{"id":20120,"date":"2020-06-25T19:46:34","date_gmt":"2020-06-25T11:46:34","guid":{"rendered":"https:\/\/swarma.org\/?p=20120"},"modified":"2020-06-25T19:46:34","modified_gmt":"2020-06-25T11:46:34","slug":"%e2%80%8b%e8%8b%b1%e5%9b%bd%e6%96%b0%e5%86%a0%e8%82%ba%e7%82%8e%e7%a6%81%e9%97%ad-%e5%af%b9%e7%a9%ba%e6%b0%94%e6%b1%a1%e6%9f%93%e6%9c%89%e4%bb%80%e4%b9%88%e5%bd%b1%e5%93%8d-%e7%bd%91%e7%bb%9c","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20120","title":{"rendered":"\u200b\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"},"content":{"rendered":"
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    \u82f1\u56fd\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7981\u95ed: <\/strong><\/span><\/p>\n

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

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

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

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

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


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

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

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

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

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

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

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

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


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

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


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    \u7f51\u7edc\u4e2d\u968f\u673a\u64ad\u79cd\u7b56\u7565\u7684\u8bc4\u4f30<\/strong><\/span><\/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

    Evaluating stochastic seeding strategies in networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/1809.09561<\/span><\/p>\n

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

    Alex Chin,Dean Eckles,Johan Ugander<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated “off-policy” using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an anti-bullying intervention.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5f53\u8bd5\u56fe\u6700\u5927\u5316\u4e00\u4e2a\u884c\u4e3a\u5728\u793e\u4f1a\u7f51\u7edc\u8fde\u63a5\u7684\u4eba\u7fa4\u4e2d\u7684\u91c7\u7528\u65f6\uff0c\u901a\u5e38\u9700\u8981\u5236\u5b9a\u7b56\u7565\uff0c\u5728\u7f51\u7edc\u4e2d\u7684\u4ec0\u4e48\u5730\u65b9\u79cd\u5b50\u884c\u4e3a\uff0c\u901a\u5e38\u5e26\u6709\u968f\u673a\u6027\u5143\u7d20\u3002\u7edf\u4e00\u5730\u968f\u673a\u9009\u62e9\u79cd\u5b50\u662f\u4e00\u79cd\u57fa\u672c\u4f46\u5f3a\u5236\u6027\u7684\u7b56\u7565\uff0c\u56e0\u4e3a\u5b83\u5728\u6574\u4e2a\u7f51\u7edc\u4e2d\u5e7f\u6cdb\u5730\u5206\u5e03\u79cd\u5b50\u3002\u4e00\u4e2a\u66f4\u590d\u6742\u7684\u968f\u673a\u7b56\u7565\uff0c\u4e00\u8df3\u5b9a\u4f4d\uff0c\u662f\u9009\u62e9\u968f\u673a\u4e2a\u4f53\u7684\u968f\u673a\u7f51\u7edc\u90bb\u5c45; \u8fd9\u5229\u7528\u4e86\u53cb\u8c0a\u6096\u8bba\u7684\u4e00\u4e2a\u7248\u672c\uff0c\u5373\u4e00\u4e2a\u968f\u673a\u4e2a\u4f53\u7684\u670b\u53cb\u88ab\u671f\u671b\u62e5\u6709\u6bd4\u4e00\u4e2a\u968f\u673a\u4e2a\u4f53\u66f4\u591a\u7684\u670b\u53cb\uff0c\u5e0c\u671b\u5728\u8054\u7cfb\u66f4\u591a\u7684\u4e2a\u4f53\u8eab\u4e0a\u79cd\u4e0b\u4e00\u79cd\u884c\u4e3a\u4f1a\u5bfc\u81f4\u66f4\u591a\u7684\u88ab\u6536\u517b\u3002\u5df2\u7ecf\u63d0\u51fa\u4e86\u8bb8\u591a\u79cd\u5b50\u6218\u7565\uff0c\u4f46\u7ecf\u9a8c\u6027\u8bc4\u4ef7\u8981\u6c42\u4e13\u95e8\u4e3a\u6b64\u76ee\u7684\u8fdb\u884c\u5927\u89c4\u6a21\u5b9e\u5730\u8bd5\u9a8c\uff0c\u5e76\u5f97\u51fa\u4e86\u76f8\u5bf9\u4e0d\u7cbe\u786e\u7684\u6218\u7565\u6bd4\u8f83\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u5728\u6b64\u7c7b\u5b9e\u9a8c\u4e2d\u66f4\u6709\u6548\u5730\u8bc4\u4f30\u968f\u673a\u64ad\u79cd\u7b56\u7565\uff0c\u5982\u4f55\u5229\u7528\u4e3a\u5176\u4ed6\u76ee\u7684\u8bbe\u8ba1\u7684\u5b9e\u9a8c\u4e2d\u4ea7\u751f\u7684\u73b0\u6709\u6570\u636e\u6765\u8bc4\u4f30\u201d\u975e\u653f\u7b56\u201d\u7b56\u7565\uff0c\u4ee5\u53ca\u5982\u4f55\u8bbe\u8ba1\u66f4\u6709\u6548\u7684\u5b9e\u9a8c\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u8003\u8651\u4e86\u968f\u673a\u64ad\u79cd\u7b56\u7565\u4e4b\u95f4\u7684\u5bf9\u6bd4\uff0c\u5e76\u5206\u6790\u4e86\u9002\u7528\u4e8e\u653f\u7b56\u8bc4\u4f30\u548c\u91cd\u8981\u6027\u62bd\u6837\u7684\u975e\u53c2\u6570\u4f30\u8ba1\u3002\u5728\u5b9e\u9645\u7f51\u7edc\u4e0a\u7684\u4eff\u771f\u7ed3\u679c\u8868\u660e\uff0c\u6240\u63d0\u51fa\u7684\u4f30\u8ba1\u5668\u548c\u8bbe\u8ba1\u65b9\u6cd5\u80fd\u591f\u5728\u63d0\u9ad8\u7cbe\u5ea6\u7684\u540c\u65f6\u4ea7\u751f\u6709\u6548\u7684\u63a8\u7406\u3002\u7136\u540e\uff0c\u6211\u4eec\u5c06\u6211\u4eec\u63d0\u51fa\u7684\u4f30\u8ba1\u5e94\u7528\u4e8e\u4e24\u4e2a\u5b9e\u5730\u5b9e\u9a8c\uff0c\u4e00\u4e2a\u662f\u6307\u5b9a\u5bb6\u5ead\u8fdb\u884c\u5f3a\u5316\u8425\u9500\u5e72\u9884\uff0c\u53e6\u4e00\u4e2a\u662f\u6307\u5b9a\u5b66\u751f\u8fdb\u884c\u53cd\u6b3a\u51cc\u5e72\u9884\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u8d44\u6e90\u5206\u914d\u7684\u8d85\u8fb9\u754c\u9884\u6d4b<\/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

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

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

    http:\/\/arxiv.org\/abs\/2006.11070<\/span><\/p>\n

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

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


    <\/p>\n

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

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

    \u6458\u8981\uff1a<\/span><\/strong>\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u7cfb\u7edf\u6d89\u53ca\u9ad8\u9636\u4ea4\u4e92\uff0c\u56e0\u6b64\u9700\u8981\u590d\u6742\u7684\u6a21\u578b\uff0c\u4f8b\u5982\u8d85\u56fe\u3002\u4f8b\u5982\uff0c\u4e00\u7bc7\u7814\u7a76\u6587\u7ae0\u53ef\u80fd\u6709\u591a\u4e2a\u5408\u4f5c\u4f5c\u8005\uff0c\u56e0\u6b64\uff0c\u5171\u540c\u4f5c\u8005\u7f51\u7edc\u6700\u597d\u8868\u793a\u4e3a\u4e00\u4e2a\u8d85\u56fe\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u91cd\u70b9\u5173\u6ce8\u8d85\u8fb9\u7f18\u9884\u6d4b\u95ee\u9898\u3002\u8fd9\u4e2a\u95ee\u9898\u5728\u591a\u4e2a\u9886\u57df\u90fd\u6709\u5de8\u5927\u7684\u5e94\u7528\uff0c\u4f8b\u5982\u9884\u6d4b\u793e\u4ea4\u7f51\u7edc\u4e2d\u7684\u65b0\u534f\u4f5c\uff0c\u53d1\u73b0\u4ee3\u8c22\u7f51\u7edc\u4e2d\u7684\u65b0\u5316\u5b66\u53cd\u5e94\u7b49\u3002\u5c3d\u7ba1\u975e\u5e38\u91cd\u8981\uff0c\u4f46\u662f\u8d85\u8fb9\u7f18\u9884\u6d4b\u95ee\u9898\u5e76\u672a\u5f97\u5230\u8db3\u591f\u7684\u91cd\u89c6\uff0c\u4e3b\u8981\u662f\u56e0\u4e3a\u5176\u56fa\u6709\u7684\u590d\u6742\u3002\u5728\u56fe\u4e2d<\/span><\/nobr>\u00f1 \u8282\u70b9\u7684\u6f5c\u5728\u8fb9\u6570\u4e3a <\/span><\/nobr>O\uff08\u00f12\uff09\uff0c\u800c\u5728\u8d85\u56fe\u4e2d\uff0c\u6f5c\u5728\u8d85\u8fb9\u7684\u6570\u91cf\u4e3a <\/span><\/nobr>O\uff082\u00f1\uff09\u3002\u4e3a\u4e86\u907f\u514d\u5728\u5982\u6b64\u5de8\u5927\u7684\u7a7a\u95f4\u4e2d\u641c\u7d22\uff0c\u5f53\u524d\u7684\u65b9\u6cd5\u901a\u8fc7\u4ee5\u4e0b\u4e24\u79cd\u65b9\u5f0f\u6765\u9650\u5236\u539f\u59cb\u95ee\u9898\u3002\u4e00\u7c7b\u7b97\u6cd5\u5047\u8bbe\u8d85\u56fe\u662f<\/span><\/nobr>\u0137-\u5236\u670d\u3002\u4f46\u662f\uff0c\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u7cfb\u7edf\u5e76\u4e0d\u4ec5\u9650\u4e8e\u4ea4\u4e92\u4f5c\u7528\u6d89\u53ca<\/span><\/nobr>\u0137\u7ec4\u4ef6\u3002\u56e0\u6b64\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u4e0d\u9002\u7528\u4e8e\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u3002\u7b2c\u4e8c\u7c7b\u7b97\u6cd5\u9700\u8981\u5019\u9009\u8d85\u8fb9\u7f18\u96c6\uff0c\u4ece\u4e2d\u9009\u62e9\u6f5c\u5728\u7684\u8d85\u8fb9\u7f18\u3002\u5728\u6ca1\u6709\u9886\u57df\u77e5\u8bc6\u7684\u60c5\u51b5\u4e0b\uff0c\u5019\u9009\u96c6\u53ef\u4ee5\u5177\u6709<\/span><\/nobr>O\uff082\u00f1\uff09 \u53ef\u80fd\u7684\u8d85\u8fb9\u7f18\uff0c\u8fd9\u4f7f\u8fd9\u4e2a\u95ee\u9898\u53d8\u5f97\u68d8\u624b\u3002\u6211\u4eec\u63d0\u51fa\u4e86HPRA-\u4f7f\u7528\u8d44\u6e90\u5206\u914d\u7684\u8d85\u8fb9\u7f18\u9884\u6d4b\uff0c\u8fd9\u662f\u540c\u7c7b\u7b97\u6cd5\u4e2d\u7684\u7b2c\u4e00\u4e2a\u7b97\u6cd5\uff0c\u5b83\u514b\u670d\u4e86\u8fd9\u4e9b\u95ee\u9898\u5e76\u5728\u4e0d\u4f7f\u7528\u4efb\u4f55\u5019\u9009\u8d85\u8fb9\u7f18\u96c6\u7684\u60c5\u51b5\u4e0b\u9884\u6d4b\u4e86\u4efb\u4f55\u57fa\u6570\u7684\u8d85\u8fb9\u7f18\u3002HPRA\u662f\u4e00\u79cd\u57fa\u4e8e\u76f8\u4f3c\u5ea6\u7684\u65b9\u6cd5\uff0c\u81f4\u529b\u4e8e\u8d44\u6e90\u5206\u914d\u8fc7\u7a0b\u7684\u539f\u7406\u3002\u9664\u4e86\u6062\u590d\u4e22\u5931\u7684\u8d85\u8fb9\u7f18\u5916\uff0c\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86HPRA\u53ef\u4ee5\u5728\u5404\u79cd\u8d85\u56fe\u4e2d\u9884\u6d4b\u672a\u6765\u7684\u8d85\u8fb9\u7f18\u3002<\/span><\/p>\n


