ece\u521d\u59cb\u503c\u589e\u52a0\u65f6\uff0c\u65b9\u5dee\u548c\u76f8\u5173\u6027\u90fd\u53d1\u751f\u4e86\u8fc5\u901f\u7684\u53d8\u5316\u3002\u6b64\u5916\uff0c\u8fd8\u7ed9\u51fa\u4e86\u6570\u503c\u7b97\u4f8b\u3002<\/span><\/p>\n


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    \u516c\u5171\u4ea4\u901a\u7684\u65e0\u6e90 Wi-Fi \u76d1\u6d4b: <\/strong><\/span><\/p>\n

    \u4ee5 Madeira Island \u4e3a\u4f8b<\/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

    Passive Wi-Fi Monitoring in Public Transport: A case study in the Madeira Island<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16083<\/span><\/p>\n

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

    Miguel Ribeiro,Bernardo Galv\u00e3o,Catia Prandi,Nuno Nunes<\/span><\/p>\n


    <\/p>\n

    A<\/span><\/strong>bstract\uff1a<\/strong>Transportation has become of evermore importance in the last years, affecting people’s satisfaction and significantly impacting their quality of life. In this paper we present a low-cost infrastructure to collect passive Wi-Fi probes with the aim of monitoring, optimizing and personalizing public transport, towards a more sustainable mobility. We developed an embedded system deployed in 19 public transportation vehicles using passive Wi-Fi data. This data is analyzed on a per-vehicle and per-stop basis and compared against ground truth data (ticketing), while also using a method of estimating passenger exits, detecting peak loads on vehicles, and origin destination habits. As such, we argue that this data enables route optimization and provides local authorities and tourism boards with a tool to monitor and optimize the management of routes and transportation, identify and prevent accessibility issues, with the aim of improving the services offered to citizens and tourists, towards a more sustainable mobility.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fc7\u53bb\u7684\u5e74\u91cc\uff0c\u4ea4\u901a\u53d8\u5f97\u8d8a\u6765\u8d8a\u91cd\u8981\uff0c\u5b83\u5f71\u54cd\u7740\u4eba\u4eec\u7684\u6ee1\u610f\u5ea6\uff0c\u5e76\u663e\u8457\u5730\u5f71\u54cd\u7740\u4ed6\u4eec\u7684\u751f\u6d3b\u8d28\u91cf\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u4f4e\u6210\u672c\u7684\u57fa\u7840\u8bbe\u65bd\uff0c\u4ee5\u6536\u96c6\u88ab\u52a8 Wi-Fi \u63a2\u5934\uff0c\u76ee\u7684\u662f\u76d1\u6d4b\uff0c\u4f18\u5316\u548c\u4e2a\u6027\u5316\u7684\u516c\u5171\u4ea4\u901a\uff0c\u4ee5\u5b9e\u73b0\u66f4\u53ef\u6301\u7eed\u7684\u4ea4\u901a\u3002\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u4e2a\u5d4c\u5165\u5f0f\u7cfb\u7edf\uff0c\u90e8\u7f72\u572819\u4e2a\u516c\u5171\u4ea4\u901a\u5de5\u5177\u4f7f\u7528\u88ab\u52a8\u65e0\u7ebf\u7f51\u7edc\u6570\u636e\u3002\u8fd9\u4e9b\u6570\u636e\u5206\u6790\u4e86\u6bcf\u8f86\u8f66\u548c\u6bcf\u4e2a\u505c\u9760\u7ad9\u7684\u57fa\u7840\u4e0a\uff0c\u5e76\u4e0e\u5730\u9762\u771f\u76f8\u6570\u636e(\u7968\u52a1)\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u540c\u65f6\u8fd8\u4f7f\u7528\u4e86\u4f30\u8ba1\u4e58\u5ba2\u51fa\u53e3\u3001\u68c0\u6d4b\u8f66\u8f86\u5cf0\u503c\u8f7d\u8377\u548c\u51fa\u53d1\u76ee\u7684\u5730\u4e60\u60ef\u7684\u65b9\u6cd5\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8ba4\u4e3a\uff0c\u8fd9\u4e9b\u6570\u636e\u6709\u52a9\u4e8e\u4f18\u5316\u8def\u7ebf\uff0c\u5e76\u4e3a\u5730\u65b9\u5f53\u5c40\u548c\u65c5\u6e38\u5c40\u63d0\u4f9b\u4e00\u4e2a\u5de5\u5177\uff0c\u4ee5\u76d1\u6d4b\u548c\u4f18\u5316\u8def\u7ebf\u548c\u4ea4\u901a\u7ba1\u7406\uff0c\u67e5\u660e\u548c\u9632\u6b62\u65e0\u969c\u788d\u95ee\u9898\uff0c\u76ee\u7684\u662f\u6539\u5584\u5411\u516c\u6c11\u548c\u6e38\u5ba2\u63d0\u4f9b\u7684\u670d\u52a1\uff0c\u5b9e\u73b0\u66f4\u53ef\u6301\u7eed\u7684\u6d41\u52a8\u6027\u3002<\/span><\/p>\n


    <\/p>\n

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    <\/p>\n

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    <\/p>\n

    Canarytrap: <\/strong><\/span><\/p>\n

    \u68c0\u6d4b\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u4e2d<\/strong><\/span><\/p>\n

    \u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u7684\u6570\u636e\u6ee5\u7528<\/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

    CanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15794<\/span><\/p>\n

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

    Shehroze Farooqi,Maaz Musa,Zubair Shafiq,Fareed Zaffar<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Online social networks support a vibrant ecosystem of third-party apps that get access to personal information of a large number of users. Despite several recent high-profile incidents, methods to systematically detect data misuse by third-party apps on online social networks are lacking. We propose CanaryTrap to detect misuse of data shared with third-party apps. CanaryTrap associates a honeytoken to a user account and then monitors its unrecognized use via different channels after sharing it with the third-party app. We design and implement CanaryTrap to investigate misuse of data shared with third-party apps on Facebook. Specifically, we share the email address associated with a Facebook account as a honeytoken by installing a third-party app. We then monitor the received emails and use Facebook’s ad transparency tool to detect any unrecognized use of the shared honeytoken. Our deployment of CanaryTrap to monitor 1,024 Facebook apps has uncovered multiple cases of misuse of data shared with third-party apps on Facebook including ransomware, spam, and targeted advertising.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u652f\u6301\u4e00\u4e2a\u5145\u6ee1\u6d3b\u529b\u7684\u7b2c\u4e09\u65b9\u5e94\u7528\u751f\u6001\u7cfb\u7edf\uff0c\u8fd9\u4e9b\u5e94\u7528\u53ef\u4ee5\u8bbf\u95ee\u5927\u91cf\u7528\u6237\u7684\u4e2a\u4eba\u4fe1\u606f\u3002\u5c3d\u7ba1\u6700\u8fd1\u53d1\u751f\u4e86\u51e0\u8d77\u5907\u53d7\u77a9\u76ee\u7684\u4e8b\u4ef6\uff0c\u4f46\u662f\u7f3a\u4e4f\u7cfb\u7edf\u5730\u68c0\u6d4b\u7b2c\u4e09\u65b9\u5e94\u7528\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u4e0a\u6ee5\u7528\u6570\u636e\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u5efa\u8bae\u4f7f\u7528 CanaryTrap \u6765\u68c0\u6d4b\u4e0e\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u5171\u4eab\u7684\u6570\u636e\u7684\u8bef\u7528\u3002Canarytrap \u5c06\u4e00\u4e2a honeytoken \u5173\u8054\u5230\u4e00\u4e2a\u7528\u6237\u5e10\u6237\uff0c\u7136\u540e\u5728\u4e0e\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u5171\u4eab\u4e4b\u540e\uff0c\u901a\u8fc7\u4e0d\u540c\u7684\u6e20\u9053\u76d1\u89c6\u5176\u672a\u88ab\u8bc6\u522b\u7684\u7528\u9014\u3002\u6211\u4eec\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e86 CanaryTrap \u6765\u8c03\u67e5 Facebook \u4e0a\u4e0e\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u5171\u4eab\u7684\u6570\u636e\u7684\u6ee5\u7528\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u901a\u8fc7\u5b89\u88c5\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\uff0c\u5c06\u4e0e Facebook \u5e10\u6237\u5173\u8054\u7684\u7535\u5b50\u90ae\u4ef6\u5730\u5740\u4f5c\u4e3a\u4e00\u4e2a\u871c\u7f50\u5171\u4eab\u3002\u7136\u540e\u6211\u4eec\u76d1\u63a7\u6536\u5230\u7684\u90ae\u4ef6\uff0c\u5e76\u4f7f\u7528 Facebook \u7684\u5e7f\u544a\u900f\u660e\u5316\u5de5\u5177\u6765\u68c0\u6d4b\u4efb\u4f55\u672a\u88ab\u8bc6\u522b\u7684\u4f7f\u7528\u5171\u4eab\u871c\u7f50\u7684\u60c5\u51b5\u3002\u6211\u4eec\u90e8\u7f72\u4e86 CanaryTrap \u6765\u76d1\u63a71024\u4e2a Facebook \u5e94\u7528\u7a0b\u5e8f\uff0c\u53d1\u73b0\u4e86\u591a\u8d77\u6ee5\u7528\u7b2c\u4e09\u65b9\u5e94\u7528\u7a0b\u5e8f\u5728 Facebook \u4e0a\u5171\u4eab\u7684\u6570\u636e\u7684\u6848\u4f8b\uff0c\u5305\u62ec\u52d2\u7d22\u8f6f\u4ef6\u3001\u5783\u573e\u90ae\u4ef6\u548c\u5b9a\u5411\u5e7f\u544a\u3002<\/span><\/p>\n


    <\/p>\n

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    \n
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    \u5c40\u90e8\u7f51\u7edf\u8ba1\u91cf\u7684\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406<\/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

    Central limit theorems for local network statistics<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15738<\/span><\/p>\n

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

    P-A. Maugis<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Subgraph counts – in particular the number of occurrences of small shapes such as triangles – characterize properties of random networks, and as a result have seen wide use as network summary statistics. However, subgraphs are typically counted globally, and existing approaches fail to describe vertex-specific characteristics. On the other hand, rooted subgraph counts – counts focusing on any given vertex’s neighborhood – are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model which generalizes many popular statistical network models. This result enables a shift in the statistical analysis of large graphs, from estimating network summaries, to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that local friendship patterns are significant predictors of gender and race.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5b50\u56fe\u8ba1\u6570\u2014\u2014\u7279\u522b\u662f\u8bf8\u5982\u4e09\u89d2\u5f62\u7b49\u5c0f\u5f62\u72b6\u51fa\u73b0\u7684\u6b21\u6570\u2014\u2014\u63cf\u8ff0\u4e86\u968f\u673a\u7f51\u7edc\u7684\u7279\u6027\uff0c\u56e0\u6b64\u88ab\u5e7f\u6cdb\u7528\u4f5c\u7f51\u7edc\u6c47\u603b\u7edf\u8ba1\u6570\u636e\u3002\u7136\u800c\uff0c\u5b50\u56fe\u901a\u5e38\u662f\u5168\u5c40\u8ba1\u6570\u7684\uff0c\u73b0\u6709\u7684\u65b9\u6cd5\u65e0\u6cd5\u63cf\u8ff0\u9876\u70b9\u7279\u5b9a\u7684\u7279\u5f81\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u6709\u6839\u5b50\u56fe\u8ba1\u6570\u662f\u5c40\u90e8\u7f51\u7edc\u6027\u8d28\u7684\u57fa\u672c\u63cf\u8ff0\u7b26\uff0c\u5b83\u5173\u6ce8\u4e8e\u4efb\u610f\u7ed9\u5b9a\u9876\u70b9\u7684\u90bb\u57df\u3002\u672c\u6587\u63a8\u5bfc\u4e86\u975e\u9f50\u6b21\u968f\u673a\u56fe\u4e2d\u6709\u6839\u5b50\u56fe\u8ba1\u6570\u7684\u6e10\u8fd1\u8054\u5408\u5206\u5e03\uff0c\u8be5\u6a21\u578b\u63a8\u5e7f\u4e86\u8bb8\u591a\u6d41\u884c\u7684\u7edf\u8ba1\u7f51\u7edc\u6a21\u578b\u3002\u8fd9\u4e2a\u7ed3\u679c\u4f7f\u5927\u578b\u56fe\u7684\u7edf\u8ba1\u5206\u6790\u8f6c\u53d8\uff0c\u4ece\u4f30\u8ba1\u7f51\u7edc\u6458\u8981\uff0c\u5230\u4f30\u8ba1\u6a21\u578b\u8fde\u63a5\u672c\u5730\u7f51\u7edc\u7ed3\u6784\u548c\u9876\u70b9\u7279\u5b9a\u7684\u534f\u53d8\u91cf\u3002\u4f5c\u4e3a\u4e00\u4e2a\u4f8b\u5b50\uff0c\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u5b66\u6821\u7684\u53cb\u8c0a\u7f51\u7edc\uff0c\u5e76\u8868\u660e\u5f53\u5730\u7684\u53cb\u8c0a\u6a21\u5f0f\u662f\u6027\u522b\u548c\u79cd\u65cf\u7684\u91cd\u8981\u9884\u6d4b\u56e0\u7d20\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    21\u9b54\u517d\u4e16\u754c\u7684\u5ba2\u6237\u6d41\u5931\u9884\u6d4b<\/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