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    \u5965\u5730\u5229\u793e\u4ea4\u5a92\u4f53\u57282010\u5e74<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u60c5\u7eea\u4eea\u8868\u677f<\/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

    Dashboard of sentiment in Austrian social media during COVID-19<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11158<\/span><\/p>\n

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

    Max Pellert,Jana Lasser,Hannah Metzler,David Garcia<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http:\/\/www.mpellert.at\/covid19_monitor_austria\/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e3a\u4e86\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4\u8fd1\u4e4e\u5b9e\u65f6\u5730\u8ddf\u8e2a\u5965\u5730\u5229\u4eba\u7fa4\u7684\u5728\u7ebf\u60c5\u7eea\u8868\u8fbe\uff0c\u6211\u4eec\u4f7f\u7528\u6765\u81ea3\u4e2a\u4e0d\u540c\u6570\u636e\u6e90\u7684\u6570\u5b57\u8ffd\u8e2a\u5efa\u7acb\u4e86\u4e00\u4e2a\u81ea\u6211\u66f4\u65b0\u7684\u60c5\u7eea\u52a8\u6001\u76d1\u6d4b\u5668\u3002\u8fd9\u4f7f\u51b3\u7b56\u8005\u548c\u611f\u5174\u8da3\u7684\u516c\u4f17\u80fd\u591f\u8bc4\u4f30\u5404\u79cd\u95ee\u9898\uff0c\u4f8b\u5982\u5bf9\u5728\u5927\u6d41\u884c\u671f\u95f4\u91c7\u53d6\u7684\u5e94\u5bf9\u63aa\u65bd\u7684\u6001\u5ea6\u4ee5\u53ca\u53ef\u80fd\u5728\u65e9\u671f\u51fa\u73b0\u7684(\u5fc3\u7406)\u5065\u5eb7\u5371\u673a\u3002\u6211\u4eec\u4f7f\u7528 web \u6293\u53d6\u548c API \u8bbf\u95ee\u4ece\u65b0\u95fb\u5e73\u53f0\u68c0\u7d22\u6570\u636ehttp:\/\/derstandard.at\/\u3002\u5b66\u751f\u7684 Twitter \u548c achat \u5e73\u53f0\u3002\u6211\u4eec\u8bb0\u5f55\u6211\u4eec\u5de5\u4f5c\u6d41\u7a0b\u7684\u6280\u672f\u7ec6\u8282\uff0c\u4ee5\u4fbf\u4e3a\u5176\u4ed6\u6709\u5174\u8da3\u5728\u4e0d\u540c\u73af\u5883\u4e0b\u6784\u5efa\u7c7b\u4f3c\u5de5\u5177\u7684\u7814\u7a76\u4eba\u5458\u63d0\u4f9b\u8d44\u6599\u3002\u81ea\u52a8\u7684\u6587\u672c\u5206\u6790\u5141\u8bb8\u6211\u4eec\u7a81\u51fa\u663e\u793a\u8bed\u8a00\u4f7f\u7528\u7684\u53d8\u5316\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4e00\u4e2a\u4e2d\u7acb\u7684\u57fa\u51c6\u3002\u6211\u4eec\u7528\u7279\u6b8a\u7684\u8bcd\u8bed\u4e91\u6765\u5f62\u8c61\u5316\u6574\u4f53\u5dee\u5f02\u3002\u7eb5\u5411\u6765\u770b\uff0c\u6211\u4eec\u7684\u65f6\u95f4\u5e8f\u5217\u663e\u793a\u4e86\u7126\u8651\u7684\u5c16\u5cf0\uff0c\u8fd9\u53ef\u80fd\u4e0e\u51e0\u4e2a\u4e8b\u4ef6\u548c\u5a92\u4f53\u62a5\u9053\u6709\u5173\u3002\u6b64\u5916\uff0c\u6211\u4eec\u53d1\u73b0\u6124\u6012\u60c5\u7eea\u660e\u663e\u51cf\u5c11\u3002\u8fd9\u79cd\u53d8\u5316\u6301\u7eed\u4e86\u76f8\u5f53\u957f\u7684\u4e00\u6bb5\u65f6\u95f4(\u957f\u8fbe12\u5468)\u3002\u6211\u4eec\u8ba8\u8bba\u8fd9\u4e9b\u4ee5\u53ca\u66f4\u591a\u7684\u6a21\u5f0f\uff0c\u5e76\u5c06\u5b83\u4eec\u4e0e\u96c6\u4f53\u60c5\u611f\u7684\u51fa\u73b0\u8054\u7cfb\u8d77\u6765\u3002\u5c55\u793a\u6211\u4eec\u6570\u636e\u7684\u4e92\u52a8\u4eea\u8868\u677f\u53ef\u4ee5\u5728http:\/\/www.mpellert.at\/covid19_monitor_austria\/\u3002\u6211\u4eec\u7684\u5de5\u4f5c\u5df2\u7ecf\u5438\u5f15\u4e86\u5a92\u4f53\u7684\u6ce8\u610f\uff0c\u5e76\u4e14\u662f\u5965\u5730\u5229\u56fd\u5bb6\u56fe\u4e66\u9986\u6536\u96c6\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u56fe\u4e66\u9986\u8d44\u6e90\u7f51\u7edc\u6863\u6848\u7684\u4e00\u90e8\u5206\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    \u5c40\u90e8\u5149\u8c31\u56fe\u8fc7\u6ee4\u6846\u67b6: <\/strong><\/span><\/p>\n

    \u7edf\u4e00\u6846\u67b6\uff0c\u8bbe\u8ba1\u8003\u8651\u7684\u8c03\u67e5\u548c\u6570\u503c\u6bd4\u8f83<\/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

    Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11220<\/span><\/p>\n

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

    David I Shuman<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Representing data residing on a graph as a linear combination of building block signals can enable efficient and insightful visual or statistical analysis of the data, and such representations prove useful as regularizers in signal processing and machine learning tasks. Designing such collections of building block signals — or more formally, dictionaries of atoms — that specifically account for the underlying graph structure as well as any available representative training signals has been an active area of research over the last decade. In this article, we survey a particular class of dictionaries called localized spectral graph filter frames, whose atoms are created by localizing spectral patterns to different regions of the graph. After showing how this class encompasses a variety of approaches from spectral graph wavelets to graph filter banks, we focus on the two main questions of how to design the spectral filters and how to select the center vertices to which the patterns are localized. Throughout, we emphasize computationally efficient methods that ensure the resulting transforms and their inverses can be applied to data residing on large, sparse graphs. We demonstrate how this class of transform methods can be used in signal processing tasks such as denoising and non-linear approximation, and provide code for readers to experiment with these methods in new application domains.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5c06\u56fe\u8868\u4e0a\u7684\u6570\u636e\u8868\u793a\u4e3a\u4e00\u4e2a\u7ebf\u6027\u7ec4\u5408\u7684\u79ef\u6728\u4fe1\u53f7\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6709\u6548\u548c\u6df1\u523b\u7684\u89c6\u89c9\u6216\u7edf\u8ba1\u5206\u6790\uff0c\u8fd9\u79cd\u8868\u793a\u65b9\u5f0f\u5728\u4fe1\u53f7\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u4e2d\u88ab\u8bc1\u660e\u662f\u6709\u7528\u7684\u3002\u8bbe\u8ba1\u8fd9\u6837\u7684\u6784\u5efa\u5757\u4fe1\u53f7\u96c6\u5408—- \u6216\u8005\u66f4\u6b63\u5f0f\u5730\u8bf4\uff0c\u539f\u5b50\u8bcd\u5178—- \u7279\u522b\u8003\u8651\u5230\u5e95\u5c42\u56fe\u5f62\u7ed3\u6784\u4ee5\u53ca\u4efb\u4f55\u53ef\u7528\u7684\u4ee3\u8868\u6027\u8bad\u7ec3\u4fe1\u53f7\uff0c\u5df2\u7ecf\u6210\u4e3a\u8fc7\u53bb\u5341\u5e74\u7814\u7a76\u7684\u4e00\u4e2a\u6d3b\u8dc3\u9886\u57df\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8c03\u67e5\u4e86\u4e00\u7c7b\u7279\u6b8a\u7684\u8bcd\u5178\u79f0\u4e3a\u5c40\u90e8\u5149\u8c31\u56fe\u8fc7\u6ee4\u6846\u67b6\uff0c\u5176\u539f\u5b50\u662f\u901a\u8fc7\u5c40\u90e8\u5316\u5149\u8c31\u6a21\u5f0f\u5230\u56fe\u7684\u4e0d\u540c\u533a\u57df\u6765\u521b\u5efa\u7684\u3002\u5728\u5c55\u793a\u4e86\u8fd9\u4e2a\u7c7b\u5982\u4f55\u5305\u542b\u4ece\u8c31\u56fe\u5c0f\u6ce2\u5230\u56fe\u5f62\u6ee4\u6ce2\u5668\u7ec4\u7684\u5404\u79cd\u65b9\u6cd5\u4e4b\u540e\uff0c\u6211\u4eec\u7740\u91cd\u8ba8\u8bba\u4e86\u4e24\u4e2a\u4e3b\u8981\u95ee\u9898: \u5982\u4f55\u8bbe\u8ba1\u8c31\u6ee4\u6ce2\u5668\u4ee5\u53ca\u5982\u4f55\u9009\u62e9\u56fe\u5f62\u88ab\u5b9a\u4f4d\u5230\u7684\u4e2d\u5fc3\u70b9\u3002\u81ea\u59cb\u81f3\u7ec8\uff0c\u6211\u4eec\u5f3a\u8c03\u8ba1\u7b97\u6548\u7387\u7684\u65b9\u6cd5\uff0c\u4ee5\u786e\u4fdd\u751f\u6210\u7684\u53d8\u6362\u548c\u4ed6\u4eec\u7684\u53cd\u6f14\u53ef\u4ee5\u5e94\u7528\u4e8e\u5927\u578b\uff0c\u7a00\u758f\u56fe\u7684\u6570\u636e\u9a7b\u7559\u3002\u6211\u4eec\u6f14\u793a\u4e86\u8fd9\u7c7b\u53d8\u6362\u65b9\u6cd5\u5982\u4f55\u7528\u4e8e\u4fe1\u53f7\u5904\u7406\u4efb\u52a1\uff0c\u5982\u53bb\u566a\u548c\u975e\u7ebf\u6027\u903c\u8fd1\uff0c\u5e76\u4e3a\u8bfb\u8005\u63d0\u4f9b\u4ee3\u7801\u5b9e\u9a8c\u8fd9\u4e9b\u65b9\u6cd5\u5728\u65b0\u7684\u5e94\u7528\u9886\u57df\u3002<\/span><\/p>\n


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

    \u5e7c\u7a1a\u6570\u5b57\u75d5\u8ff9\u4e0e\u8c03\u67e5\u6570\u636e\u7684\u5bf9\u5e94<\/strong><\/span><\/p>\n

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

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

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

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

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

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

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


    <\/p>\n

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

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


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

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

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

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

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

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

    http:\/\/arxiv.org\/abs\/2006.11211<\/span><\/p>\n

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

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


    <\/p>\n

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

    \u6458\u8981\uff1a<\/span><\/strong>\u6700\u8fd1\u5728\u533f\u540d\u6d88\u606f\u4f20\u9012\u5e73\u53f0\u7684\u63a8\u52a8\u4e0b\uff0c\u5f15\u5165\u4e86\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\uff0c\u53ef\u4ee5\u6a21\u7cca\u8c23\u8a00\u7684\u6765\u6e90: \u4e00\u4e2a\u201c\u5feb\u7167\u5bf9\u624b\u201d\u8bbf\u95ee\u201c\u88ab\u611f\u67d3\u201d\u8282\u70b9\u7684\u5b50\u56fe\uff0c\u53ea\u80fd\u505a\u5230\u968f\u673a\u731c\u6d4b\u6e90\u8282\u70b9\u7684\u5b9e\u4f53\u3002\u5982\u679c\u5bf9\u624b\u53ef\u4ee5\u8bbf\u95ee\u591a\u4e2a\u72ec\u7acb\u7684\u5feb\u7167\u4f1a\u53d1\u751f\u4ec0\u4e48\uff1f\u5f53\u5e95\u56fe\u662f\u65e0\u7a77\u5927\u65f6\uff0c\u6211\u4eec\u7814\u7a76\u8fd9\u4e2a\u95ee\u9898d- \u666e\u901a\u6811\u3002\u6211\u4eec\u8bc1\u660e\u4e86(1)\u5728\u4e24\u4e2a\u72ec\u7acb\u7684\u5feb\u7167\u60c5\u51b5\u4e0b\uff0c\u4ecd\u7136\u53ef\u80fd\u5b58\u5728\u4e00\u79cd\u5f31\u5f62\u5f0f\u7684\u6e90\u6df7\u6dc6\uff0c\u4f46\u662f(2)\u5df2\u7ecf\u6709\u4e86\u4e09\u4e2a\u89c2\u5bdf\u503c\uff0c\u6709\u4e00\u4e2a\u7b80\u5355\u7684\u7b97\u6cd5\uff0c\u4e0d\u7ba1\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\u5982\u4f55\uff0c\u90fd\u53ef\u4ee5\u627e\u5230\u5177\u6709\u5e38\u6570\u6982\u7387\u7684\u8c23\u8a00\u6e90\u3002\u6211\u4eec\u8fd8\u523b\u753b\u4e86\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\u5728\u672c\u5730\u6269\u6563\u548c\u6e90\u6a21\u7cca\u4e4b\u95f4\u7684\u6743\u8861(\u5728\u5355\u4e2a\u5feb\u7167\u4e0b)\u3002\u8fd9\u4e9b\u7ed3\u679c\u63d0\u51fa\u4e86\u5173\u4e8e\u5728\u793e\u4f1a\u7f51\u7edc\u4e2d\u4f20\u64ad\u4fe1\u606f\u65f6\u533f\u540d\u4fdd\u8bc1\u7684\u7a33\u5065\u6027\u7684\u95ee\u9898\u3002<\/span><\/p>\n


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

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    \u653e\u5927\u56fe\u4e2d\u7684\u6df7\u6c8c\u6da8\u843d<\/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

    Chaotic fluctuations in graphs with amplification<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11015<\/span><\/p>\n

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

    Stefano Lepri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We consider a model for chaotic diffusion with amplification on graphs associated with piecewise-linear maps of the interval. We investigate the possibility of having power-law tails in the invariant measure by approximate solution of the Perron-Frobenius equation and discuss the connection with the generalized Lyapunov exponents<\/span><\/nobr>L(q). We then consider the case of open maps where trajectories escape and demonstrate that stationary power-law distributions occur when <\/span><\/nobr>L(q)=r, with <\/span><\/nobr>r being the escape rate. The proposed system is a toy model for coupled active chaotic cavities or lasing networks and allows to elucidate in a simple mathematical framework the conditions for observing L\u00e9vy statistical regimes and chaotic intermittency in such systems.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8003\u8651\u4e86\u4e00\u4e2a\u5e26\u56fe\u5f62\u653e\u5927\u7684\u6df7\u6c8c\u6269\u6563\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u4e0e\u533a\u95f4\u7684\u5206\u6bb5\u7ebf\u6027\u6620\u5c04\u6709\u5173\u3002\u6211\u4eec\u901a\u8fc7 Perron-Frobenius \u65b9\u7a0b\u7684\u8fd1\u4f3c\u89e3\u6765\u7814\u7a76\u4e0d\u53d8\u6d4b\u5ea6\u4e2d\u6709\u5e42\u5f8b\u5c3e\u7684\u53ef\u80fd\u6027\uff0c\u5e76\u8ba8\u8bba\u4e86\u5b83\u4e0e\u5e7f\u4e49 Lyapunov \u6307\u6570\u7684\u5173\u7cfbL(q)\uff0c\u7136\u540e\u6211\u4eec\u8003\u8651\u5f00\u653e\u6620\u5c04\u7684\u60c5\u51b5\uff0c\u5176\u4e2d\u8f68\u8ff9\u9003\u9038\uff0c\u5e76\u8bc1\u660e\u7a33\u5b9a\u7684\u5e42\u5f8b\u5206\u5e03\u53d1\u751f\u65f6L(q)=r\uff0c\u5c31\u662f\u9003\u8dd1\u7387\u3002\u63d0\u51fa\u7684\u7cfb\u7edf\u662f\u8026\u5408\u6709\u6e90\u6df7\u6c8c\u8154\u6216\u6fc0\u5149\u7f51\u7edc\u7684\u4e00\u4e2a\u73a9\u5177\u6a21\u578b\uff0c\u5e76\u5141\u8bb8\u5728\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u5b66\u6846\u67b6\u4e2d\u9610\u660e\u89c2\u5bdf L\u00e9vy \u7edf\u8ba1\u533a\u57df\u548c\u6df7\u6c8c\u95f4\u6b47\u6027\u7684\u6761\u4ef6\u3002<\/span><\/p>\n