    Predicting Customer Churn in World of Warcraft<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15735<\/span><\/p>\n

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

    Sulman Khan<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this paper, we explore a dataset that focuses on one year from January 1, 2008, until December 31, 2008, as it highlights the release of a major content update in the game. Machine learning is used in two aspects of this paper: Survival Analysis and Binary Classification. Firstly, we explore the dataset using the Kaplan Meier estimator to predict the duration until a customer churns, and lastly predict whether a person will churn in six months using traditional machine learning algorithms such as Logistic Regression, Support Vector Machine, KNN Classifier, and Random Forests. From the survival analysis results, WoW customers have a relatively long duration until churn, which solidifies the addictiveness of the game. Lastly, the binary classification performed in the best performing algorithm having a 96% ROC AUC score in predicting whether a customer will churn in six months.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u63a2\u7d22\u4e86\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5b83\u805a\u7126\u4e8e\u4ece2008\u5e741\u67081\u65e5\u52302008\u5e7412\u670831\u65e5\u7684\u4e00\u5e74\uff0c\u56e0\u4e3a\u5b83\u7a81\u51fa\u4e86\u6e38\u620f\u4e2d\u4e00\u4e2a\u4e3b\u8981\u5185\u5bb9\u66f4\u65b0\u7684\u53d1\u5e03\u3002\u672c\u6587\u5c06\u673a\u5668\u5b66\u4e60\u5e94\u7528\u4e8e\u4e24\u4e2a\u65b9\u9762: \u751f\u5b58\u5206\u6790\u548c\u4e8c\u5206\u7c7b\u3002\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528 Kaplan Meier \u4f30\u8ba1\u5668\u63a2\u7d22\u6570\u636e\u96c6\u6765\u9884\u6d4b\u5ba2\u6237\u6d41\u5931\u7684\u6301\u7eed\u65f6\u95f4\uff0c\u6700\u540e\u4f7f\u7528\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6765\u9884\u6d4b\u4e00\u4e2a\u4eba\u662f\u5426\u4f1a\u5728\u516d\u4e2a\u6708\u5185\u6d41\u5931\uff0c\u6bd4\u5982 Logit\u6a21\u578b\u3001\u652f\u6301\u5411\u91cf\u673a\u3001 KNN \u5206\u7c7b\u5668\u548c\u968f\u673a\u68ee\u6797\u3002\u4ece\u751f\u5b58\u5206\u6790\u7684\u7ed3\u679c\u6765\u770b\uff0c\u9b54\u517d\u4e16\u754c\u7684\u7528\u6237\u5728\u5ba2\u6237\u6d41\u5931\u4e4b\u524d\u6709\u4e00\u6bb5\u76f8\u5bf9\u8f83\u957f\u7684\u65f6\u95f4\uff0c\u8fd9\u5de9\u56fa\u4e86\u6e38\u620f\u7684\u5438\u5f15\u529b\u3002\u6700\u540e\uff0c\u4e8c\u8fdb\u5236\u5206\u7c7b\u6267\u884c\u7684\u8868\u73b0\u6700\u597d\u7684\u7b97\u6cd5\u670996% ROC AUC \u5206\u6570\u5728\u9884\u6d4b\u5ba2\u6237\u662f\u5426\u4f1a\u5728\u516d\u4e2a\u6708\u5185\u7ffb\u7bb1\u5012\u67dc\u3002<\/span><\/p>\n


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    \u9500\u552e\u6027\u522b: <\/strong><\/span><\/p>\n

    \u662f\u4ec0\u4e48\u51b3\u5b9a\u4e86\u9500\u552e\u7387\u548c\u53d7\u6b22\u8fce\u7a0b\u5ea6? <\/strong><\/span><\/p>\n

    \u5bf911500\u4efd\u5728\u7ebf\u6863\u6848\u7684\u5206\u6790<\/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

    Selling sex: what determines rates and popularity? An analysis of 11.5 thousand online profiles<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15648<\/span><\/p>\n

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

    Alicia Mergenthaler,Taha Yasseri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Sex work, or the exchange of sexual services for money or goods, is ubiquitous across eras and cultures. However, the practice of selling sex is often hidden due to stigma and the varying legal status of sex work. Online platforms that sex workers use to advertise services have become an increasingly important tool in studying a market that is largely hidden. Although prior literature has primarily shed light on sex work from a public health or policy perspective (focusing largely on female sex workers), there are few studies that empirically research patterns of service provision in online sex work. Little research has been done on understanding pricing and popularity in the market for commercial sex work. This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the “industry-standard” for sex workers. While the size of these influences vary across genders, nationality, age, and services provided are shown to be primary drivers of rates and popularity in sex work.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6027\u5de5\u4f5c\uff0c\u6216\u7528\u6027\u670d\u52a1\u4ea4\u6362\u91d1\u94b1\u6216\u5546\u54c1\uff0c\u5728\u4e0d\u540c\u7684\u65f6\u4ee3\u548c\u6587\u5316\u4e2d\u65e0\u5904\u4e0d\u5728\u3002\u7136\u800c\uff0c\u7531\u4e8e\u6027\u5de5\u4f5c\u7684\u803b\u8fb1\u548c\u4e0d\u540c\u7684\u6cd5\u5f8b\u5730\u4f4d\uff0c\u5356\u6deb\u884c\u4e3a\u5f80\u5f80\u88ab\u9690\u85cf\u8d77\u6765\u3002\u6027\u5de5\u4f5c\u8005\u7528\u6765\u5ba3\u4f20\u670d\u52a1\u7684\u5728\u7ebf\u5e73\u53f0\u5df2\u7ecf\u6210\u4e3a\u4e00\u4e2a\u8d8a\u6765\u8d8a\u91cd\u8981\u7684\u5de5\u5177\uff0c\u7528\u6765\u7814\u7a76\u4e00\u4e2a\u5f88\u5927\u7a0b\u5ea6\u4e0a\u9690\u85cf\u8d77\u6765\u7684\u5e02\u573a\u3002\u5c3d\u7ba1\u5148\u524d\u7684\u6587\u732e\u4e3b\u8981\u4ece\u516c\u5171\u536b\u751f\u6216\u653f\u7b56\u89d2\u5ea6\u9610\u660e\u4e86\u6027\u5de5\u4f5c(\u4e3b\u8981\u4fa7\u91cd\u4e8e\u5973\u6027\u6027\u5de5\u4f5c\u8005) \uff0c\u4f46\u5f88\u5c11\u6709\u7814\u7a76\u5bf9\u7f51\u4e0a\u6027\u5de5\u4f5c\u670d\u52a1\u63d0\u4f9b\u6a21\u5f0f\u8fdb\u884c\u5b9e\u8bc1\u7814\u7a76\u3002\u5173\u4e8e\u4e86\u89e3\u5546\u4e1a\u6027\u5de5\u4f5c\u7684\u5b9a\u4ef7\u548c\u5e02\u573a\u6d41\u884c\u7a0b\u5ea6\u7684\u7814\u7a76\u5f88\u5c11\u3002\u672c\u7814\u7a76\u5229\u7528\u82f1\u56fd\u6700\u5927\u7684\u5728\u7ebf\u6027\u670d\u52a1\u7f51\u7edc\u7684\u6570\u636e\uff0c\u8c03\u67e5\u4e86\u5546\u4e1a\u6027\u670d\u52a1\u5728\u7ebf\u5e02\u573a\u5b9a\u4ef7\u548c\u53d7\u6b22\u8fce\u7a0b\u5ea6\u7684\u51b3\u5b9a\u56e0\u7d20\uff0c\u8be5\u7f51\u7edc\u662f\u6027\u5de5\u4f5c\u8005\u7684\u201d\u884c\u4e1a\u6807\u51c6\u201d\u5e73\u53f0\u3002\u867d\u7136\u8fd9\u4e9b\u5f71\u54cd\u7684\u5927\u5c0f\u56e0\u6027\u522b\u3001\u56fd\u7c4d\u3001\u5e74\u9f84\u548c\u6240\u63d0\u4f9b\u7684\u670d\u52a1\u800c\u5f02\uff0c\u4f46\u8fd9\u4e9b\u90fd\u662f\u6027\u5de5\u4f5c\u6bd4\u4f8b\u548c\u53d7\u6b22\u8fce\u7a0b\u5ea6\u7684\u4e3b\u8981\u9a71\u52a8\u56e0\u7d20\u3002<\/span><\/p>\n


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    \u8ba1\u7b97\u6c42\u7231: <\/strong><\/span><\/p>\n

    \u901a\u8fc7\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790<\/strong><\/span><\/p>\n

    \u7406\u89e3\u5728\u7ebf\u7ea6\u4f1a\u7684\u8fdb\u5316<\/strong><\/span><\/p>\n

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

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

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

    Computational Courtship: Understanding the Evolution of Online Dating through Large-scale Data Analysis<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/1809.10032<\/span><\/p>\n