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

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

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

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

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

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

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

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

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

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

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


    <\/p>\n

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

    \u6458\u8981\uff1a<\/span><\/strong>\u5fc3\u7406\u7269\u7406\u5b66\u8bd5\u56fe\u5c06\u7269\u7406\u8f93\u5165\u91cf\u4e0e\u5fc3\u7406\u6216\u795e\u7ecf\u76f8\u5173\u8054\u3002\u7528\u5fae\u89c2\u6a21\u578b\u6765\u89e3\u91ca\u5b8f\u89c2\u7684\u5fc3\u7406\u7269\u7406\u5b9a\u5f8b\uff0c\u5728\u7edf\u8ba1\u7269\u7406\u5b66\u7684\u610f\u4e49\u4e0a\uff0c\u662f\u4e00\u4e2a\u51e0\u4e4e\u672a\u88ab\u63a2\u7d22\u7684\u9886\u57df\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u68c0\u67e5\u4e00\u4e2a\u611f\u89c9\u4e0a\u76ae\u7ec4\u6210\u7684\u4e24\u4e2a\u8fde\u63a5\u7684\u65b9\u683c\u968f\u673a\u79ef\u5206\u548c\u706b\u7ec6\u80de\u3002\u5bf9\u4e8e\u4e00\u4e2a\u6b63\u65b9\u5f62\u70b9\u9635\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u4e00\u4e2a\u65af\u8482\u6587\u65af\u5b9a\u5f8b<\/span><\/nobr>\u03c1\u221dhm \u53f2\u8482\u6587\u65af\u7684\u6307\u6570m=0.254\u548c\u76f8\u4f3c\u7684\u9971\u548c\u5ea6\uff0c\u5176\u4e2d<\/span><\/nobr>\u03c1 \u662f\u795e\u7ecf\u5143\u7f51\u7edc\u6d3b\u52a8\uff0c\u6211\u4eec\u5c06\u65af\u8482\u6587\u65af\u7684\u5e42\u5f8b\u6307\u6570\u4e0e\u573a\u4e34\u754c\u6307\u6570\u76f8\u5173\u8054m=1\/\u03b4h=\u03b2\/\u03c3\uff0c\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86\u8be5\u7cfb\u7edf\u5c5e\u4e8e\u5b9a\u5411 Percolation (DP)\u666e\u9002\u6027\u7c7b(\u6216\u8005\u53ef\u80fd\u5c5e\u4e8e compact-DP \u7c7b)\u3002\u901a\u8fc7\u53e0\u52a0\u4e24\u5c42\u6b63\u65b9\u5f62\u6676\u683c\uff0c\u4ee5\u53ca\u7b2c\u4e00\u5c42\u548c\u7b2c\u4e8c\u5c42\u4e4b\u95f4\u8fde\u901a\u6027\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u8f93\u51fa\u5c42\u83b7\u5f97<\/span><\/nobr>\u03c12\u221dhm2,<\/span><\/nobr>m2=0.08\u2248m2,\u76f8\u5f53\u4e8e\u4e00\u4e2a\u5de8\u5927\u7684\u52a8\u6001\u8303\u56f4\u3002\u8fd9\u79cd\u52a8\u6001\u8303\u56f4\u7684\u589e\u5f3a\u53ea\u53d1\u751f\u5728\u5c42\u63a5\u8fd1\u5176\u4e34\u754c\u70b9\u7684\u65f6\u5019\u3002<\/span><\/p>\n


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    \u8fd1\u4e0d\u53ef\u538b\u7f29\u6027\u89d2\u819c\u5f39\u6027\u6a2a\u89c2\u5404\u5411\u540c\u6027:<\/strong><\/span><\/p>\n

    \u58f0\u5b66\u5fae\u5207\u524a OCE \u6a21\u578b\u4e0e\u5b9e\u9a8c<\/strong><\/span><\/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

    Nearly-incompressible transverse isotropy (NITI) of cornea elasticity: model and experiments with acoustic micro-tapping OCE<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10893<\/span><\/p>\n

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

    John J Pitre Jr,Mitchell A Kirby,David S Li,Tueng T Shen,Ruikang K Wang,Matthew O’Donnell,Ivan Pelivanov<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The cornea provides the largest refractive power for the human visual system. Its stiffness, along with intraocular pressure (IOP), are linked to several pathologies, including keratoconus and glaucoma. Although mechanical tests can quantify corneal elasticity ex vivo, they cannot be used clinically. Optical coherence elastography (OCE), which launches and tracks shear waves to estimate stiffness, provides an attractive non-contact probe of corneal elasticity. To date, however, OCE studies report corneal moduli around tens of kPa, orders-of-magnitude less than those (few MPa) obtained by tensile\/inflation testing. This large discrepancy impedes OCE’s clinical adoption. Based on corneal microstructure, we introduce and fully characterize a nearly-incompressible transversally isotropic (NITI) model depicting corneal biomechanics. We show that the cornea must be described by two shear moduli, contrary to current single-modulus models, decoupling tensile and shear responses. We measure both as a function of IOP in ex vivo porcine cornea, obtaining values consistent with both tensile and shear tests. At pressures above 30 mmHg, the model begins to fail, consistent with non-linear changes in cornea at high IOP.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u89d2\u819c\u4e3a\u4eba\u7c7b\u7684\u89c6\u89c9\u7cfb\u7edf\u63d0\u4f9b\u6700\u5927\u7684\u5c48\u5149\u529b\u3002\u5b83\u7684\u50f5\u786c\uff0c\u4f34\u968f\u7740\u773c\u538b\uff0c\u4e0e\u51e0\u79cd\u75be\u75c5\u6709\u5173\uff0c\u5305\u62ec\u5706\u9525\u89d2\u819c\u548c\u9752\u5149\u773c\u3002\u673a\u68b0\u6027\u89d2\u819c\u5f39\u6027\u8bd5\u9a8c\u867d\u7136\u53ef\u4ee5\u91cf\u5316\u4f53\u5916\u89d2\u819c\u5f39\u6027\uff0c\u4f46\u4e0d\u9002\u7528\u4e8e\u4e34\u5e8a\u3002\u5149\u5b66\u76f8\u5e72\u5f39\u6027\u6210\u50cf(OCE)\u53d1\u5c04\u548c\u8ddf\u8e2a\u526a\u5207\u6ce2\uff0c\u4ee5\u4f30\u8ba1\u521a\u5ea6\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6709\u5438\u5f15\u529b\u7684\u975e\u63a5\u89e6\u63a2\u5934\u7684\u89d2\u819c\u5f39\u6027\u3002\u7136\u800c\uff0c\u5230\u76ee\u524d\u4e3a\u6b62\uff0cOCE \u7814\u7a76\u62a5\u544a\u7684\u89d2\u819c\u6a21\u91cf\u5927\u7ea6\u5728\u51e0\u5341\u5343\u5e15\uff0c\u6570\u91cf\u7ea7\u4f4e\u4e8e\u90a3\u4e9b(\u51e0\u5146\u5e15)\u901a\u8fc7\u62c9\u4f38 \/ \u81a8\u80c0\u6d4b\u8bd5\u83b7\u5f97\u7684\u3002\u8fd9\u79cd\u5de8\u5927\u7684\u5dee\u5f02\u963b\u788d\u4e86 OCE \u7684\u4e34\u5e8a\u5e94\u7528\u3002\u57fa\u4e8e\u89d2\u819c\u7684\u5fae\u89c2\u7ed3\u6784\uff0c\u6211\u4eec\u4ecb\u7ecd\u5e76\u5b8c\u6574\u63cf\u8ff0\u4e86\u4e00\u4e2a\u63cf\u8ff0\u89d2\u819c\u751f\u7269\u529b\u5b66\u7684\u8fd1\u4e0d\u53ef\u538b\u7f29\u6a2a\u89c2\u5404\u5411\u540c\u6027(NITI)\u6a21\u578b\u3002\u6211\u4eec\u6307\u51fa\uff0c\u89d2\u819c\u5fc5\u987b\u7528\u4e24\u4e2a\u526a\u5207\u6a21\u91cf\u6765\u63cf\u8ff0\uff0c\u8fd9\u4e0e\u73b0\u6709\u7684\u5355\u6a21\u91cf\u6a21\u578b\u4e0d\u540c\uff0c\u5b83\u89e3\u8026\u4e86\u89d2\u819c\u7684\u62c9\u4f38\u548c\u526a\u5207\u54cd\u5e94\u3002\u6211\u4eec\u5728\u79bb\u4f53\u732a\u89d2\u819c\u4e0a\u6d4b\u91cf\u773c\u538b\u7684\u51fd\u6570\uff0c\u5f97\u5230\u4e0e\u62c9\u4f38\u548c\u526a\u5207\u8bd5\u9a8c\u4e00\u81f4\u7684\u6570\u503c\u3002\u538b\u529b\u8d85\u8fc730\u6beb\u7c73\u6c5e\u67f1\u65f6\uff0c\u6a21\u578b\u5f00\u59cb\u5931\u6548\uff0c\u8fd9\u4e0e\u9ad8\u773c\u538b\u65f6\u89d2\u819c\u7684\u975e\u7ebf\u6027\u53d8\u5316\u4e00\u81f4\u3002<\/span><\/p>\n


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    \n
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    \u6ce2\u524d\u6210\u5f62\u591a\u6a21\u5149\u7ea4<\/strong><\/span><\/p>\n

    \u5355\u6b21\u6fc0\u53d1\u5149\u58f0\u8367\u5149\u5fae\u5185\u7aa5\u955c<\/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

    Single-shot hybrid photoacoustic-fluorescent microendoscopy through a multi-mode fiber with wavefront shaping<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.10856<\/span><\/p>\n

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

    Sylvain Mezil,Antonio M. Caravaca-Aguirre,Edward Z. Zhang,Philippe Moreau,Ir\u00e8ne Wang,Paul C. Beard,Emmanuel Bossy<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We present a minimally-invasive endoscope based on a multimode fiber that combines photoacoustic and fluorescence sensing. From the measurement of a transmission matrix during a prior calibration step, a focused spot is produced and raster-scanned over a sample at the distal tip of the fiber by use of a fast spatial light modulator. An ultra-sensitive fiber-optic ultrasound sensor for photoacoustic detection placed next to the fiber is combined with a photodetector to obtain both fluorescence and photoacoustic images with a distal imaging tip no larger than 250um. The high signal-to-noise ratio provided by wavefront shaping based focusing and the ultra-sensitive ultrasound sensor enables imaging with a single laser shot per pixel, demonstrating fast two-dimensional hybrid imaging of red blood cells and fluorescent beads.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u5149\u58f0\u548c\u8367\u5149\u4f20\u611f\u76f8\u7ed3\u5408\u7684\u591a\u6a21\u5149\u7ea4\u7684\u5fae\u521b\u5185\u7aa5\u955c\u3002\u5728\u5148\u524d\u7684\u6821\u51c6\u6b65\u9aa4\u4e2d\uff0c\u901a\u8fc7\u6d4b\u91cf\u4f20\u8f93\u77e9\u9635\uff0c\u4ea7\u751f\u4e00\u4e2a\u805a\u7126\u70b9\uff0c\u5e76\u4f7f\u7528\u5feb\u901f\u7a7a\u95f4\u5149\u8c03\u5236\u5668\u626b\u63cf\u5149\u7ea4\u8fdc\u7aef\u7684\u6837\u54c1\u3002\u5c06\u653e\u7f6e\u5728\u5149\u7ea4\u65c1\u7684\u7528\u4e8e\u5149\u58f0\u63a2\u6d4b\u7684\u8d85\u7075\u654f\u5149\u7ea4\u8d85\u58f0\u4f20\u611f\u5668\u4e0e\u5149\u7535\u63a2\u6d4b\u5668\u76f8\u7ed3\u5408\uff0c\u83b7\u5f97\u8fdc\u7aef\u6210\u50cf\u5c16\u7aef\u4e0d\u5927\u4e8e250\u5fae\u7c73\u7684\u8367\u5149\u548c\u5149\u58f0\u56fe\u50cf\u3002\u57fa\u4e8e\u6ce2\u524d\u6210\u5f62\u7684\u805a\u7126\u548c\u8d85\u7075\u654f\u8d85\u58f0\u6ce2\u4f20\u611f\u5668\u63d0\u4f9b\u7684\u9ad8\u4fe1\u566a\u6bd4\uff0c\u4f7f\u6bcf\u50cf\u7d20\u4e00\u4e2a\u6fc0\u5149\u53d1\u5c04\u6210\u50cf\u6210\u4e3a\u53ef\u80fd\uff0c\u6f14\u793a\u4e86\u7ea2\u7ec6\u80de\u548c\u8367\u5149\u73e0\u7684\u5feb\u901f\u4e8c\u7ef4\u6df7\u5408\u6210\u50cf\u3002<\/span><\/p>\n


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

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    \u4ece\u5927\u5206\u5b50\u4e2d\u63a2\u7d22\u914d\u4f53\u89e3\u79bb\u7684\u5de5\u4f5c\u6d41\u7a0b:<\/strong><\/span><\/p>\n

    \u6709\u6548\u7684\u968f\u673a\u52a0\u901f\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df<\/strong><\/span><\/p>\n

    \u548c\u914d\u4f53\u8f68\u8ff9\u7684\u76f8\u4e92\u4f5c\u7528\u6307\u7eb9\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

    A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11066<\/span><\/p>\n

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

    Daria B. Kokha,Bernd Doser,Stefan Richter,Fabian Ormersbach,Xingyi Cheng,Rebecca C. Wade<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The dissociation of ligands from proteins and other biomacromolecules occurs over a wide range of timescales. For most pharmaceutically relevant inhibitors, these timescales are far beyond those that are accessible by conventional molecular dynamics (MD) simulation. Consequently, to explore ligand egress mechanisms and compute dissociation rates, it is necessary to enhance the sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site that does not require the user to choose a ligand egress reaction coordinate. It thus enables the unbiased exploration of ligand egress routes. Furthermore, the tauRAMD procedure can be used to compute the relative residence times of ligands. When combined with a machine-learning analysis of protein-ligand interaction fingerprints (IFP), molecular features that affect ligand unbinding kinetics can be identified. Here, we describe the implementation of RAMD in GROMACS 2020, which provides significantly improved computational performance, with scaling to large molecular systems. For the automated analysis of RAMD results, we developed MD-IFP, a set of tools for the generation of IFPs along unbinding trajectories and for their use in the exploration of ligand dynamics. We demonstrate that the analysis of ligand dissociation trajectories by mapping them onto the IFP space enables the characterization of ligand dissociation routes and metastable states. The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of tauRAMD in drug design.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u914d\u4f53\u4ece\u86cb\u767d\u8d28\u548c\u5176\u4ed6\u751f\u7269\u5927\u5206\u5b50\u4e2d\u5206\u79bb\u51fa\u6765\u7684\u65f6\u95f4\u8de8\u5ea6\u5f88\u5927\u3002\u5bf9\u4e8e\u5927\u591a\u6570\u4e0e\u836f\u7269\u76f8\u5173\u7684\u6291\u5236\u5242\uff0c\u8fd9\u4e9b\u65f6\u95f4\u5c3a\u5ea6\u8fdc\u8fdc\u8d85\u8fc7\u5e38\u89c4\u7684\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u6240\u80fd\u8fbe\u5230\u7684\u65f6\u95f4\u5c3a\u5ea6\u3002\u56e0\u6b64\uff0c\u4e3a\u4e86\u63a2\u7d22\u914d\u4f53\u79bb\u5f00\u673a\u5236\u548c\u8ba1\u7b97\u914d\u4f53\u79bb\u89e3\u7387\uff0c\u6709\u5fc5\u8981\u52a0\u5f3a\u914d\u4f53\u975e\u7ed3\u5408\u53d6\u6837\u3002\u968f\u673a\u52a0\u901f MD (RAMD)\u662f\u4e00\u79cd\u7b80\u5355\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u589e\u5f3a\u914d\u4f53\u4ece\u5927\u5206\u5b50\u7ed3\u5408\u4f4d\u70b9\u7684\u8fdb\u51fa\uff0c\u800c\u4e0d\u9700\u8981\u7528\u6237\u9009\u62e9\u914d\u4f53\u8fdb\u51fa\u53cd\u5e94\u5750\u6807\u3002\u56e0\u6b64\uff0c\u5b83\u80fd\u591f\u4e0d\u504f\u4e0d\u501a\u5730\u63a2\u7d22\u914d\u4f53\u7684\u51fa\u53e3\u8def\u7ebf\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u7528 tauRAMD \u7a0b\u5e8f\u8ba1\u7b97\u914d\u4f53\u7684\u76f8\u5bf9\u505c\u7559\u65f6\u95f4\u3002\u5f53\u7ed3\u5408\u86cb\u767d\u8d28-\u914d\u4f53\u76f8\u4e92\u4f5c\u7528\u6307\u7eb9(IFP)\u7684\u673a\u5668\u5b66\u4e60\u5206\u6790\uff0c\u5206\u5b50\u7279\u5f81\u5f71\u54cd\u914d\u4f53\u975e\u7ed3\u5408\u52a8\u529b\u5b66\u53ef\u4ee5\u88ab\u8bc6\u522b\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u63cf\u8ff0\u4e86\u5728 GROMACS 2020\u4e2d RAMD \u7684\u5b9e\u73b0\uff0c\u5b83\u63d0\u4f9b\u4e86\u663e\u8457\u6539\u8fdb\u7684\u8ba1\u7b97\u6027\u80fd\uff0c\u5e76\u6269\u5c55\u5230\u5927\u5206\u5b50\u7cfb\u7edf\u3002\u4e3a\u4e86\u5bf9 RAMD \u7ed3\u679c\u8fdb\u884c\u81ea\u52a8\u5206\u6790\uff0c\u6211\u4eec\u5f00\u53d1\u4e86 MD-IFP\uff0c\u8fd9\u662f\u4e00\u5957\u5de5\u5177\uff0c\u7528\u4e8e\u6cbf\u7740\u975e\u7ed3\u5408\u8f68\u8ff9\u751f\u6210 IFPs\uff0c\u5e76\u7528\u4e8e\u7814\u7a76\u914d\u4f53\u52a8\u529b\u5b66\u3002\u6211\u4eec\u8bc1\u660e\uff0c\u901a\u8fc7\u5c06\u914d\u4f53\u5206\u89e3\u8f68\u8ff9\u6620\u5c04\u5230 IFP \u7a7a\u95f4\uff0c\u5206\u6790\u914d\u4f53\u5206\u89e3\u8f68\u8ff9\uff0c\u53ef\u4ee5\u5f97\u5230\u914d\u4f53\u5206\u89e3\u8def\u5f84\u548c\u4e9a\u7a33\u6001\u7684\u89d2\u8272\u5851\u9020\u3002Ramd \u548c MD-IFP \u7684\u8054\u5408\u5b9e\u65bd\u63d0\u4f9b\u4e86\u4e00\u79cd\u8ba1\u7b97\u6548\u7387\u9ad8\u3001\u53ef\u514d\u8d39\u83b7\u5f97\u7684\u5de5\u4f5c\u6d41\u7a0b\uff0c\u53ef\u4ee5\u5728\u5408\u7406\u7684\u8ba1\u7b97\u65f6\u95f4\u5185\u5e94\u7528\u4e8e\u6570\u767e\u79cd\u5316\u5408\u7269\uff0c\u5e76\u5c06\u4fc3\u8fdb\u5728\u836f\u7269\u8bbe\u8ba1\u4e2d\u4f7f\u7528 tauRAMD\u3002<\/span><\/p>\n


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    \u7528\u5149\u6cf5\u6fc0\u53d1\u5927\u5206\u5b50\u7684\u975e\u5e73\u8861\u6001<\/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

    Exciting out-of-equilibrium states in macromolecules through light pumping<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11008<\/span><\/p>\n