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

    Rachel Dinh,Patrick Gildersleve,Chris Blex,Taha Yasseri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Have we become more tolerant of dating people of different social backgrounds compared to ten years ago? Has the rise of online dating exacerbated or alleviated gender inequalities in modern courtship? Are the most attractive people on these platforms necessarily the most successful? In this work, we examine the mate preferences and communication patterns of male and female users of the online dating site eHarmony over the past decade to identify how attitudes and behaviors have changed over this time period. While other studies have investigated disparities in user behavior between male and female users, this study is unique in its longitudinal approach. Specifically, we analyze how men and women differ in their preferences for certain traits in potential partners and how those preferences have changed over time. The second line of inquiry investigates to what extent physical attractiveness determines the rate of messages a user receives, and how this relationship varies between men and women. Thirdly, we explore whether online dating practices between males and females have become more equal over time or if biases and inequalities have remained constant (or increased). Fourthly, we study the behavioural traits in sending and replying to messages based on one’s own experience of receiving messages and being replied to. Finally, we found that similarity between profiles is not a predictor for success except for the number of children and smoking habits. This work could have broader implications for shifting gender norms and social attitudes, reflected in online courtship rituals. Apart from the data-based research, we connect the results to existing theories that concern the role of ICTs in societal change. As searching for love online becomes increasingly common across generations and geographies, these findings may shed light on how people can build relationships through the Internet.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e0e\u5341\u5e74\u524d\u76f8\u6bd4\uff0c\u6211\u4eec\u662f\u5426\u53d8\u5f97\u66f4\u80fd\u5bb9\u5fcd\u4e0e\u4e0d\u540c\u793e\u4f1a\u80cc\u666f\u7684\u4eba\u7ea6\u4f1a\uff1f\u7f51\u4e0a\u7ea6\u4f1a\u7684\u5174\u8d77\u662f\u5426\u52a0\u5267\u6216\u51cf\u8f7b\u4e86\u73b0\u4ee3\u6c42\u7231\u4e2d\u7684\u6027\u522b\u4e0d\u5e73\u7b49\uff1f\u8fd9\u4e9b\u5e73\u53f0\u4e0a\u6700\u6709\u5438\u5f15\u529b\u7684\u4eba\u4e00\u5b9a\u662f\u6700\u6210\u529f\u7684\u5417\uff1f\u5728\u8fd9\u9879\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u8c03\u67e5\u4e86\u5728\u7ebf\u4ea4\u53cb\u7f51\u7ad9 eHarmony \u7684\u7537\u6027\u548c\u5973\u6027\u7528\u6237\u5728\u8fc7\u53bb\u5341\u5e74\u4e2d\u7684\u62e9\u5076\u504f\u597d\u548c\u4ea4\u6d41\u6a21\u5f0f\uff0c\u4ee5\u786e\u5b9a\u5728\u8fd9\u6bb5\u65f6\u95f4\u5185\u6001\u5ea6\u548c\u884c\u4e3a\u662f\u5982\u4f55\u6539\u53d8\u7684\u3002\u867d\u7136\u5176\u4ed6\u7814\u7a76\u5df2\u7ecf\u8c03\u67e5\u4e86\u7537\u6027\u548c\u5973\u6027\u7528\u6237\u4e4b\u95f4\u7684\u7528\u6237\u884c\u4e3a\u5dee\u5f02\uff0c\u4f46\u8fd9\u9879\u7814\u7a76\u7684\u7eb5\u5411\u7814\u7a76\u65b9\u6cd5\u662f\u72ec\u7279\u7684\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u5206\u6790\u4e86\u7537\u6027\u548c\u5973\u6027\u5bf9\u6f5c\u5728\u4f34\u4fa3\u7684\u67d0\u4e9b\u7279\u5f81\u7684\u504f\u597d\u662f\u5982\u4f55\u4e0d\u540c\u7684\uff0c\u4ee5\u53ca\u8fd9\u4e9b\u504f\u597d\u662f\u5982\u4f55\u968f\u7740\u65f6\u95f4\u53d8\u5316\u7684\u3002\u7b2c\u4e8c\u4e2a\u95ee\u9898\u662f\u8c03\u67e5\u4f53\u5f81\u7f8e\u5728\u591a\u5927\u7a0b\u5ea6\u4e0a\u51b3\u5b9a\u4e86\u7528\u6237\u6536\u5230\u4fe1\u606f\u7684\u9891\u7387\uff0c\u4ee5\u53ca\u8fd9\u79cd\u5173\u7cfb\u5728\u7537\u6027\u548c\u5973\u6027\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u7b2c\u4e09\uff0c\u6211\u4eec\u63a2\u8ba8\u7684\u662f\uff0c\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u7537\u6027\u548c\u5973\u6027\u4e4b\u95f4\u7684\u5728\u7ebf\u7ea6\u4f1a\u5b9e\u8df5\u662f\u5426\u53d8\u5f97\u66f4\u52a0\u5e73\u7b49\uff0c\u6216\u8005\u504f\u89c1\u548c\u4e0d\u5e73\u7b49\u662f\u5426\u4fdd\u6301\u4e0d\u53d8(\u6216\u8005\u589e\u52a0)\u3002\u7b2c\u56db\uff0c\u6211\u4eec\u6839\u636e\u4e2a\u4eba\u63a5\u6536\u548c\u56de\u590d\u90ae\u4ef6\u7684\u7ecf\u9a8c\uff0c\u7814\u7a76\u53d1\u9001\u548c\u56de\u590d\u90ae\u4ef6\u7684\u884c\u4e3a\u7279\u5f81\u3002\u6700\u540e\uff0c\u6211\u4eec\u53d1\u73b0\uff0c\u9664\u4e86\u5b69\u5b50\u7684\u6570\u91cf\u548c\u5438\u70df\u4e60\u60ef\u4e4b\u5916\uff0c\u4e2a\u4eba\u8d44\u6599\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u5e76\u4e0d\u662f\u6210\u529f\u7684\u9884\u6d4b\u56e0\u7d20\u3002\u8fd9\u9879\u5de5\u4f5c\u53ef\u80fd\u5bf9\u8f6c\u53d8\u6027\u522b\u89c4\u8303\u548c\u793e\u4f1a\u6001\u5ea6\u6709\u66f4\u5e7f\u6cdb\u7684\u5f71\u54cd\uff0c\u53cd\u6620\u5728\u7f51\u4e0a\u6c42\u7231\u4eea\u5f0f\u4e0a\u3002\u9664\u4e86\u57fa\u4e8e\u6570\u636e\u7684\u7814\u7a76\u4e4b\u5916\uff0c\u6211\u4eec\u8fd8\u5c06\u7814\u7a76\u7ed3\u679c\u4e0e\u6709\u5173\u4fe1\u606f\u548c\u901a\u4fe1\u6280\u672f\u5728\u793e\u4f1a\u53d8\u9769\u4e2d\u7684\u4f5c\u7528\u7684\u73b0\u6709\u7406\u8bba\u8054\u7cfb\u8d77\u6765\u3002\u968f\u7740\u5728\u7f51\u4e0a\u5bfb\u627e\u7231\u60c5\u53d8\u5f97\u8d8a\u6765\u8d8a\u666e\u904d\uff0c\u8fd9\u4e9b\u53d1\u73b0\u53ef\u80fd\u4f1a\u4e3a\u4eba\u4eec\u5982\u4f55\u901a\u8fc7\u4e92\u8054\u7f51\u5efa\u7acb\u5173\u7cfb\u63d0\u4f9b\u7ebf\u7d22\u3002<\/span><\/p>\n


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    \u793e\u533a\u68c0\u6d4b\u548c\u4fe1\u606f\u6e17\u900f\u5728\u4e00\u4e2a\u51e0\u4f55\u8bbe\u7f6e<\/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

    Community detection and percolation of information in a geometric setting<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15574<\/span><\/p>\n

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

    Ronen Eldan,Dan Mikulincer,Hester Pieters<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry. We consider a geometric random graph over a homogeneous metric space where the probability of two vertices to be connected is an arbitrary function of the distance. We give sufficient conditions under which the locations can be recovered (up to an isomorphism of the space) in the sparse regime. Moreover, we define a geometric counterpart of the model of flow of information on trees, due to Mossel and Peres, in which one considers a branching random walk on a sphere and the goal is to recover the location of the root based on the locations of leaves. We give some sufficient conditions for percolation and for non-percolation of information in this model.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8fc8\u51fa\u4e86\u7b2c\u4e00\u6b65\uff0c\u4ee5\u63a8\u5e7f\u7684\u7406\u8bba\u968f\u673a\u5757\u6a21\u578b\uff0c\u5728\u7a00\u758f\u7684\u5236\u5ea6\uff0c\u8d70\u5411\u4e00\u4e2a\u6a21\u578b\uff0c\u5176\u4e2d\u79bb\u6563\u7684\u793e\u533a\u7ed3\u6784\u53d6\u4ee3\u4e86\u4e00\u4e2a\u57fa\u672c\u7684\u51e0\u4f55\u3002\u6211\u4eec\u8003\u8651\u9f50\u6b21\u5ea6\u91cf\u7a7a\u95f4\u4e0a\u7684\u4e00\u4e2a\u51e0\u4f55\u968f\u673a\u56fe\uff0c\u5176\u4e2d\u4e24\u4e2a\u9876\u70b9\u8fde\u901a\u7684\u6982\u7387\u662f\u8ddd\u79bb\u7684\u4efb\u610f\u51fd\u6570\u3002\u6211\u4eec\u7ed9\u51fa\u4e86\u5728\u7a00\u758f\u533a\u57df\u4e2d\u4f4d\u7f6e\u6062\u590d\u7684\u5145\u5206\u6761\u4ef6(\u76f4\u5230\u7a7a\u95f4\u7684\u540c\u6784)\u3002\u6b64\u5916\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u6811\u4e0a\u4fe1\u606f\u6d41\u6a21\u578b\u7684\u4e00\u4e2a\u51e0\u4f55\u5bf9\u5e94\u7269\uff0c\u8fd9\u662f\u7531\u4e8e Mossel \u548c\u4f69\u96f7\u65af\uff0c\u5728\u8fd9\u4e2a\u51e0\u4f55\u5bf9\u5e94\u7269\u4e2d\uff0c\u6211\u4eec\u8003\u8651\u4e86\u7403\u9762\u4e0a\u7684\u4e00\u4e2a\u5206\u652f\u968f\u673a\u6e38\u52a8\uff0c\u76ee\u6807\u662f\u57fa\u4e8e\u53f6\u5b50\u7684\u4f4d\u7f6e\u6062\u590d\u6839\u7684\u4f4d\u7f6e\u3002\u7ed9\u51fa\u4e86\u8be5\u6a21\u578b\u4e2d\u4fe1\u606f\u4e0d\u903e\u6e17\u548c\u903e\u6e17\u7684\u5145\u5206\u6761\u4ef6\u3002<\/span><\/p>\n


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    \u4efb\u610f\u57df\u4e0a\u5e26\u6f5c\u53d8\u91cf\u7684\u7cbe\u786e\u63a8\u7406<\/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

    Exact Inference with Latent Variables in an Arbitrary Domain<\/span>
    <\/p>\n

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

    http:\/\/arxiv.org\/abs\/1902.03099<\/span><\/p>\n

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

    Chuyang Ke,Jean Honorio<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We analyze the necessary and sufficient conditions for exact inference of a latent model. In latent models, each entity is associated with a latent variable following some probability distribution. The challenging question we try to solve is: can we perform exact inference without observing the latent variables, even without knowing what the domain of the latent variables is? We show that exact inference can be achieved using a semidefinite programming (SDP) approach without knowing either the latent variables or their domain. Our analysis predicts the experimental correctness of SDP with high accuracy, showing the suitability of our focus on the Karush-Kuhn-Tucker (KKT) conditions and the spectrum of a properly defined matrix. As a byproduct of our analysis, we also provide concentration inequalities with dependence on latent variables, both for bounded moment generating functions as well as for the spectra of matrices. To the best of our knowledge, these results are novel and could be useful for many other problems.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5206\u6790\u4e86\u6f5c\u5728\u6a21\u578b\u7cbe\u786e\u63a8\u7406\u7684\u5145\u8981\u6761\u4ef6\u3002\u5728\u6f5c\u5728\u6a21\u578b\u4e2d\uff0c\u6bcf\u4e2a\u5b9e\u4f53\u90fd\u4e0e\u67d0\u4e2a\u6982\u7387\u5206\u5e03\u540e\u7684\u6f5c\u5728\u53d8\u91cf\u76f8\u5173\u8054\u3002\u6211\u4eec\u8bd5\u56fe\u89e3\u51b3\u7684\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\u662f: \u6211\u4eec\u80fd\u5426\u5728\u4e0d\u89c2\u5bdf\u6f5c\u53d8\u91cf\u7684\u60c5\u51b5\u4e0b\u6267\u884c\u7cbe\u786e\u63a8\u7406\uff0c\u5373\u4f7f\u4e0d\u77e5\u9053\u6f5c\u53d8\u91cf\u7684\u9886\u57df\u662f\u4ec0\u4e48\uff1f\u6211\u4eec\u8bc1\u660e\u4e86\u5728\u4e0d\u77e5\u9053\u6f5c\u53d8\u91cf\u53ca\u5176\u57df\u7684\u60c5\u51b5\u4e0b\uff0c\u534a\u5b9a\u89c4\u5212\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u7cbe\u786e\u63a8\u7406\u3002\u6211\u4eec\u7684\u5206\u6790\u9ad8\u7cbe\u5ea6\u5730\u9884\u6d4b\u4e86 SDP \u7684\u5b9e\u9a8c\u6b63\u786e\u6027\uff0c\u8868\u660e\u6211\u4eec\u5bf9 Karush-Kuhn-Tucker (KKT)\u6761\u4ef6\u548c\u6b63\u786e\u5b9a\u4e49\u7684\u77e9\u9635\u8c31\u7684\u5173\u6ce8\u662f\u9002\u5b9c\u7684\u3002\u4f5c\u4e3a\u5206\u6790\u7684\u526f\u4ea7\u54c1\uff0c\u6211\u4eec\u8fd8\u63d0\u4f9b\u4e86\u4f9d\u8d56\u4e8e\u6f5c\u53d8\u91cf\u7684\u6d53\u5ea6\u4e0d\u7b49\u5f0f\uff0c\u5305\u62ec\u6709\u754c\u77e9\u6bcd\u51fd\u6570\u548c\u77e9\u9635\u8c31\u3002\u636e\u6211\u4eec\u6240\u77e5\uff0c\u8fd9\u4e9b\u7ed3\u679c\u662f\u65b0\u9896\u7684\uff0c\u53ef\u80fd\u5bf9\u8bb8\u591a\u5176\u4ed6\u95ee\u9898\u6709\u7528\u3002<\/span><\/p>\n


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    \u6301\u7eed\u6f14\u5316\u7f51\u7edc\u4e2d\u6301\u4e45\u6d3b\u52a8\u7684\u6316\u6398<\/strong><\/span><\/p>\n

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

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

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

    Mining Persistent Activity in Continually Evolving Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15410<\/span><\/p>\n