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

    Elham Faraji,Roberto Franzosi,Stefano Mancini,Marco Pettini<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In the present paper we address the problem of the energy downconversion of the light absorbed by a protein into its internal vibrational modes. We consider the case in which the light receptors are fluorophores either naturally co-expressed with the protein or artificially covalently bound to some of its amino acids. In a recent work [Phys. Rev. X 8, 031061 (2018)], it has been experimentally found that by shining a laser light on the fluorophores attached to a protein the energy fed to it can be channeled into the normal mode of lowest frequency of vibration thus making the subunits of the protein coherently oscillate. Even if the phonon condensation phenomenon has been theoretically explained, the first step – the energy transfer from electronic excitation into phonon excitation – has been left open. The present work is aimed at filling this gap.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u86cb\u767d\u8d28\u5438\u6536\u7684\u5149\u80fd\u4e0b\u8f6c\u6362\u4e3a\u5176\u5185\u90e8\u632f\u52a8\u6a21\u5f0f\u7684\u95ee\u9898\u3002\u6211\u4eec\u8003\u8651\u7684\u60c5\u51b5\u4e0b\uff0c\u5149\u53d7\u4f53\u662f\u8367\u5149\u56e2\u6216\u8005\u81ea\u7136\u5171\u540c\u8868\u8fbe\u7684\u86cb\u767d\u8d28\u6216\u4eba\u5de5\u5171\u4ef7\u7ed3\u5408\u5230\u4e00\u4e9b\u6c28\u57fa\u9178\u3002\u5728\u6700\u8fd1\u7684\u4f5c\u54c1[\u4f53\u80b2\u30028,031061(2018)] \uff0c\u5b83\u5df2\u7ecf\u88ab\u5b9e\u9a8c\u53d1\u73b0\uff0c\u901a\u8fc7\u7528\u6fc0\u5149\u7167\u5c04\u9644\u7740\u5728\u86cb\u767d\u8d28\u4e0a\u7684\u8367\u5149\u56e2\uff0c\u80fd\u91cf\u88ab\u8f93\u9001\u5230\u6b63\u5e38\u7684\u6700\u4f4e\u632f\u52a8\u9891\u7387\u6a21\u5f0f\uff0c\u4ece\u800c\u4f7f\u86cb\u767d\u8d28\u7684\u4e9a\u57fa\u76f8\u5e72\u632f\u8361\u3002\u5373\u4f7f\u4ece\u7406\u8bba\u4e0a\u89e3\u91ca\u4e86\u58f0\u5b50\u51dd\u805a\u73b0\u8c61\uff0c\u7b2c\u4e00\u6b65\u2014\u2014\u4ece\u7535\u5b50\u6fc0\u53d1\u5230\u58f0\u5b50\u6fc0\u53d1\u7684\u80fd\u91cf\u8f6c\u79fb\u2014\u2014\u4ecd\u7136\u662f\u5f00\u653e\u7684\u3002\u76ee\u524d\u7684\u5de5\u4f5c\u65e8\u5728\u586b\u8865\u8fd9\u4e00\u7a7a\u767d\u3002<\/span><\/p>\n


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    \u542f\u52a8\u63a8\u7406\u589e\u52a0\u610f\u56fe\uff0c\u6234\u4e0a\u9762\u7f69\uff0c<\/strong><\/span><\/p>\n

    \u4ee5\u51cf\u7f13\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad<\/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

    Priming reasoning increases intentions to wear a face covering to slow down COVID-19 transmission<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11273<\/span><\/p>\n

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

    Valerio Capraro,H\u00e9l\u00e8ne Barcelo<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Finding mechanisms to promote the use of face masks is fundamental during the second phase of the COVID-19 pandemic response, when shelter-in-place rules are relaxed and some segments of the population are allowed to circulate more freely. Here we report three pre-registered studies (total N = 1,920), using an heterogenous sample of people living in the USA, showing that priming people to “rely on their reasoning” rather than to “rely on their emotions” significantly increases their intentions to wear a face covering. Compared to the baseline, priming reasoning promotes intentions to wear a face covering, whereas priming emotion has no significant effect. These findings have theoretical and practical implications. Practically, they offer a simple and scalable intervention to promote intentions to wear a face mask. Theoretically, they shed light on the cognitive basis of intentions to wear a face covering.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u536b\u751f\u7ec4\u7ec7\u5927\u6d41\u884c\u75c5\u5e94\u5bf9\u7684\u7b2c\u4e8c\u9636\u6bb5\uff0c\u5bfb\u627e\u4fc3\u8fdb\u53e3\u7f69\u4f7f\u7528\u7684\u673a\u5236\u662f\u81f3\u5173\u91cd\u8981\u7684\uff0c\u5c4a\u65f6\u539f\u5730\u907f\u96be\u7684\u89c4\u5b9a\u5c06\u653e\u5bbd\uff0c\u4e00\u4e9b\u4eba\u53e3\u7fa4\u4f53\u5c06\u88ab\u5141\u8bb8\u66f4\u81ea\u7531\u5730\u6d41\u52a8\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u62a5\u544a\u4e86\u4e09\u4e2a\u9884\u5148\u6ce8\u518c\u7684\u7814\u7a76(\u603b\u6570 n = 1920) \uff0c\u4f7f\u7528\u4e86\u4e00\u4e2a\u751f\u6d3b\u5728\u7f8e\u56fd\u7684\u5f02\u8d28\u6837\u672c\uff0c\u663e\u793a\u542f\u52a8\u4eba\u4eec\u201c\u4f9d\u9760\u4ed6\u4eec\u7684\u63a8\u7406\u201d\u800c\u4e0d\u662f\u201c\u4f9d\u9760\u4ed6\u4eec\u7684\u60c5\u7eea\u201d\u663e\u8457\u5730\u589e\u52a0\u4e86\u4ed6\u4eec\u7684\u610f\u56fe\u6234\u9762\u7f69\u3002\u4e0e\u57fa\u7ebf\u76f8\u6bd4\uff0c\u542f\u52a8\u63a8\u7406\u4fc3\u8fdb\u610f\u5411\u6234\u9762\u7f69\uff0c\u800c\u542f\u52a8\u60c5\u7eea\u5bf9\u610f\u5411\u6234\u9762\u7f69\u6ca1\u6709\u663e\u8457\u5f71\u54cd\u3002\u8fd9\u4e9b\u53d1\u73b0\u5177\u6709\u7406\u8bba\u548c\u5b9e\u8df5\u610f\u4e49\u3002\u5b9e\u9645\u4e0a\uff0c\u4ed6\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7b80\u5355\u548c\u53ef\u6269\u5c55\u7684\u5e72\u9884\u63aa\u65bd\u6765\u4fc3\u8fdb\u6234\u53e3\u7f69\u7684\u610f\u56fe\u3002\u4ece\u7406\u8bba\u4e0a\u8bb2\uff0c\u4ed6\u4eec\u63ed\u793a\u4e86\u6234\u9762\u7f69\u610f\u56fe\u7684\u8ba4\u77e5\u57fa\u7840\u3002<\/span><\/p>\n


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    \u9057\u4f20\u7a0b\u5e8f\u8bbf\u95ee\u8ba1\u5212\u89e3\u51b3\u65b9\u6848<\/strong><\/span><\/p>\n

    \u53ef\u4ee5\u51cf\u5c11\u4e25\u5cfb\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2<\/strong><\/span><\/p>\n

    \u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u7684\u4eba\u53e3\u7981\u95ed<\/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

    Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.10748<\/span><\/p>\n

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

    Daniel Howard<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning’, `afternoon’, `night’ or `any time’ when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5f15\u5165\u4e86\u4e00\u79cd\u8ba1\u7b97\u65b9\u6cd5\uff0c\u4ee5\u51cf\u5c11\u4eba\u4eec\u5728\u5e94\u5bf9\u5927\u6d41\u884c\u65f6\u906d\u53d7\u67d0\u79cd\u7a0b\u5ea6\u7684\u5c01\u9501\u7684\u611f\u67d3\u673a\u4f1a\uff0c\u6b63\u59822020\u5e74\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u4e00\u6837\u3002\u4eba\u4eec\u4f7f\u7528\u79fb\u52a8\u7535\u8bdd\u6216\u8ba1\u7b97\u673a\u8bbe\u5907\u8981\u6c42\u524d\u5f80\u4ed6\u4eec\u9700\u8981\u6216\u611f\u5174\u8da3\u7684\u5730\u65b9\uff0c\u8868\u660e\u4e00\u5929\u4e2d\u7684\u8270\u82e6\u65f6\u95f4: \u201d\u4e0a\u5348\u201d\u3001\u201d\u4e0b\u5348\u201d\u3001\u201d\u591c\u95f4\u201d\u6216\u201d\u4efb\u4f55\u65f6\u95f4\u201d \uff0c\u5f53\u4ed6\u4eec\u60f3\u8981\u8fdb\u884c\u8fd9\u4e9b\u5916\u51fa\u6d3b\u52a8\u65f6\uff0c\u4ee5\u53ca\u4ed6\u4eec\u60f3\u8981\u53bb\u7684\u5730\u65b9\u3002\u4eba\u5de5\u667a\u80fd\u65b9\u6cd5\u662f\u9057\u4f20\u89c4\u5212\u7684\u4e00\u79cd\u53d8\u4f53\uff0c\u5b83\u7814\u7a76\u6240\u6709\u8bf7\u6c42\uff0c\u5e76\u5bf9\u8fd9\u79cd\u8bbf\u95ee\u4f5c\u51fa\u5177\u4f53\u65f6\u95f4\u5206\u914d\uff0c\u4ece\u800c\u6700\u5927\u9650\u5ea6\u5730\u51cf\u5c11\u4eba\u4eec\u611f\u67d3\u3001\u4f4f\u9662\u548c\u6b7b\u4ea1\u7684\u603b\u4f53\u98ce\u9669\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u8fd9\u4e00\u8ba1\u7b97\u7684\u82e5\u5e72\u5907\u9009\u65b9\u6848\uff0c\u5e76\u4ecb\u7ecd\u4e86\u8fde\u7eed\u4e09\u5929\u8d85\u8fc71700\u6b21\u8bbf\u95ee\u4e2d\u6d89\u53ca\u4e0d\u540c\u5e74\u9f84\u548c\u80cc\u666f\u5065\u5eb7\u6c34\u5e73\u7684230\u591a\u4eba\u7684\u6570\u5b57\u5b9e\u9a8c\u7ed3\u679c\u3002\u5f15\u5165\u4e00\u4e2a\u65b0\u7684\u90e8\u5206\u611f\u67d3\u6a21\u578b\u6765\u8ba8\u8bba\u8fd9\u4e9b\u6982\u5ff5\u9a8c\u8bc1\u89e3\u51b3\u65b9\u6848\uff0c\u5e76\u5c06\u5176\u4e0e\u8bbf\u95ee\u5730\u70b9\u7684\u5faa\u73af\u65e0\u6d88\u606f\u65f6\u95f4\u5b89\u6392\u8fdb\u884c\u6bd4\u8f83\u3002\u8ba1\u7b97\u7ed3\u679c\u8868\u660e\uff0c\u968f\u7740\u6b7b\u4ea1\u4eba\u6570\u548c\u4f4f\u9662\u4eba\u6570\u7684\u5927\u5e45\u51cf\u5c11\uff0c\u60c5\u51b5\u6709\u4e86\u5de8\u5927\u7684\u6539\u5584\u3002\u8fd9\u4e9b\u87ba\u65cb\u4e95\u4e3a\u4e00\u4e2a\u66f4\u73b0\u5b9e\u7684\u7814\u7a76\u4f7f\u7528\u51c6\u786e\u7684\u611f\u67d3\u6a21\u578b\uff0c\u4ee5\u6d4b\u8bd5\u90e8\u7f72\u5728\u73b0\u5b9e\u4e16\u754c\u7684\u770b\u6cd5\u3002\u9a71\u52a8\u611f\u67d3\u6a21\u578b\u7684\u8f93\u5165\u662f\u6309\u5206\u7c7b\u7b49\u7ea7\u5212\u5206\u7684\u611f\u67d3\u7a0b\u5ea6\uff0c\u4f8b\u5982\u53ef\u80fd\u4ea7\u751f\u4e8e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6216\u4efb\u4f55\u6c61\u67d3\u6a21\u578b\u7684\u79cd\u7fa4\u6d4b\u8bd5\u7684\u4fe1\u606f\u3002\u8ba1\u7b97\u4e2d\u5047\u5b9a\u7684\u5206\u7c7b\u7c7b\u522b\u662f\u6309\u5e74\u9f84\u7ec4\u5206\u5217\u7684\u53ef\u80fd\u611f\u67d3\u7a0b\u5ea6\u3002<\/span><\/p>\n