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

    Caleb Belth,Xinyi Zheng,Danai Koutra<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns’ frequencies, for how long and how regularly they have occurred—i.e., their persistence—can add to our understanding of evolving networks. In this work, we propose the problem of mining activity that persists through time in continually evolving networks—i.e., activity that repeatedly and consistently occurs. We extend the notion of temporal motifs to capture activity among specific nodes, in what we call activity snippets, which are small sequences of edge-updates that reoccur. We propose axioms and properties that a measure of persistence should satisfy, and develop such a persistence measure. We also propose PENminer, an efficient framework for mining activity snippets’ Persistence in Evolving Networks, and design both offline and streaming algorithms. We apply PENminer to numerous real, large-scale evolving networks and edge streams, and find activity that is surprisingly regular over a long period of time, but too infrequent to be discovered by aggregate count alone, and bursts of activity exposed by their lack of persistence. Our findings with PENminer include neighborhoods in NYC where taxi traffic persisted through Hurricane Sandy, the opening of new bike-stations, characteristics of social network users, and more. Moreover, we use PENminer towards identifying anomalies in multiple networks, outperforming baselines at identifying subtle anomalies by 9.8-48% in AUC.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u9891\u7e41\u6a21\u5f0f\u6316\u6398\u662f\u4e00\u4e2a\u5173\u952e\u7684\u7814\u7a76\u9886\u57df\uff0c\u5b83\u4f7f\u6211\u4eec\u6df1\u5165\u4e86\u89e3\u4e0d\u65ad\u6f14\u5316\u7684\u7f51\u7edc\u7684\u7ed3\u6784\u548c\u52a8\u6001\uff0c\u5982\u793e\u4f1a\u7f51\u7edc\u6216\u9053\u8def\u7f51\u7edc\u3002\u7136\u800c\uff0c\u4e00\u4e2a\u7f51\u7edc\u4e0d\u4ec5\u5728\u8fdb\u5316\uff0c\u800c\u4e14\u5f80\u5f80\u662f\u5b83\u8fdb\u5316\u7684\u65b9\u5f0f\uff0c\u5b83\u81ea\u5df1\u4e5f\u5728\u8fdb\u5316\u3002\u56e0\u6b64\uff0c\u9664\u4e86\u6a21\u5f0f\u7684\u9891\u7387\u4e4b\u5916\uff0c\u4e86\u89e3\u5b83\u4eec\u53d1\u751f\u7684\u65f6\u95f4\u548c\u89c4\u5f8b—- \u4e5f\u5c31\u662f\u5b83\u4eec\u7684\u6301\u4e45\u6027—- \u53ef\u4ee5\u589e\u52a0\u6211\u4eec\u5bf9\u8fdb\u5316\u4e2d\u7684\u7f51\u7edc\u7684\u7406\u89e3\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u5728\u4e0d\u65ad\u53d1\u5c55\u7684\u7f51\u7edc\u4e2d\u6301\u7eed\u5b58\u5728\u7684\u6316\u6398\u6d3b\u52a8\u7684\u95ee\u9898—- \u4e5f\u5c31\u662f\u8bf4\uff0c\u6d3b\u52a8\u4e0d\u65ad\u5730\u3001\u6301\u7eed\u5730\u53d1\u751f\u3002\u6211\u4eec\u6269\u5c55\u4e86\u65f6\u95f4\u5e8f\u5217\u7684\u6982\u5ff5\u6765\u6355\u6349\u7279\u5b9a\u8282\u70b9\u4e4b\u95f4\u7684\u6d3b\u52a8\uff0c\u6211\u4eec\u79f0\u4e4b\u4e3a\u6d3b\u52a8\u7247\u6bb5\uff0c\u8fd9\u662f\u91cd\u590d\u51fa\u73b0\u7684\u8fb9\u7f18\u66f4\u65b0\u7684\u5c0f\u5e8f\u5217\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u6301\u4e45\u6027\u5ea6\u91cf\u5e94\u8be5\u6ee1\u8db3\u7684\u516c\u7406\u548c\u5c5e\u6027\uff0c\u5e76\u5f00\u53d1\u4e86\u8fd9\u6837\u4e00\u4e2a\u6301\u4e45\u6027\u5ea6\u91cf\u3002\u6211\u4eec\u8fd8\u63d0\u51fa\u4e86 PENminer\uff0c\u4e00\u4e2a\u5728\u6f14\u5316\u7f51\u7edc\u4e2d\u6316\u6398\u6d3b\u52a8\u7247\u6bb5\u6301\u4e45\u6027\u7684\u6709\u6548\u6846\u67b6\uff0c\u5e76\u8bbe\u8ba1\u4e86\u79bb\u7ebf\u548c\u6d41\u5f0f\u7b97\u6cd5\u3002\u6211\u4eec\u5c06 PENminer \u5e94\u7528\u4e8e\u8bb8\u591a\u771f\u5b9e\u7684\u3001\u5927\u89c4\u6a21\u6f14\u5316\u7684\u7f51\u7edc\u548c\u8fb9\u7f18\u6d41\uff0c\u5e76\u53d1\u73b0\u4e86\u5728\u5f88\u957f\u4e00\u6bb5\u65f6\u95f4\u5185\u4ee4\u4eba\u60ca\u8bb6\u5730\u6709\u89c4\u5f8b\u7684\u6d3b\u52a8\uff0c\u4f46\u662f\u4ec5\u4ec5\u901a\u8fc7\u805a\u5408\u8ba1\u6570\u662f\u65e0\u6cd5\u53d1\u73b0\u8fd9\u4e9b\u6d3b\u52a8\u7684\uff0c\u4ee5\u53ca\u7531\u4e8e\u7f3a\u4e4f\u6301\u4e45\u6027\u800c\u66b4\u9732\u51fa\u6765\u7684\u6d3b\u52a8\u7206\u53d1\u3002\u6211\u4eec\u5728 PENminer \u4e0a\u7684\u53d1\u73b0\u5305\u62ec\u7ebd\u7ea6\u5e02\u7684\u4e00\u4e9b\u793e\u533a\uff0c\u5728\u98d3\u98ce\u6851\u8fea\u6301\u7eed\u4e0d\u65ad\u7684\u51fa\u79df\u8f66\u4ea4\u901a\uff0c\u65b0\u7684\u81ea\u884c\u8f66\u7ad9\u7684\u5f00\u653e\uff0c\u793e\u4ea4\u7f51\u7edc\u7528\u6237\u7684\u7279\u70b9\u7b49\u7b49\u3002\u6b64\u5916\uff0c\u6211\u4eec\u4f7f\u7528 PENminer \u8bc6\u522b\u5f02\u5e38\u5728\u591a\u4e2a\u7f51\u7edc\uff0c\u4f18\u4e8e\u57fa\u7ebf\u5728\u8bc6\u522b\u5fae\u5999\u7684\u5f02\u5e38\u5728 AUC 9.8-48%\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    \u4e0d\u53ef\u903e\u8d8a: <\/strong><\/span><\/p>\n

    \u591a\u7ef4\u590d\u6742\u7f51\u7edc\u4e2d<\/strong><\/span><\/p>\n

    \u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u7684\u865a\u5047\u8def\u5f84\u7684\u907f\u514d<\/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

    You Shall not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15401<\/span><\/p>\n

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

    Klaus Wehmuth,Artur Ziviani,Leonardo Chinelate Costa,Ana Paula Couto da Silva,Alex Borges Vieira<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In complex network analysis, centralities based on shortest paths, such as betweenness and closeness, are widely used. More recently, many complex systems are being represented by time-varying, multilayer, and time-varying multilayer networks, i.e. multidimensional (or high order) networks. Nevertheless, it is well-known that the aggregation process may create spurious paths on the aggregated view of such multidimensional (high order) networks. Consequently, these spurious paths may then cause shortest-path based centrality metrics to produce incorrect results, thus undermining the network centrality analysis. In this context, we propose a method able to avoid taking into account spurious paths when computing centralities based on shortest paths in multidimensional (or high order) networks. Our method is based on MultiAspect Graphs (MAG) to represent the multidimensional networks and we show that well-known centrality algorithms can be straightforwardly adapted to the MAG environment. Moreover, we show that, by using this MAG representation, pitfalls usually associated with spurious paths resulting from aggregation in multidimensional networks can be avoided at the time of the aggregation process. As a result, shortest-path based centralities are assured to be computed correctly for multidimensional networks, without taking into account spurious paths that could otherwise lead to incorrect results. We also present a case study that shows the impact of spurious paths in the computing of shortest paths and consequently of shortest-path based centralities, thus illustrating the importance of this contribution.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u590d\u6742\u7f51\u7edc\u5206\u6790\u4e2d\uff0c\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u7684\u4e2d\u5fc3\u88ab\u5e7f\u6cdb\u5e94\u7528\u3002\u6700\u8fd1\uff0c\u8bb8\u591a\u590d\u6742\u7cfb\u7edf\u88ab\u65f6\u53d8\u3001\u591a\u5c42\u548c\u65f6\u53d8\u7684\u591a\u5c42\u7f51\u7edc\u8868\u793a\uff0c\u5373\u591a\u7ef4(\u6216\u9ad8\u9636)\u7f51\u7edc\u3002\u7136\u800c\uff0c\u4f17\u6240\u5468\u77e5\uff0c\u805a\u5408\u8fc7\u7a0b\u53ef\u80fd\u5728\u8fd9\u79cd\u591a\u7ef4(\u9ad8\u9636)\u7f51\u7edc\u7684\u805a\u5408\u89c6\u56fe\u4e0a\u521b\u5efa\u865a\u5047\u8def\u5f84\u3002\u56e0\u6b64\uff0c\u8fd9\u4e9b\u865a\u5047\u8def\u5f84\u53ef\u80fd\u5bfc\u81f4\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u7684\u4e2d\u5fc3\u6027\u5ea6\u91cf\u4ea7\u751f\u9519\u8bef\u7684\u7ed3\u679c\uff0c\u4ece\u800c\u7834\u574f\u4e86\u7f51\u7edc\u4e2d\u5fc3\u6027\u5206\u6790\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b9\u6cd5\uff0c\u80fd\u591f\u907f\u514d\u8003\u8651\u865a\u5047\u8def\u5f84\u65f6\uff0c\u8ba1\u7b97\u4e2d\u5fc3\u7684\u591a\u7ef4(\u6216\u9ad8\u9636)\u7f51\u7edc\u7684\u6700\u77ed\u8def\u5f84\u3002\u6211\u4eec\u7684\u65b9\u6cd5\u662f\u57fa\u4e8e\u591a\u65b9\u9762\u56fe(MAG)\u6765\u8868\u793a\u591a\u7ef4\u7f51\u7edc\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u4f17\u6240\u5468\u77e5\u7684\u4e2d\u5fc3\u6027\u7b97\u6cd5\u53ef\u4ee5\u76f4\u63a5\u9002\u7528\u4e8e MAG \u73af\u5883\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u8bc1\u660e\uff0c\u901a\u8fc7\u4f7f\u7528\u8fd9\u79cd MAG \u8868\u793a\uff0c\u5728\u805a\u5408\u8fc7\u7a0b\u4e2d\u53ef\u4ee5\u907f\u514d\u901a\u5e38\u4e0e\u591a\u7ef4\u7f51\u7edc\u4e2d\u805a\u5408\u4ea7\u751f\u7684\u865a\u5047\u8def\u5f84\u76f8\u5173\u7684\u9677\u9631\u3002\u56e0\u6b64\uff0c\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u7684\u96c6\u4e2d\u5ea6\u53ef\u4ee5\u786e\u4fdd\u6b63\u786e\u8ba1\u7b97\u591a\u7ef4\u7f51\u7edc\uff0c\u800c\u4e0d\u5fc5\u8003\u8651\u53ef\u80fd\u5bfc\u81f4\u9519\u8bef\u7ed3\u679c\u7684\u865a\u5047\u8def\u5f84\u3002\u6211\u4eec\u8fd8\u63d0\u51fa\u4e86\u4e00\u4e2a\u6848\u4f8b\u7814\u7a76\uff0c\u663e\u793a\u4e86\u865a\u5047\u8def\u5f84\u5728\u8ba1\u7b97\u6700\u77ed\u8def\u5f84\uff0c\u56e0\u6b64\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u96c6\u4e2d\u7684\u5f71\u54cd\uff0c\u4ece\u800c\u8bf4\u660e\u4e86\u8fd9\u4e00\u8d21\u732e\u7684\u91cd\u8981\u6027\u3002<\/span><\/p>\n


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    \u5b8c\u5168\u91c7\u7528\u53cd\u9988\u4e0b\u5f71\u54cd\u529b<\/strong><\/span><\/p>\n

    \u6700\u5927\u5316\u81ea\u9002\u5e94\u95f4\u9699\u7684\u4f18\u5316\u754c <\/strong><\/span><\/p>\n

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

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

    Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15374<\/span><\/p>\n

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

    Gianlorenzo D’Angelo,Debashmita Poddar,Cosimo Vinci<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In the influence maximization (IM) problem, we are given a social network and a budget<\/span><\/nobr>k, and we look for a set of <\/span><\/nobr>k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion. In this paper, we study the adaptive IM, where the nodes are selected sequentially one by one, and the decision on the <\/span><\/nobr>ith seed can be based on the observed cascade produced by the first <\/span><\/nobr>i\u22121 seeds. We focus on the full-adoption feedback in which we can observe the entire cascade of each previously selected seed and on the independent cascade model where each edge is associated with an independent probability of diffusing influence.
    Our main result is the first sub-linear upper bound that holds for any graph. Specifically, we show that the adaptivity gap is upper-bounded by <\/span>    <\/nobr>\u2308n1\/3\u2309, where <\/span><\/nobr>n is the number of nodes in the graph. Moreover, we improve over the known upper bound for in-arborescences from <\/span><\/nobr>2ee\u22121\u22483.16 to <\/span><\/nobr>2e2e2\u22121\u22482.31. Finally, we study <\/span><\/nobr>\u03b1-bounded graphs, a class of undirected graphs in which the sum of node degrees higher than two is at most <\/span><\/nobr>\u03b1, and show that the adaptivity gap is upper-bounded by <\/span><\/nobr>\u03b1\u2212\u2212\u221a+O(1). Moreover, we show that in 0-bounded graphs, i.e. undirected graphs in which each connected component is a path or a cycle, the adaptivity gap is at most <\/span><\/nobr>3e3e3\u22121\u22483.16. To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u5f71\u54cd\u6700\u5927\u5316(IM)\u95ee\u9898\u4e2d\uff0c\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u793e\u4f1a\u7f51\u7edc\u548c\u4e00\u4e2a\u9884\u7b97\u5728\u5f71\u54cd\u6700\u5927\u5316(IM)\u95ee\u9898\u4e2d\uff0c\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u793e\u4f1a\u7f51\u7edc\u548c\u4e00\u4e2a\u9884\u7b97<\/span><\/nobr>k,\u6211\u4eec\u5bfb\u627e\u4e00\u5957<\/span><\/nobr>k\u7f51\u7edc\u4e2d\u7684\u8282\u70b9\uff0c\u79f0\u4e3a\u79cd\u5b50\uff0c\u6839\u636e\u5f71\u54cd\u6269\u6563\u7684\u968f\u673a\u6a21\u578b\uff0c\u6700\u5927\u5316\u7531\u79cd\u5b50\u4ea7\u751f\u7684\u5f71\u54cd\u7ea7\u8054\u6240\u8fbe\u5230\u7684\u9884\u671f\u8282\u70b9\u6570\u3002\u672c\u6587\u7814\u7a76\u4e86\u81ea\u9002\u5e94\u6df7\u5408\u9057\u4f20\u7b97\u6cd5\uff0c\u5176\u4e2d\u8282\u70b9\u6309\u987a\u5e8f\u9010\u4e2a\u9009\u62e9\uff0c\u8282\u70b9\u7684\u9009\u62e9\u4f9d\u8d56\u4e8e\u81ea\u9002\u5e94\u6df7\u5408\u9057\u4f20\u7b97\u6cd5i\u79cd\u5b50\u53ef\u4ee5\u57fa\u4e8e\u89c2\u5bdf\u5230\u7684\u7ea7\u8054\u4ea7\u751f\u7684\u7b2c\u4e00<\/span><\/nobr>i\u22121 \u79cd\u5b50\u3002\u6211\u4eec\u91cd\u70b9\u7814\u7a76\u4e86\u5b8c\u5168\u91c7\u7528\u53cd\u9988\uff0c\u5728\u8fd9\u79cd\u53cd\u9988\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230\u4ee5\u524d\u9009\u62e9\u7684\u6bcf\u4e2a\u79cd\u5b50\u7684\u6574\u4e2a\u7ea7\u8054\uff0c\u4ee5\u53ca\u6bcf\u4e2a\u8fb9\u90fd\u4e0e\u6269\u6563\u5f71\u54cd\u7684\u72ec\u7acb\u6982\u7387\u76f8\u5173\u7684\u72ec\u7acb\u7ea7\u8054\u6a21\u578b\u6211\u4eec\u7684\u4e3b\u8981\u7ed3\u679c\u662f\u7b2c\u4e00\u4e2a\u9002\u7528\u4e8e\u4efb\u4f55\u56fe\u7684\u6b21\u7ebf\u6027\u4e0a\u754c\u3002\u5177\u4f53\u5730\u8bf4\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u81ea\u9002\u5e94\u95f4\u9699\u7684\u4e0a\u754c\u4e3a<\/span><\/nobr>\u2308n1\/3\u2309,\u4e13\u9875\u3001\u54ea\u91cc<\/span><\/nobr>n \u56fe\u4e2d\u7684\u8282\u70b9\u6570\u3002\u6b64\u5916\uff0c\u6211\u4eec\u6539\u8fdb\u4e86\u6811\u5f62\u56fe\u4e2d\u5df2\u77e5\u7684\u4e0a\u754c<\/span><\/nobr>2ee\u22121\u22483.16 \u5230<\/span><\/nobr>2e2e2\u22121\u22482.31. \u6700\u540e\uff0c\u6211\u4eec\u5b66\u4e60<\/span><\/nobr>\u03b1-\u6709\u754c\u56fe\uff0c\u4e00\u7c7b\u8282\u70b9\u5ea6\u4e4b\u548c\u5927\u4e8e2\u7684\u65e0\u5411\u56fe<\/span><\/nobr>\u03b1,\u5e76\u8868\u660e\u9002\u5e94\u9699\u7684\u4e0a\u9650\u4e3a<\/span><\/nobr>\u03b1\u2212\u2212\u221a+O(1).\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86\u57280\u6709\u754c\u56fe\u4e2d\uff0c\u5373\u6bcf\u4e2a\u8fde\u63a5\u5143\u4ef6(\u56fe\u8bba)\u662f\u4e00\u6761\u8def\u6216\u4e00\u4e2a\u5708\u7684\u65e0\u5411\u56fe\u4e2d\uff0c\u81ea\u9002\u5e94\u95f4\u9699\u6700\u5927<\/span><\/nobr>3e3e3\u22121\u22483.16. \u4e3a\u4e86\u8bc1\u660e\u6211\u4eec\u7684\u754c\u9650\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u4e00\u4e9b\u65b0\u7684\u6280\u672f\u6765\u5c06\u9002\u5e94\u6027\u7b56\u7565\u4e0e\u975e\u9002\u5e94\u6027\u7b56\u7565\u76f8\u5173\u8054\uff0c\u8fd9\u4e9b\u7b56\u7565\u53ef\u80fd\u7b26\u5408\u5b83\u4eec\u81ea\u8eab\u7684\u5229\u76ca\u3002<\/span><\/p>\n


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    \u7f51\u7edc\u8282\u70b9\u611f\u77e5\u5d4c\u5165\u7684\u793e\u533a\u7ed3\u6784<\/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

    Community Structure aware Embedding of Nodes in a Network<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15313<\/span><\/p>\n

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

    Swarup Chattopadhyay,Debasis Ganguly<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Detecting communities or the modular structure of real-life networks (e.g. a social network or a product purchase network) is an important task because the way a network functions is often determined by its communities. Traditional approaches to community detection involve modularity-based algorithms, which generally speaking, construct partitions based on heuristics that seek to maximize the ratio of the edges within the partitions to those between them. On the other hand, node embedding approaches represent each node in a graph as a real-valued vector and is thereby able to transform the problem of community detection in a graph to that of clustering a set of vectors. Existing node embedding approaches are primarily based on, first, initiating random walks from each node to construct a context of a node, and then make the vector representation of a node close to its context. However, standard node embedding approaches do not directly take into account the community structure of a network while constructing the context around each node. To alleviate this, we explore two different threads of work. First, we investigate the use of maximum entropy-based random walks to obtain more centrality preserving embedding of nodes, which may lead to more effective clusters in the embedded space. Second, we propose a community structure-aware node embedding approach, where we incorporate modularity-based partitioning heuristics into the objective function of node embedding. We demonstrate that our proposed combination of the combinatorial and the embedding approaches for community detection outperforms a number of modularity-based baselines and K-means clustering on a standard node-embedded (node2vec) vector space on a wide range of real-life and synthetic networks of different sizes and densities.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u68c0\u6d4b\u793e\u533a\u6216\u73b0\u5b9e\u751f\u6d3b\u4e2d\u7f51\u7edc\u7684\u6a21\u5757\u7ed3\u6784(\u4f8b\u5982\u793e\u4f1a\u7f51\u7edc\u6216\u4ea7\u54c1\u8d2d\u4e70\u7f51\u7edc)\u662f\u4e00\u9879\u91cd\u8981\u7684\u4efb\u52a1\uff0c\u56e0\u4e3a\u7f51\u7edc\u7684\u8fd0\u4f5c\u65b9\u5f0f\u5f80\u5f80\u7531\u5176\u793e\u533a\u51b3\u5b9a\u3002\u4f20\u7edf\u7684\u793e\u533a\u68c0\u6d4b\u65b9\u6cd5\u6d89\u53ca\u57fa\u4e8e\u6a21\u5757\u5316\u7684\u7b97\u6cd5\uff0c\u4e00\u822c\u6765\u8bf4\uff0c\u8fd9\u79cd\u7b97\u6cd5\u57fa\u4e8e\u542f\u53d1\u5f0f\u7b97\u6cd5\u6784\u9020\u5206\u533a\uff0c\u5bfb\u6c42\u6700\u5927\u5316\u5206\u533a\u5185\u90e8\u8fb9\u4e0e\u5206\u533a\u4e4b\u95f4\u8fb9\u7684\u6bd4\u4f8b\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u8282\u70b9\u5d4c\u5165\u65b9\u6cd5\u5c06\u56fe\u4e2d\u7684\u6bcf\u4e2a\u8282\u70b9\u8868\u793a\u4e3a\u4e00\u4e2a\u5b9e\u503c\u5411\u91cf\uff0c\u4ece\u800c\u80fd\u591f\u5c06\u56fe\u4e2d\u7684\u793e\u533a\u68c0\u6d4b\u95ee\u9898\u8f6c\u5316\u4e3a\u4e00\u7ec4\u5411\u91cf\u7684\u805a\u7c7b\u95ee\u9898\u3002\u73b0\u6709\u7684\u8282\u70b9\u5d4c\u5165\u65b9\u6cd5\u4e3b\u8981\u57fa\u4e8e\uff0c\u9996\u5148\u4ece\u6bcf\u4e2a\u8282\u70b9\u542f\u52a8\u968f\u673a\u6f2b\u6e38\u6765\u6784\u9020\u8282\u70b9\u7684\u4e0a\u4e0b\u6587\uff0c\u7136\u540e\u4f7f\u8282\u70b9\u7684\u77e2\u91cf\u8868\u793a\u63a5\u8fd1\u5176\u4e0a\u4e0b\u6587\u3002\u7136\u800c\uff0c\u6807\u51c6\u7684\u8282\u70b9\u5d4c\u5165\u65b9\u6cd5\u5728\u6784\u9020\u6bcf\u4e2a\u8282\u70b9\u7684\u4e0a\u4e0b\u6587\u65f6\u5e76\u6ca1\u6709\u76f4\u63a5\u8003\u8651\u5230\u7f51\u7edc\u7684\u793e\u533a\u7ed3\u6784\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86\u4e24\u79cd\u4e0d\u540c\u7684\u5de5\u4f5c\u601d\u8def\u3002\u9996\u5148\uff0c\u6211\u4eec\u7814\u7a76\u4f7f\u7528\u57fa\u4e8e\u6700\u5927\u71b5\u7684\u968f\u673a\u6e38\u52a8\u6765\u4fdd\u6301\u8282\u70b9\u5d4c\u5165\u7684\u4e2d\u5fc3\u6027\uff0c\u4ece\u800c\u5728\u5d4c\u5165\u7a7a\u95f4\u4e2d\u4ea7\u751f\u66f4\u6709\u6548\u7684\u7c07\u3002\u5176\u6b21\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u793e\u533a\u7ed3\u6784\u611f\u77e5\u7684\u8282\u70b9\u5d4c\u5165\u65b9\u6cd5\uff0c\u5728\u8282\u70b9\u5d4c\u5165\u7684\u76ee\u6807\u51fd\u6570\u4e2d\u5f15\u5165\u4e86\u57fa\u4e8e\u6a21\u5757\u5316\u7684\u5206\u5272\u542f\u53d1\u5f0f\u7b97\u6cd5\u3002\u6211\u4eec\u8bc1\u660e\uff0c\u6211\u4eec\u63d0\u51fa\u7684\u7ec4\u5408\u548c\u5d4c\u5165\u65b9\u6cd5\u7684\u793e\u533a\u68c0\u6d4b\u7ec4\u5408\u4f18\u4e8e\u4e00\u4e2a\u6807\u51c6\u7684\u8282\u70b9\u5d4c\u5165(node2vec)\u5411\u91cf\u7a7a\u95f4\u4e0a\u7684\u6a21\u5757\u5316\u57fa\u7ebf\u548c K\u5e73\u5747\u7b97\u6cd5\uff0c\u5728\u4e00\u4e2a\u4e0d\u540c\u89c4\u6a21\u548c\u5bc6\u5ea6\u7684\u5b9e\u9645\u751f\u6d3b\u548c\u5408\u6210\u7f51\u7edc\u7684\u8303\u56f4\u5e7f\u6cdb\u3002<\/span><\/p>\n