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


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

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

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

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

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

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

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

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


    <\/p>\n

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

    \u6458\u8981\uff1a<\/span><\/strong>\u7531 SARS-COV2\u5f15\u8d77\u7684\u51a0\u72b6\u75c5\u6bd2\u75c5(\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e)\u5df2\u7ecf\u6210\u4e3a\u4e00\u79cd\u6d41\u884c\u75c5\u3002\u8fd9\u79cd\u75be\u75c5\u5177\u6709\u9ad8\u5ea6\u4f20\u67d3\u6027\uff0c\u53ef\u80fd\u81f4\u547d\uff0c\u5f15\u8d77\u5168\u7403\u516c\u5171\u536b\u751f\u5173\u6ce8\u3002\u4e3a\u4e86\u904f\u5236\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u8513\u5ef6\uff0c\u5404\u56fd\u653f\u5e9c\u6b63\u5728\u91c7\u53d6\u5168\u56fd\u6027\u7684\u5e72\u9884\u63aa\u65bd\uff0c\u5982\u5c01\u9501\u3001\u9694\u79bb\u548c\u9694\u79bb\u3001\u9650\u5236\u65c5\u884c\u3001\u53d6\u6d88\u793e\u4f1a\u6d3b\u52a8\u548c\u5e7f\u6cdb\u7684\u68c0\u6d4b\u3002\u4e3a\u4e86\u4ee5\u6570\u636e\u9a71\u52a8\u7684\u65b9\u5f0f\u7406\u89e3\u8fd9\u4e9b\u63aa\u65bd\u5bf9\u6d41\u884c\u75c5\u63a7\u5236\u7684\u5f71\u54cd\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u6982\u7387\u5143\u80de\u81ea\u52a8\u673a(PCA)\u7684\u6539\u8fdb SEIQR \u6a21\u578b\u3002\u4e0e\u8be5\u6a21\u578b\u76f8\u5173\u7684\u8f6c\u53d8\u662f\u7531\u5173\u4e8e\u75c5\u6bd2\u7684\u5e74\u4ee3\u5b66\u3001\u75c7\u72b6\u3001\u53d1\u75c5\u673a\u5236\u548c\u4f20\u64ad\u7387\u7684\u6570\u636e\u9a71\u52a8\u7684\u3002\u901a\u8fc7\u8bba\u8bc1\u57fa\u4e8e\u683c\u5b50\u7684\u6a21\u578b\u6355\u83b7\u4e86\u52a8\u529b\u5b66\u7684\u7279\u5f81\u4ee5\u53ca\u5b58\u5728\u7684\u6ce2\u52a8\uff0c\u6211\u4eec\u5bf9\u6a21\u578b\u8fdb\u884c\u4e86\u4e25\u683c\u7684\u8ba1\u7b97\u5206\u6790\uff0c\u4ee5\u8003\u8651\u5f3a\u52a0\u5728\u4eba\u4eec\u8eab\u4e0a\u7684\u793e\u4f1a\u8ddd\u79bb\u63aa\u65bd\u7684\u7a7a\u95f4\u52a8\u529b\u5b66\u3002\u8003\u8651\u5230\u4e0e\u51cf\u707e\u7b56\u7565\u76f8\u5173\u7684\u6982\u7387\u884c\u4e3a\u65b9\u9762\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u8003\u8651\u79cd\u7fa4\u5bc6\u5ea6\u548c\u6d4b\u8bd5\u6548\u7387\u7b49\u56e0\u7d20\u7684\u6a21\u578b\u3002\u5229\u7528\u8be5\u6a21\u578b\uff0c\u6211\u4eec\u91cd\u70b9\u5173\u6ce8\u4e0d\u540c\u56fd\u5bb6\u7684\u6d41\u884c\u75c5\u52a8\u6001\u6570\u636e\u7684\u53ef\u53d8\u6027\uff0c\u5e76\u6307\u51fa\u8fd9\u4e9b\u5bf9\u6bd4\u89c2\u5bdf\u80cc\u540e\u7684\u539f\u56e0\u3002\u636e\u6211\u4eec\u6240\u77e5\uff0c\u8fd9\u662f\u7b2c\u4e00\u6b21\u5c1d\u8bd5\u4f7f\u7528 PCA \u6a21\u578b\u6765\u6a21\u62df\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\uff0c\u8be5\u6a21\u578b\u901a\u8fc7\u6d1e\u5bdf\u4e0d\u540c\u611f\u67d3\u53c2\u6570\u7684\u4f5c\u7528\uff0c\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u611f\u67d3\u4f20\u64ad\u7684\u65f6\u7a7a\u53d8\u5316p\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    \u51e0\u4f55\uff0c\u63a8\u7406\uff0c\u590d\u6742\u6027\u548c\u6c11\u4e3b<\/strong><\/span><\/p>\n

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

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

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

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

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

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

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

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


    <\/p>\n

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

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


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    \u5728\u6d41\u884c\u671f\u95f4\u8ba1\u7b97\u98ce\u9669\u589e\u91cf\u4ee5\u505a\u51fa\u51b3\u7b56<\/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><\/span><\/strong><\/h2>\n

    Counting Risk Incre<\/span>ments to Make Decisions During an Epidemic<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11244<\/span><\/p>\n