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    \u901a\u8fc7\u5c0f\u9053\u6d88\u606f\u5b66\u4e60: <\/strong><\/span><\/p>\n

    \u566a\u97f3\u7684\u5f71\u54cd\u548c\u793e\u4f1a\u7f51\u7edc\u7684\u5e7f\u5ea6\u548c\u6df1\u5ea6<\/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

    Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/1812.03354<\/span><\/p>\n

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

    Matthew O. Jackson,Suraj Malladi,David McAdams<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We examine how well people learn when information is noisily relayed from person to person; and we study how communication platforms can improve learning without censoring or fact-checking messages. We analyze learning as a function of social network depth (how many times information is relayed) and breadth (the number of relay chains accessed). Noise builds up as depth increases, so learning requires greater breadth. In the presence of mutations (deliberate or random) and transmission failures of messages, we characterize sharp thresholds for breadths above which receivers learn fully and below which they learn nothing. When there is uncertainty about mutation rates, optimizing learning requires either capping depth, or if that is not possible, limiting breadth by capping the number of people to whom someone can forward a message. Limiting breadth cuts the number of messages received but also decreases the fraction originating further from the receiver, and so can increase the signal to noise ratio. Finally, we extend our model to study learning from message survival: e.g., people are more likely to pass messages with one conclusion than another. We find that as depth grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7814\u7a76\u5f53\u4fe1\u606f\u4ece\u4e00\u4e2a\u4eba\u4f20\u5230\u53e6\u4e00\u4e2a\u4eba\u65f6\uff0c\u4eba\u4eec\u7684\u5b66\u4e60\u6548\u679c\u5982\u4f55; \u6211\u4eec\u7814\u7a76\u4ea4\u6d41\u5e73\u53f0\u5982\u4f55\u5728\u4e0d\u5ba1\u67e5\u6216\u6838\u5b9e\u4fe1\u606f\u7684\u60c5\u51b5\u4e0b\u63d0\u9ad8\u5b66\u4e60\u6548\u679c\u3002\u6211\u4eec\u5206\u6790\u5b66\u4e60\u4f5c\u4e3a\u4e00\u4e2a\u51fd\u6570\u7684\u793e\u4f1a\u7f51\u7edc\u6df1\u5ea6(\u591a\u5c11\u6b21\u4fe1\u606f\u4e2d\u7ee7)\u548c\u5e7f\u5ea6(\u4e2d\u7ee7\u94fe\u63a5\u7684\u6570\u91cf)\u3002\u968f\u7740\u6df1\u5ea6\u7684\u589e\u52a0\uff0c\u566a\u97f3\u4e5f\u4f1a\u589e\u52a0\uff0c\u6240\u4ee5\u5b66\u4e60\u9700\u8981\u66f4\u5927\u7684\u5e7f\u5ea6\u3002\u5728\u7a81\u53d8(\u6709\u610f\u6216\u968f\u673a)\u548c\u4fe1\u606f\u4f20\u8f93\u5931\u8d25\u7684\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u63cf\u8ff0\u4e86\u63a5\u6536\u8005\u5145\u5206\u5b66\u4e60\u548c\u4f4e\u4e8e\u4ed6\u4eec\u4ec0\u4e48\u4e5f\u5b66\u4e0d\u5230\u7684\u5bbd\u5ea6\u7684\u5c16\u9510\u9608\u503c\u3002\u5f53\u7a81\u53d8\u7387\u5b58\u5728\u4e0d\u786e\u5b9a\u6027\u65f6\uff0c\u4f18\u5316\u5b66\u4e60\u8981\u4e48\u9700\u8981\u9650\u5236\u6df1\u5ea6\uff0c\u8981\u4e48\u4e0d\u53ef\u80fd\uff0c\u901a\u8fc7\u9650\u5236\u67d0\u4eba\u53ef\u4ee5\u8f6c\u53d1\u4fe1\u606f\u7684\u4eba\u6570\u6765\u9650\u5236\u5e7f\u5ea6\u3002\u9650\u5236\u5bbd\u5ea6\u4f1a\u51cf\u5c11\u63a5\u6536\u5230\u7684\u6d88\u606f\u6570\u91cf\uff0c\u4f46\u4e5f\u4f1a\u8fdb\u4e00\u6b65\u51cf\u5c11\u6765\u81ea\u63a5\u6536\u5668\u7684\u90e8\u5206\uff0c\u56e0\u6b64\u53ef\u4ee5\u589e\u52a0\u4fe1\u566a\u6bd4\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c06\u6211\u4eec\u7684\u6a21\u578b\u6269\u5c55\u5230\u7814\u7a76\u4ece\u4fe1\u606f\u751f\u5b58\u4e2d\u5b66\u4e60: \u4f8b\u5982\uff0c\u4eba\u4eec\u66f4\u503e\u5411\u4e8e\u4f20\u9012\u4e00\u4e2a\u7ed3\u8bba\u800c\u4e0d\u662f\u53e6\u4e00\u4e2a\u7ed3\u8bba\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u968f\u7740\u6df1\u5ea6\u7684\u589e\u52a0\uff0c\u6240\u6709\u7684\u5b66\u4e60\u90fd\u6765\u81ea\u4e8e\u63a5\u6536\u5230\u7684\u4fe1\u606f\u7684\u603b\u6570\u6216\u8005\u6765\u81ea\u4e8e\u63a5\u6536\u5230\u7684\u4fe1\u606f\u7684\u5185\u5bb9\uff0c\u4f46\u662f\u5b66\u4e60\u8005\u4e0d\u9700\u8981\u540c\u65f6\u5173\u6ce8\u8fd9\u4e24\u8005\u3002<\/span><\/p>\n


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    \u7edf\u4e00\u8d4b\u503c\u4e0b\u7684\u5206\u6563\u7ade\u4e89\u5f3a\u76d7: <\/strong><\/span><\/p>\n

    \u652f\u914d\u8fd8\u662f\u5220\u9664<\/strong><\/span><\/p>\n

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

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

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

    Dominate or Delete: Decentralized Competing Bandits with Uniform Valuation<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15166<\/span><\/p>\n

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

    Abishek Sankararaman,Soumya Basu,Karthik Abinav Sankararaman<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms) with unknown and heterogeneous valuations. Such markets abstract online matching platforms (for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al. cite{matching_bandits}. The uniform valuation in the demand side admits a unique stable matching equilibrium in the system. We design the first decentralized algorithm – fullname; (name), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon, and thus partially resolves an open question in cite{matching_bandits}. name; works in phases of exponentially increasing length. In each phase <\/span><\/nobr>i, an agent first deletes dominated arms — the arms preferred by agents ranked higher than itself. Deletion follows dynamic explore-exploit using UCB algorithm on the remaining arms for <\/span><\/nobr>2i rounds. {Finally, the preferred arm is broadcast in a decentralized fashion to other agents through {em pure exploitation} in <\/span><\/nobr>(N\u22121)K rounds with <\/span><\/nobr>N agents and <\/span><\/nobr>K arms.} Comparing the obtained reward with respect to the unique stable matching, we show that name; achieves <\/span><\/nobr>O(log(T)\/\u03942) regret in <\/span><\/nobr>T rounds, where <\/span><\/nobr>\u0394 is the minimum gap across all agents and arms. We provide a (orderwise) matching regret lower-bound.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u7814\u7a76\u4e86\u53cc\u8fb9\u5339\u914d\u5e02\u573a\u4e2d\u7684\u9057\u61be\u6700\u5c0f\u5316\u95ee\u9898\uff0c\u5728\u53cc\u8fb9\u5339\u914d\u5e02\u573a\u4e2d\uff0c\u4ef7\u503c\u4e00\u81f4\u7684\u9700\u6c42\u65b9\u4ee3\u7406\u4eba(\u53c8\u79f0\u4ee3\u7406\u4eba)\u4e0d\u65ad\u5730\u4e0e\u4ef7\u503c\u4e0d\u786e\u5b9a\u7684\u4f9b\u5e94\u65b9\u4ee3\u7406\u4eba(\u53c8\u79f0\u6b66\u5668\u4ee3\u7406\u4eba)\u7ade\u4e89\u83b7\u5f97\u5339\u914d\u3002\u8fd9\u4e9b\u5e02\u573a\u62bd\u8c61\u51fa\u5728\u7ebf\u5339\u914d\u5e73\u53f0(\u4f8b\u5982 UpWork\u3001 TaskRabbit) \uff0c\u5e76\u4e14\u5c5e\u4e8e Liu \u7b49\u4eba\u5f15\u7528\u7684\u914d\u5bf9\u571f\u532a\u6a21\u578b\u7684\u8303\u7574\u3002\u9700\u6c42\u4fa7\u7684\u7edf\u4e00\u5b9a\u4ef7\u4f7f\u7cfb\u7edf\u4e2d\u5b58\u5728\u552f\u4e00\u7684\u7a33\u5b9a\u5339\u914d\u5747\u8861\u3002\u6211\u4eec\u8bbe\u8ba1\u4e86\u7b2c\u4e00\u4e2a\u5206\u6563\u7b97\u6cd5\u2014\u2014\u5168\u540d; (\u540d\u79f0) \uff0c\u7528\u4e8e\u5728\u7edf\u4e00\u4f30\u503c\u4e0b\u5339\u914d\u571f\u532a\uff0c\u4e0d\u9700\u8981\u4efb\u4f55\u5173\u4e8e\u60ac\u8d4f\u95f4\u9694\u6216\u65f6\u95f4\u8303\u56f4\u7684\u77e5\u8bc6\uff0c\u4ece\u800c\u90e8\u5206\u89e3\u51b3\u4e86\u5728\u5f15\u7528{ matching _ bandits }\u4e2d\u7684\u4e00\u4e2a\u5f00\u653e\u6027\u95ee\u9898\u3002\u5de5\u4f5c\u5728\u6307\u6570\u589e\u957f\u7684\u957f\u5ea6\u7684\u9636\u6bb5\u3002\u5728\u6bcf\u4e2a\u9636\u6bb5<\/span><\/nobr>i,\u4e00\u4e2a\u4ee3\u7406\u4eba\u9996\u5148\u5220\u9664\u4e3b\u5bfc\u6b66\u5668—- \u4ee3\u7406\u4eba\u63d0\u5230\u7684\u6b66\u5668\u6392\u540d\u9ad8\u4e8e\u81ea\u5df1\u3002\u5220\u9664\u9075\u5faa\u52a8\u6001\u63a2\u7d22-\u5f00\u53d1\u4f7f\u7528 UCB \u7b97\u6cd5\u5bf9\u5269\u4f59\u7684\u6b66\u5668<\/span><\/nobr>2i \u6700\u540e\uff0c\u901a\u8fc7{ em pure exploitation }\u4ee5\u5206\u6563\u7684\u65b9\u5f0f\u5411\u5176\u4ed6\u4ee3\u7406\u673a\u6784\u5e7f\u64ad\u9996\u9009\u81c2<\/span><\/nobr>(N\u22121)K \u4e0e… \u5468\u65cb<\/span><\/nobr>N \u4ee3\u7406\u4eba<\/span><\/nobr>K \u5c06\u83b7\u5f97\u7684\u62a5\u916c\u4e0e\u72ec\u7279\u7684\u9a6c\u5339\u5339\u914d\u8fdb\u884c\u6bd4\u8f83\uff0c\u6211\u4eec\u663e\u793a\u8fd9\u4e2a\u540d\u79f0;\u5b9e\u73b0<\/span><\/nobr>O(log(T)\/\u03942)  \u540e\u6094<\/span><\/nobr>T \u5faa\u73af\uff0c\u5728\u54ea\u91cc<\/span><\/nobr>\u0394 \u6240\u6709\u4ee3\u7406\u548c\u6b66\u5668\u7684\u6700\u5c0f\u5dee\u8ddd\u3002\u6211\u4eec\u63d0\u4f9b\u4e00\u4e2a(\u8ba2\u5355)\u5339\u914d\u7684\u9057\u61be\u4e0b\u9650\u3002<\/span><\/p>\n


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    \u4e0e\u4f60\u7684\u793e\u533a\u4fdd\u6301\u8054\u7cfb: <\/strong><\/span><\/p>\n

    \u96c6\u7fa4\u4e4b\u95f4\u7684\u6865\u6881\u89e6\u53d1\u4e86<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u6269\u5f20<\/strong><\/span><\/strong><\/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

    Stay with Your Community: Bridges between Clusters Trigger Expansion of COVID-19<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16047<\/span><\/p>\n

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

    Yukio Ohsawa,Masaharu Tsubokura<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The spreading of virus infection is here simulated over artificial human networks. The real-space urban life of people is modeled as a modified scale-free network with constraints. A scale-free network has been adopted in several studies for modeling on-line communities so far but is modified here for the aim to represent peoples’ social behaviors where the generated communities are restricted reflecting the spatiotemporal constraints in the real life. Furthermore, the networks have been extended by introducing multiple cliques in the initial step of network construction and enabling people to zero-degree people as well as popular (large degree) people. As a result, four findings and a policy proposal have been obtained. First, the “second waves” occur without external influence or constraints on contacts or the releasing of the constraints. These second waves, mostly lower than the first wave, implies the bridges between infected and fresh clusters may trigger new expansions of spreading. Second, if the network changes the structure on the way of infection spreading or after its suppression, the peak of the second wave can be larger than the first. Third, the peak height in the time series depends on the difference between the upper bound of the number of people each member accepts to meet and the number of people one chooses to meet. This tendency is observed for two kinds of artificial networks and implies the impact of the bridges between communities on the virus spreading. Fourth, the release of once given constraint may trigger a second wave higher than the peak of the time series without introducing any constraint from the beginning, if the release is introduced at a time close to the peak. Thus, both governments and individuals should be careful in returning to human society with inter-community contacts.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8fd9\u91cc\u6a21\u62df\u4e86\u75c5\u6bd2\u611f\u67d3\u5728\u4eba\u5de5\u7f51\u7edc\u4e0a\u7684\u4f20\u64ad\u3002\u4eba\u4eec\u771f\u5b9e\u7684\u57ce\u5e02\u751f\u6d3b\u88ab\u6a21\u62df\u4e3a\u4e00\u4e2a\u6709\u7ea6\u675f\u7684\u4fee\u6539\u8fc7\u7684\u65e0\u5c3a\u5ea6\u7f51\u7edc\u3002\u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u4e00\u4e9b\u7814\u7a76\u5df2\u7ecf\u91c7\u7528\u4e86\u4e00\u4e2a\u65e0\u5c3a\u5ea6\u7f51\u7edc\u6a21\u578b\u6765\u6a21\u62df\u5728\u7ebf\u793e\u533a\uff0c\u4f46\u662f\u5728\u8fd9\u91cc\u88ab\u4fee\u6539\uff0c\u76ee\u7684\u662f\u4ee3\u8868\u4eba\u4eec\u7684\u793e\u4f1a\u884c\u4e3a\uff0c\u5176\u4e2d\u751f\u6210\u7684\u793e\u533a\u53d7\u5230\u9650\u5236\uff0c\u53cd\u6620\u4e86\u73b0\u5b9e\u751f\u6d3b\u4e2d\u7684\u65f6\u7a7a\u7ea6\u675f\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u5728\u7f51\u7edc\u5efa\u8bbe\u7684\u521d\u59cb\u9636\u6bb5\u5f15\u5165\u591a\u4e2a\u5c0f\u96c6\u56e2\uff0c\u4f7f\u4eba\u4eec\u65e2\u53ef\u4ee5\u6210\u4e3a\u96f6\u5ea6\u4eba\u7fa4\uff0c\u4e5f\u53ef\u4ee5\u6210\u4e3a\u53d7\u6b22\u8fce\u7684(\u5927\u5ea6)\u4eba\u7fa4\uff0c\u6269\u5927\u4e86\u7f51\u7edc\u7684\u8303\u56f4\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u5f97\u51fa\u4e86\u56db\u9879\u7814\u7a76\u7ed3\u679c\u548c\u4e00\u9879\u653f\u7b56\u5efa\u8bae\u3002\u9996\u5148\uff0c\u201c\u7b2c\u4e8c\u6ce2\u201d\u53d1\u751f\u65f6\u6ca1\u6709\u5916\u754c\u7684\u5f71\u54cd\u6216\u7ea6\u675f\uff0c\u4e5f\u6ca1\u6709\u5bf9\u63a5\u89e6\u6216\u7ea6\u675f\u7684\u91ca\u653e\u3002\u8fd9\u4e9b\u7b2c\u4e8c\u6ce2\uff0c\u5927\u591a\u6570\u4f4e\u4e8e\u7b2c\u4e00\u6ce2\uff0c\u610f\u5473\u7740\u611f\u67d3\u548c\u65b0\u7684\u7fa4\u96c6\u4e4b\u95f4\u7684\u6865\u6881\u53ef\u80fd\u4f1a\u5f15\u53d1\u65b0\u7684\u6269\u6563\u3002\u5176\u6b21\uff0c\u5982\u679c\u7f51\u7edc\u5728\u4f20\u64ad\u9014\u5f84\u4e0a\u6216\u6291\u5236\u540e\u6539\u53d8\u7ed3\u6784\uff0c\u5219\u7b2c\u4e8c\u6ce2\u7684\u5cf0\u503c\u53ef\u80fd\u5927\u4e8e\u7b2c\u4e00\u6ce2\u3002\u7b2c\u4e09\uff0c\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u5cf0\u503c\u9ad8\u5ea6\u53d6\u51b3\u4e8e\u6bcf\u4e2a\u6210\u5458\u63a5\u53d7\u4f1a\u9762\u7684\u4eba\u6570\u4e0a\u9650\u548c\u9009\u62e9\u4f1a\u9762\u7684\u4eba\u6570\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u8fd9\u79cd\u8d8b\u52bf\u5728\u4e24\u79cd\u4eba\u5de5\u7f51\u7edc\u4e2d\u90fd\u53ef\u4ee5\u89c2\u5bdf\u5230\uff0c\u8fd9\u610f\u5473\u7740\u7fa4\u843d\u4e4b\u95f4\u7684\u6865\u6881\u5bf9\u75c5\u6bd2\u4f20\u64ad\u7684\u5f71\u54cd\u3002\u7b2c\u56db\uff0c\u91ca\u653e\u4e00\u6b21\u7ed9\u5b9a\u7684\u7ea6\u675f\u53ef\u80fd\u89e6\u53d1\u6bd4\u65f6\u95f4\u5e8f\u5217\u5cf0\u503c\u66f4\u9ad8\u7684\u7b2c\u4e8c\u6ce2\uff0c\u5982\u679c\u91ca\u653e\u65f6\u95f4\u63a5\u8fd1\u5cf0\u503c\uff0c\u5219\u4ece\u4e00\u5f00\u59cb\u5c31\u4e0d\u5f15\u5165\u4efb\u4f55\u7ea6\u675f\u3002\u56e0\u6b64\uff0c\u65e0\u8bba\u662f\u653f\u5e9c\u8fd8\u662f\u4e2a\u4eba\uff0c\u5728\u56de\u5f52\u4eba\u7c7b\u793e\u4f1a\u7684\u8fc7\u7a0b\u4e2d\u90fd\u5e94\u8be5\u5c0f\u5fc3\u8c28\u614e\u3002<\/span><\/p>\n


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    \u4e00\u79cd\u7528\u4e8e\u6d41\u884c\u75c5\u63a7\u5236\u7684<\/strong><\/span><\/p>\n

    \u9690\u79c1\u4fdd\u62a4\u6d4b\u8bd5\u4f18\u5316\u7b97\u6cd5<\/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 privacy-preserving tests optimization algorithm for epidemics containment<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.15977<\/span><\/p>\n

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

    Alessandro Nuara,Francesco Trov\u00f2,Nicola Gatti<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The SARS-CoV-2 outbreak changed the everyday life of almost all the people over the world.Currently, we are facing with the problem of containing the spread of the virus both using the more effective forced lockdown, which has the drawback of slowing down the economy of the involved countries, and by identifying and isolating the positive individuals, which, instead, is an hard task in general due to the lack of information. For this specific disease, the identificato of the infected is particularly challenging since there exists cathegories, namely the asymptomatics, who are positive and potentially contagious, but do not show any of the symptoms of SARS-CoV-2. Until the developement and distribution of a vaccine is not yet ready, we need to design ways of selecting those individuals which are most likely infected, given the limited amount of tests which are available each day. In this paper, we make use of available data collected by the so called contact tracing apps to develop an algorithm, namely PPTO, that identifies those individuals that are most likely positive and, therefore, should be tested. While in the past these analysis have been conducted by centralized algorithms, requiring that all the app users data are gathered in a single database, our protocol is able to work on a device level, by exploiting the communication of anonymized information to other devices.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>SARS-CoV-2\u7684\u7206\u53d1\u6539\u53d8\u4e86\u4e16\u754c\u4e0a\u51e0\u4e4e\u6240\u6709\u4eba\u7684\u65e5\u5e38\u751f\u6d3b\u3002\u76ee\u524d\uff0c\u6211\u4eec\u9762\u4e34\u7740\u904f\u5236\u75c5\u6bd2\u4f20\u64ad\u7684\u95ee\u9898\uff0c\u4e00\u65b9\u9762\u91c7\u53d6\u66f4\u6709\u6548\u7684\u5f3a\u5236\u5c01\u9501\u63aa\u65bd\uff0c\u8fd9\u79cd\u63aa\u65bd\u7684\u7f3a\u70b9\u662f\u51cf\u7f13\u6709\u5173\u56fd\u5bb6\u7684\u7ecf\u6d4e\uff0c\u53e6\u4e00\u65b9\u9762\u67e5\u660e\u548c\u9694\u79bb\u79ef\u6781\u7684\u4e2a\u4eba\uff0c\u7531\u4e8e\u7f3a\u4e4f\u4fe1\u606f\uff0c\u8fd9\u5728\u603b\u4f53\u4e0a\u662f\u4e00\u9879\u8270\u5de8\u7684\u4efb\u52a1\u3002\u5bf9\u4e8e\u8fd9\u79cd\u7279\u6b8a\u7684\u75be\u75c5\uff0c\u9274\u5b9a\u611f\u67d3\u8005\u662f\u7279\u522b\u5177\u6709\u6311\u6218\u6027\u7684\uff0c\u56e0\u4e3a\u5b58\u5728\u7ec4\u7ec7\u5b66\uff0c\u5373\u65e0\u75c7\u72b6\u8005\uff0c\u4ed6\u4eec\u5448\u9633\u6027\uff0c\u53ef\u80fd\u5177\u6709\u4f20\u67d3\u6027\uff0c\u4f46\u6ca1\u6709\u8868\u73b0\u51fa SARS-CoV-2\u7684\u4efb\u4f55\u75c7\u72b6\u3002\u5728\u75ab\u82d7\u7684\u5f00\u53d1\u548c\u5206\u53d1\u5c1a\u672a\u51c6\u5907\u5c31\u7eea\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u8bbe\u8ba1\u51fa\u9009\u62e9\u90a3\u4e9b\u6700\u6709\u53ef\u80fd\u611f\u67d3\u7684\u4e2a\u4f53\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u6bcf\u5929\u53ef\u7528\u7684\u6d4b\u8bd5\u6570\u91cf\u6709\u9650\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5229\u7528\u6240\u8c13\u7684\u63a5\u89e6\u8ffd\u8e2a\u5e94\u7528\u7a0b\u5e8f\u6536\u96c6\u7684\u53ef\u7528\u6570\u636e\u6765\u5f00\u53d1\u4e00\u79cd\u7b97\u6cd5\uff0c\u5373 PPTO\uff0c\u6765\u8bc6\u522b\u90a3\u4e9b\u6700\u6709\u53ef\u80fd\u662f\u9633\u6027\u7684\u4e2a\u4f53\uff0c\u56e0\u6b64\uff0c\u5e94\u8be5\u8fdb\u884c\u6d4b\u8bd5\u3002\u867d\u7136\u8fc7\u53bb\u8fd9\u4e9b\u5206\u6790\u90fd\u662f\u901a\u8fc7\u96c6\u4e2d\u5f0f\u7b97\u6cd5\u8fdb\u884c\u7684\uff0c\u8981\u6c42\u6240\u6709\u7684\u5e94\u7528\u7a0b\u5e8f\u7528\u6237\u6570\u636e\u90fd\u6536\u96c6\u5728\u4e00\u4e2a\u5355\u4e00\u7684\u6570\u636e\u5e93\u4e2d\uff0c\u6211\u4eec\u7684\u534f\u8bae\u80fd\u591f\u5728\u8bbe\u5907\u7ea7\u522b\u4e0a\u5de5\u4f5c\uff0c\u901a\u8fc7\u5229\u7528\u533f\u540d\u4fe1\u606f\u4e0e\u5176\u4ed6\u8bbe\u5907\u7684\u901a\u4fe1\u3002<\/span><\/p>\n