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

    Lucien Hardy<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>I propose a smartphone app that will allow people to participate in the management of their own safety during an epidemic or pandemic such as COVID-19 by enabling them to view, in advance, the risks they would take if they visit some given venue (a cafe, the gym, the workplace, the park,…) and, furthermore, track the accumulation of such risks during the course of any given day or week. This idea can be presented to users of the app as counting points. One point represents some constant probability,<\/span><\/nobr>ppoint, of infection. Then the app would work in a similar way to a calorie counting app (instead of counting calories we count probability increments of being infected). Government could set a maximum recommended number of daily (or weekly) points available to each user in accord with its objectives (bringing the disease under control, allowing essential workers to work, protecting vulnerable individuals, …). It is posited that this, along with other proposed “levers” would allow government to manage a gradual transition to normalcy. I discuss a circuit framework with wires running between boxes. In this framework the wires represent possible sources of infection, namely individuals and the venues themselves (through deposits of pathogens left at the venue). The boxes represent interactions of these sources (when individuals visit a venue). This circuit framework allows (i) calculation of points cost for visiting venues and (ii) probabilistic contact tracing. The points systems proposed here could complement existing contact tracing apps by adding functionality to permit users to participate in decision making up front.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u63d0\u51fa\u4e86\u4e00\u4e2a\u667a\u80fd\u624b\u673a\u5e94\u7528\u7a0b\u5e8f\uff0c\u5b83\u53ef\u4ee5\u8ba9\u4eba\u4eec\u5728\u6d41\u884c\u75c5\u6216\u5927\u6d41\u884c\u75c5\u671f\u95f4\u53c2\u4e0e\u81ea\u8eab\u5b89\u5168\u7ba1\u7406\uff0c\u6bd4\u5982\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u6bd2 \/ \u827e\u6ecb\u75c5\uff0c\u8ba9\u4ed6\u4eec\u4e8b\u5148\u4e86\u89e3\u5230\uff0c\u5982\u679c\u4ed6\u4eec\u53bb\u67d0\u4e2a\u7279\u5b9a\u7684\u5730\u70b9(\u5496\u5561\u9986\u3001\u5065\u8eab\u623f\u3001\u5de5\u4f5c\u573a\u6240\u3001\u516c\u56ed… …) \uff0c\u4ed6\u4eec\u53ef\u80fd\u4f1a\u627f\u62c5\u7684\u98ce\u9669\uff0c\u800c\u4e14\uff0c\u8fd8\u53ef\u4ee5\u8ddf\u8e2a\u8fd9\u4e9b\u98ce\u9669\u5728\u4efb\u4f55\u4e00\u5929\u6216\u4e00\u5468\u4e2d\u7684\u7d2f\u79ef\u60c5\u51b5\u3002\u8fd9\u4e2a\u60f3\u6cd5\u53ef\u4ee5\u4f5c\u4e3a\u8ba1\u6570\u70b9\u5448\u73b0\u7ed9\u5e94\u7528\u7a0b\u5e8f\u7684\u7528\u6237\u3002\u4e00\u4e2a\u70b9\u4ee3\u8868\u67d0\u4e2a\u6052\u5b9a\u7684\u6982\u7387,<\/span><\/nobr>ppoint, \u7136\u540e\uff0c\u8be5\u5e94\u7528\u7a0b\u5e8f\u5c06\u4ee5\u7c7b\u4f3c\u4e8e\u5361\u8def\u91cc\u8ba1\u6570\u5e94\u7528\u7a0b\u5e8f\u7684\u65b9\u5f0f\u5de5\u4f5c(\u800c\u4e0d\u662f\u8ba1\u7b97\u5361\u8def\u91cc\uff0c\u6211\u4eec\u8ba1\u7b97\u88ab\u611f\u67d3\u7684\u6982\u7387\u589e\u91cf)\u3002\u653f\u5e9c\u53ef\u4ee5\u6839\u636e\u5176\u76ee\u6807(\u63a7\u5236\u75be\u75c5\uff0c\u5141\u8bb8\u5fc5\u8981\u7684\u5de5\u4f5c\u4eba\u5458\u5de5\u4f5c\uff0c\u4fdd\u62a4\u6613\u53d7\u4f24\u5bb3\u7684\u4e2a\u4eba…) \uff0c\u4e3a\u6bcf\u4e2a\u4f7f\u7528\u8005\u8bbe\u5b9a\u6bcf\u65e5(\u6216\u6bcf\u5468)\u53ef\u7528\u7684\u6700\u5927\u63a8\u8350\u70b9\u6570\u3002\u636e\u63a8\u6d4b\uff0c\u8fd9\u4e00\u70b9\uff0c\u8fde\u540c\u5176\u4ed6\u62df\u8bae\u7684\u201c\u6760\u6746\u201d \uff0c\u5c06\u4f7f\u653f\u5e9c\u80fd\u591f\u7ba1\u7406\u9010\u6b65\u8fc7\u6e21\u5230\u6b63\u5e38\u72b6\u6001\u3002\u6211\u8ba8\u8bba\u4e00\u4e2a\u7535\u8def\u6846\u67b6\u4e0e\u7535\u7ebf\u8fd0\u884c\u4e4b\u95f4\u7684\u76d2\u5b50\u3002\u5728\u8fd9\u4e2a\u6846\u67b6\u4e0b\uff0c\u7535\u7ebf\u4ee3\u8868\u53ef\u80fd\u7684\u611f\u67d3\u6e90\uff0c\u5373\u4e2a\u4eba\u548c\u573a\u5730\u672c\u8eab(\u901a\u8fc7\u7559\u5728\u573a\u5730\u7684\u75c5\u539f\u4f53\u6c89\u79ef)\u3002\u8fd9\u4e9b\u76d2\u5b50\u4ee3\u8868\u4e86\u8fd9\u4e9b\u6765\u6e90\u7684\u76f8\u4e92\u4f5c\u7528(\u5f53\u4e2a\u4eba\u8bbf\u95ee\u4e00\u4e2a\u573a\u6240\u65f6)\u3002\u8fd9\u4e2a\u7535\u8def\u6846\u67b6\u5141\u8bb8(i)\u8ba1\u7b97\u8bbf\u95ee\u573a\u5730\u7684\u79ef\u5206\u6210\u672c\u548c(ii)\u6982\u7387\u63a5\u89e6\u8ffd\u8e2a\u3002\u8fd9\u91cc\u63d0\u51fa\u7684\u79ef\u5206\u7cfb\u7edf\u53ef\u4ee5\u8865\u5145\u73b0\u6709\u7684\u8054\u7cfb\u4eba\u8ffd\u8e2a\u5e94\u7528\u7a0b\u5e8f\uff0c\u589e\u52a0\u529f\u80fd\uff0c\u5141\u8bb8\u7528\u6237\u53c2\u4e0e\u524d\u671f\u51b3\u7b56\u3002<\/span><\/p>\n


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    \u793e\u4f1a\u4fe1\u4efb\u7f51\u7edc\u4e2d\u7684\u610f\u89c1\u6700\u5927\u5316<\/strong><\/span><\/strong><\/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

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

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

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

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

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


    <\/p>\n

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

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

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    \u7528\u5149\u7535\u5b50\u53d1\u5c04\u80fd\u8c31<\/strong><\/span><\/p>\n

    \u6d4b\u91cf\u5171\u632f\u975e\u5f39\u6027 x \u5c04\u7ebf\u6563\u5c04: <\/strong><\/span><\/p>\n

    \u8ba1\u7b97\u7814\u7a76<\/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

    Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: A computational investigation<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.10914<\/span><\/p>\n

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

    Daniel J. Higley,Hirohito Ogasawara,Sioan Zohar,Georgi L. Dakovski<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution RIXS measurements (<100 meV), especially in the soft x-ray range, require large and low-throughput grating spectrometers that limits measurement accuracy and simplicity. Here, we computationally investigate the performance of a different method for measuring RIXS, Photoelectron Spectrometry for Analysis of X-rays (PAX). This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of compact, high-throughput electron spectrometers. In PAX, X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. The incident X-ray spectrum is then estimated through a deconvolution algorithm that leverages concepts from machine learning. We investigate a few example PAX cases. Using the 3d levels of Ag as a converter material, and with 10<\/span><\/nobr>5 detected electrons, we accurately estimate features with 100s of meV width in a model RIXS spectrum. Using a sharp Fermi edge to encode RIXS spectra, we accurately distinguish 100 meV FWHM peaks separated by 45 meV with 10<\/span><\/nobr>7 electrons detected that were photoemitted from within 0.4 eV of the Fermi level.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/strong>\u5171\u632f\u975e\u5f39\u6027 x \u5c04\u7ebf\u6563\u5c04(RIXS)\u5df2\u6210\u4e3a\u4e00\u79cd\u91cd\u8981\u7684\u79d1\u5b66\u5de5\u5177\u3002\u7136\u800c\uff0c\u4f20\u7edf\u7684\u9ad8\u5206\u8fa8\u7387 RIXS \u6d4b\u91cf(< 100mev) \uff0c\u7279\u522b\u662f\u5728\u8f6f x \u5c04\u7ebf\u8303\u56f4\u5185\uff0c\u9700\u8981\u5927\u578b\u4f4e\u901a\u91cf\u5149\u6805\u5149\u8c31\u4eea\uff0c\u8fd9\u9650\u5236\u4e86\u6d4b\u91cf\u7cbe\u5ea6\u548c\u7b80\u5355\u6027\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8ba1\u7b97\u8c03\u67e5\u7684\u6027\u80fd\u4e0d\u540c\u7684\u65b9\u6cd5\u6d4b\u91cf RIXS\uff0c\u5149\u7535\u5b50\u80fd\u8c31\u5206\u6790\u7684 x \u5c04\u7ebf(PAX)\u3002\u8fd9\u79cd\u65b9\u6cd5\u5c06 RIXS \u7684 x \u5c04\u7ebf\u6d4b\u91cf\u95ee\u9898\u8f6c\u5316\u4e3a\u7535\u5b50\u6d4b\u91cf\u95ee\u9898\uff0c\u4f7f\u5f97\u80fd\u591f\u4f7f\u7528\u5c0f\u578b\u3001\u9ad8\u901a\u91cf\u7684\u7535\u5b50\u5149\u8c31\u4eea\u3002\u5728 PAX \u4e2d\uff0c\u8981\u6d4b\u91cf\u7684 x \u5c04\u7ebf\u5165\u5c04\u5230\u4e00\u4e2a\u8f6c\u6362\u5668\u6750\u6599\u4e0a\uff0c\u7531\u6b64\u4ea7\u751f\u7684\u5149\u7535\u5b50\u7684\u80fd\u91cf\u5206\u5e03\uff0c\u5373 PAX \u5149\u8c31\uff0c\u7528\u7535\u5b50\u5149\u8c31\u4eea\u6d4b\u91cf\u3002\u7136\u540e\uff0c\u5229\u7528\u673a\u5668\u5b66\u4e60\u7684\u6982\u5ff5\uff0c\u901a\u8fc7\u89e3\u5377\u79ef\u7b97\u6cd5\u4f30\u8ba1\u5165\u5c04 x \u5c04\u7ebf\u5149\u8c31\u3002\u6211\u4eec\u8c03\u67e5\u4e86\u51e0\u4e2a\u4f8b\u5b50 PAX \u6848\u4ef6\u3002\u91c7\u75283 d \u7ea7 Ag \u4f5c\u4e3a\u8f6c\u5316\u5668\u6750\u6599\uff0c\u752810<\/span><\/nobr>5\u901a\u8fc7\u68c0\u6d4b\u7535\u5b50\uff0c\u6211\u4eec\u5728 RIXS \u6a21\u578b\u5149\u8c31\u4e2d\u4ee5100\u5146\u7535\u5b50\u4f0f\u7279\u5bbd\u5ea6\u7cbe\u786e\u5730\u4f30\u8ba1\u7279\u5f81\u3002\u5229\u7528\u5c16\u9510\u7684\u8d39\u7c73\u8fb9\u7f18\u5bf9 RIXS \u8c31\u8fdb\u884c\u7f16\u7801\uff0c\u7cbe\u786e\u5730\u5206\u8fa8\u51fa100mev \u7684\u534a\u9ad8\u5bbd\u5cf0\uff0c\u5cf0\u95f4\u8ddd\u4e3a45mev\uff0c\u5cf0\u95f4\u8ddd\u4e3a10<\/span><\/nobr>7\u4ece\u8d39\u7c73\u80fd\u7ea7\u76840.4 eV \u8303\u56f4\u5185\u63a2\u6d4b\u5230\u5149\u7535\u5b50\u3002<\/span><\/p>\n


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

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

    \n

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

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


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

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

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

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