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


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

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    \u4eba\u53e3\u8fc1\u79fb\u548c\u95f4\u65ad\u5c01\u9501<\/strong><\/span><\/p>\n

    \u5bf9 SARS-CoV-2\u4f20\u64ad\u7684\u5f71\u54cd<\/strong><\/span><\/p>\n

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

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

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

    Effect of population migration and punctuated lockdown on the spread of SARS-CoV-2<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.15010<\/span><\/p>\n

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

    Ravi Kiran,Madhumita Roy,Syed Abbas,A. Taraphder<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Once past the lockdown stage in many parts of the world, the important question now concerns the effects of relaxing the lockdown and finding the best ways to implement further lockdown(s), if required, to control the spread. With the relaxation of lockdown, people migrate to different cities and enhances the spread of the virus. In the present work we study a modified SEIRS model with population migration between two cities: a fraction of population in each city is allowed to migrate. Possible infection during transit is also incorporated – a fraction of exposed population becomes infected during transit. A punctuated lockdown is implemented to simulate a protocol of repeated lockdowns that limits the resurgence of infections. A damped oscillatory behavior is observed with multiple peaks over a period.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u4e16\u754c\u4e0a\u8bb8\u591a\u5730\u65b9\uff0c\u4e00\u65e6\u901a\u8fc7\u4e86\u5c01\u9501\u9636\u6bb5\uff0c\u73b0\u5728\u7684\u91cd\u8981\u95ee\u9898\u5c31\u662f\u653e\u677e\u5c01\u9501\u7684\u6548\u679c\uff0c\u4ee5\u53ca\u5982\u679c\u9700\u8981\u7684\u8bdd\uff0c\u627e\u5230\u5b9e\u65bd\u8fdb\u4e00\u6b65\u5c01\u9501\u7684\u6700\u4f73\u65b9\u6cd5\uff0c\u4ee5\u63a7\u5236\u4f20\u64ad\u3002\u968f\u7740\u5c01\u9501\u7684\u89e3\u9664\uff0c\u4eba\u4eec\u8fc1\u79fb\u5230\u4e0d\u540c\u7684\u57ce\u5e02\uff0c\u52a0\u901f\u4e86\u75c5\u6bd2\u7684\u4f20\u64ad\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u4e24\u4e2a\u57ce\u5e02\u4e4b\u95f4\u4eba\u53e3\u8fc1\u79fb\u7684\u4e00\u4e2a\u4fee\u6b63\u7684 SEIRS \u6a21\u578b: \u6bcf\u4e2a\u57ce\u5e02\u7684\u4e00\u90e8\u5206\u4eba\u53e3\u5141\u8bb8\u8fc1\u79fb\u3002\u5728\u8fd0\u8f93\u9014\u4e2d\u53ef\u80fd\u53d7\u5230\u611f\u67d3\u7684\u60c5\u51b5\u4e5f\u5305\u62ec\u5728\u5185\u2014\u2014\u4e00\u5c0f\u90e8\u5206\u53d7\u611f\u67d3\u7684\u4eba\u53e3\u5728\u8fd0\u8f93\u9014\u4e2d\u53d7\u5230\u611f\u67d3\u3002\u4e00\u4e2a\u95f4\u65ad\u7684\u5c01\u9501\u88ab\u5b9e\u73b0\u6765\u6a21\u62df\u4e00\u4e2a\u91cd\u590d\u5c01\u9501\u7684\u534f\u8bae\uff0c\u8fd9\u9650\u5236\u4e86\u4f20\u67d3\u75c5\u7684\u6b7b\u7070\u590d\u71c3\u3002\u5728\u4e00\u4e2a\u5468\u671f\u5185\u89c2\u5bdf\u5230\u5177\u6709\u591a\u4e2a\u5cf0\u503c\u7684\u963b\u5c3c\u632f\u8361\u884c\u4e3a\u3002<\/span><\/p>\n


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    \u9ad8\u9636\u7d2f\u79ef\u91cf\u7684\u5806\u79ef\u4fee\u6b63<\/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

    Pileup corrections on higher-order cumulants<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.15809<\/span><\/p>\n

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

    Toshihiro Nonaka,Masakiyo Kitazawa,ShinIchi Esumi<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We propose a method to remove the contributions of pileup events from higher-order cumulants and moments of event-by-event particle distributions. Assuming that the pileup events are given by the superposition of two independent single-collision events, we show that the true moments in each multiplicity bin can be obtained recursively from lower multiplicity events. In the correction procedure the necessary information are only the probabilities of pileup events. Other terms are extracted from the experimental data. We demonstrate that the true cumulants can be reconstructed successfully by this method in simple models. Systematics on trigger inefficiencies and correction parameters are discussed.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u63d0\u51fa\u4e86\u4e00\u79cd\u4ece\u9ad8\u9636\u7d2f\u79ef\u91cf\u548c\u9010\u4e8b\u7c92\u5b50\u5206\u5e03\u7684\u77e9\u4e2d\u5254\u9664\u5806\u79ef\u4e8b\u4ef6\u8d21\u732e\u7684\u65b9\u6cd5\u3002\u5047\u8bbe\u5806\u79ef\u4e8b\u4ef6\u662f\u7531\u4e24\u4e2a\u72ec\u7acb\u7684\u5355\u78b0\u649e\u4e8b\u4ef6\u53e0\u52a0\u800c\u5f97\u5230\u7684\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u6bcf\u4e2a\u591a\u91cd\u7cfb\u7edf\u4e2d\u7684\u771f\u77e9\u53ef\u4ee5\u4ece\u8f83\u4f4e\u7684\u591a\u91cd\u7cfb\u7edf\u4e2d\u9012\u5f52\u5730\u5f97\u5230\u3002\u5728\u6821\u6b63\u8fc7\u7a0b\u4e2d\uff0c\u6240\u9700\u8981\u7684\u4fe1\u606f\u53ea\u662f\u8fde\u7eed\u4e8b\u4ef6\u7684\u6982\u7387\u3002\u4ece\u5b9e\u9a8c\u6570\u636e\u4e2d\u63d0\u53d6\u4e86\u5176\u4ed6\u9879\u3002\u6211\u4eec\u8bc1\u660e\u4e86\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6210\u529f\u5730\u5728\u7b80\u5355\u7684\u6a21\u578b\u4e2d\u91cd\u5efa\u771f\u5b9e\u7684\u7d2f\u79ef\u91cf\u3002\u8ba8\u8bba\u4e86\u89e6\u53d1\u6548\u7387\u4f4e\u548c\u6821\u6b63\u53c2\u6570\u7684\u7cfb\u7edf\u6027\u3002<\/span><\/p>\n


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    \u4ece\u968f\u673a\u77e9\u9635\u7406\u8bba\u5230\u6cca\u677e\u6da8\u843d\u7684<\/strong><\/span><\/p>\n

    \u6d77\u6d0b\u8868\u9762\u6e29\u5ea6\u5168\u7403\u76f8\u5173\u77e9\u9635\u8c31<\/strong><\/span><\/p>\n

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

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

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

    Global correlation matrix spectra of the surfacetemperature of the Oceans from Random MatrixTheory to Poisson fluctuations<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.16001<\/span><\/p>\n

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

    Eucymara F. N. Santosa,Anderson L. R. Barbosa,Paulo J. Duarte-Neto<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this work we use the random matrix theory (RMT) to correctly describethe behavior of spectral statistical properties of the sea surface temperatureof oceans. This oceanographic variable plays an important role in theglobalclimate system. The data were obtained from National Oceanic and Atmo-spheric Administration (NOAA) and delimited for the period 1982 to 2016.The results show that oceanographic systems presented specific <\/span><\/nobr>\u03b2<\/span> values thatcan be used to classify each ocean according to its correlation behavior. Thenearest-neighbors spacing of correlation matrix for north, central and south ofthe three oceans get close to a RMT distribution. However, the regions delim-ited in the Antarctic pole exhibited the distribution of the nearest-neighborsspacing well described by the Poisson model, which shows astatistical changeof RMT to Poisson fluctuations.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u672c\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u968f\u673a\u77e9\u9635\u7406\u8bba(RMT)\u6765\u6b63\u786e\u5730\u63cf\u8ff0\u6d77\u6d0b\u8868\u9762\u6e29\u5ea6\u7684\u5149\u8c31\u7edf\u8ba1\u7279\u6027\u7684\u884c\u4e3a\u3002\u8fd9\u4e00\u6d77\u6d0b\u53d8\u91cf\u5728\u5168\u7403\u6c14\u5019\u7cfb\u7edf\u4e2d\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u6570\u636e\u6765\u81ea\u7f8e\u56fd\u56fd\u5bb6\u6d77\u6d0b\u548c\u5927\u6c14\u4e0e\u5927\u6c14\u7ba1\u7406\u5c40(NOAA)\uff0c\u5212\u754c\u65f6\u95f4\u4e3a1982\u5e74\u81f32016\u5e74\u3002\u7ed3\u679c\u8868\u660e\uff0c\u6d77\u6d0b\u5b66\u7cfb\u7edf\u7ed9\u51fa\u4e86\u7279\u5b9a\u7684\u6709\u6548\u503c\uff0c\u53ef\u6839\u636e\u6d77\u6d0b\u7684\u76f8\u5173\u884c\u4e3a\u5bf9\u5404\u4e2a\u6d77\u6d0b\u8fdb\u884c\u5206\u7c7b\u3002\u4e2d\u3001\u5317\u3001\u5357\u76f8\u5173\u77e9\u9635\u7684\u90bb\u57df\u95f4\u8ddd\u3002<\/span><\/p>\n


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


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    <\/strong>\u8fd1\u8ddd\u79bb\u611f\u67d3\u4f20\u64ad\u7684\u8499\u7279\u5361\u7f57\u6a21\u62df\u7814\u7a76 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901239\u7bc7<\/a>
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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    \u82f1\u56fd\u65b0\u51a0\u80ba\u708e\u7981\u95ed: \u5bf9\u7a7a\u6c14\u6c61\u67d3\u6709\u4ec0\u4e48\u5f71\u54cd | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7<\/strong><\/a>
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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901230\u7bc7<\/strong><\/a>
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    \u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c\u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc |\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901225\u7bc7<\/strong><\/a>
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    \u5b66\u4e60\u590d\u6742\u591a\u5c3a\u5ea6\u7cfb\u7edf\u7684\u6709\u6548\u52a8\u529b\u5b66 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901214\u7bc7<\/strong><\/a>
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    \u79bb\u6563\u56fe\u6a21\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7<\/strong><\/a>
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    \u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org
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    \u25c6 <\/span>\u25c6 <\/span>\u25c6<\/span><\/span><\/strong><\/strong><\/h1>\n<\/section>\n

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    \n

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

    \u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section><\/div>\n","protected":false},"excerpt":{"rendered":"

    \u672c\u6587\u7531\u673a\u5668\u7ffb\u8bd1\uff0c\u4ec5\u4f9b\u53c2\u8003\uff0c\u611f\u5174\u8da3\u8bf7\u67e5\u9605\u8bba\u6587\u539f\u6587 \u6838\u5fc3\u901f\u9012 GPT-GNN: \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u751f\u6210\u6027\u9884\u8bad\u7ec3\uff1b \u7f51\u7edc\u4e0a SIR \u4f20\u67d3\u75c5\u7684\u95ed\u73af\u63a8\u65ad\u3001\u9884\u6d4b\u4e0e\u63a7\u5236\u6846\u67b6\uff1b \u8d22\u5bcc\u5206\u5e03\u7684\u975e\u666e\u904d\u6027\u53cd\u6620\u5728\u8d22\u5bcc\u51dd\u805a\u4e34\u754c\u6027\u9644\u8fd1\uff1b \u57fa\u4e8e K-Means-LSTM \u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u786e\u8bca\u75c5\u4f8b\u6570\u9884\u6d4b\uff1b \u751f\u6b96\u6570 R_0 \u80fd…<\/p>\n","protected":false},"author":1,"featured_media":20254,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20256"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20256"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20256\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/20254"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20256"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=20256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}