{"id":20211,"date":"2020-06-28T20:13:27","date_gmt":"2020-06-28T12:13:27","guid":{"rendered":"https:\/\/swarma.org\/?p=20211"},"modified":"2020-06-28T20:13:27","modified_gmt":"2020-06-28T12:13:27","slug":"%e7%a6%bb%e6%95%a3%e5%9b%be%e6%a8%a1%e5%9e%8b%e7%9a%84%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e5%ad%a6%e4%b9%a0-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba%e6%96%87%e9%80%9f%e9%80%9221%e7%af%87","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20211","title":{"rendered":"\u79bb\u6563\u56fe\u6a21\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7"},"content":{"rendered":"
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    \u79bb\u6563\u56fe\u6a21\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60<\/strong><\/span><\/p>\n

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

    Learning of Discrete Graphical Models with Neural Networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11937<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Abhijith J.,Andrey Lokhov,Sidhant Misra,Marc Vuffray<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the true graphical model has higher order terms. We introduce NN-GRISE, a neural net based algorithm for graphical model learning, to tackle this limitation of GRISE. We use neural nets as function approximators in an interaction screening objective function. The optimization of this objective then produces a neural-net representation for the conditionals of the graphical model. NN-GRISE algorithm is seen to be a better alternative to GRISE when the energy function of the true model has a high order with a high degree of symmetry. In these cases, NN-GRISE is able to find the correct parsimonious representation for the conditionals without being fed any prior information about the true model. NN-GRISE can also be used to learn the underlying structure of the true model with some simple modifications to its training procedure. In addition, we also show a variant of NN-GRISE that can be used to learn a neural net representation for the full energy function of the true model.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u56fe\u5f62\u6a21\u578b\u5728\u79d1\u5b66\u4e0a\u88ab\u5e7f\u6cdb\u7528\u6765\u8868\u793a\u5177\u6709\u6761\u4ef6\u4f9d\u8d56\u7ed3\u6784\u7684\u8054\u5408\u6982\u7387\u5206\u5e03\u3002\u5229\u7528\u5e7f\u4e49\u6b63\u5219\u5316\u76f8\u4e92\u4f5c\u7528\u7b5b\u9009\u4f30\u8ba1(GRISE)\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u8fd1\u4f3c\u6700\u4f18\u7684\u6837\u672c\u590d\u6742\u5ea6\u6765\u6c42\u89e3\u7ed9\u5b9a\u7684\u79bb\u6563\u56fe\u5f62\u6a21\u578b\u5373\u4ece\u5176\u8054\u5408\u5206\u5e03\u4e2d\u62bd\u53d6\u7684\u6837\u672c\u7684\u5b66\u4e60\u53cd\u95ee\u9898\uff0c\u8fd9\u79cd\u65b9\u6cd5\u662f\u4e00\u79cd\u51f8\u4f18\u5316\u65b9\u6cd5\u3002\u4f46\u662f\u5f53\u771f\u5b9e\u56fe\u6a21\u578b\u7684\u80fd\u91cf\u51fd\u6570\u5177\u6709\u8f83\u9ad8\u9636\u9879\u65f6\uff0cGRISE \u7684\u8ba1\u7b97\u4ee3\u4ef7\u5c31\u53d8\u5f97\u9ad8\u4e0d\u53ef\u6500\u3002\u6211\u4eec\u4ecb\u7ecd NN-GRISE\uff0c\u4e00\u79cd\u57fa\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u56fe\u5f62\u6a21\u578b\u5b66\u4e60\u7b97\u6cd5\uff0c\u4ee5\u89e3\u51b3\u8fd9\u4e00\u5c40\u9650\u6027\u3002\u5728\u4ea4\u4e92\u5f0f\u7b5b\u9009\u76ee\u6807\u51fd\u6570\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u51fd\u6570\u903c\u8fd1\u5668\u3002\u8fd9\u4e2a\u76ee\u6807\u7684\u4f18\u5316\u7136\u540e\u4ea7\u751f\u4e86\u56fe\u5f62\u6a21\u578b\u6761\u4ef6\u7684\u795e\u7ecf\u7f51\u7edc\u8868\u793a\u3002\u5f53\u771f\u5b9e\u6a21\u578b\u7684\u80fd\u91cf\u51fd\u6570\u5177\u6709\u9ad8\u9636\u5bf9\u79f0\u6027\u65f6\uff0cNN-GRISE \u7b97\u6cd5\u88ab\u8ba4\u4e3a\u662f\u4e00\u79cd\u8f83\u597d\u7684\u4ee3\u66ff GRISE \u7b97\u6cd5\u7684\u65b9\u6cd5\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0cNN-GRISE \u80fd\u591f\u4e3a\u6761\u4ef6\u53e5\u627e\u5230\u6b63\u786e\u7684\u7b80\u7ea6\u8868\u793a\uff0c\u800c\u4e0d\u9700\u8981\u63d0\u4f9b\u4efb\u4f55\u5173\u4e8e\u771f\u5b9e\u6a21\u578b\u7684\u5148\u9a8c\u4fe1\u606f\u3002Nn-grise \u8fd8\u53ef\u4ee5\u7528\u6765\u5b66\u4e60\u771f\u5b9e\u6a21\u578b\u7684\u5e95\u5c42\u7ed3\u6784\uff0c\u53ea\u9700\u5bf9\u5176\u8bad\u7ec3\u8fc7\u7a0b\u8fdb\u884c\u4e00\u4e9b\u7b80\u5355\u7684\u4fee\u6539\u3002\u53e6\u5916\uff0c\u6211\u4eec\u8fd8\u5c55\u793a\u4e86 NN-GRISE \u7684\u4e00\u4e2a\u53d8\u4f53\uff0c\u5b83\u53ef\u4ee5\u7528\u6765\u5b66\u4e60\u771f\u5b9e\u6a21\u578b\u7684\u5168\u80fd\u51fd\u6570\u7684\u795e\u7ecf\u7f51\u7edc\u8868\u793a\u3002<\/span><\/p>\n


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    \u4e24\u79cd\u79bb\u6563\u52a8\u529b\u5b66\u6a21\u578b\u7684\u5173\u7cfb:<\/strong><\/span><\/p>\n

    \u4e00\u7ef4\u5143\u80de\u81ea\u52a8\u673a\u4e0e\u79ef\u5206\u503c\u53d8\u6362<\/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

    Relationship of Two Discrete Dynamical Models: One-dimensional Cellular Automata and Integral Value Transformations<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13741<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Sreeya Ghosh,Sudhakar Sahoo,Sk. Sarif Hassan,Jayanta Kumar Das,Pabitra Pal Choudhury<\/span><\/p>\n


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    Abstract\uff1a<\/span><\/strong>Cellular Automaton (CA) and an Integral Value Transformation (IVT) are two well established mathematical models which evolve in discrete time steps. Theoretically, studies on CA suggest that CA is capable of producing a great variety of evolution patterns. However computation of non-linear CA or higher dimensional CA maybe complex, whereas IVTs can be manipulated easily. The main purpose of this paper is to study the link between a transition function of a one-dimensional CA and IVTs. Mathematically, we have also established the algebraic structures of a set of transition functions of a one-dimensional CA as well as that of a set of IVTs using binary operations. Also DNA sequence evolution has been modelled using IVTs.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7ec6\u80de\u81ea\u52a8\u673a\u53d8\u6362(CA)\u548c\u79ef\u5206\u503c\u53d8\u6362(IVT)\u662f\u4e24\u4e2a\u5728\u79bb\u6563\u65f6\u95f4\u6b65\u8fdb\u5316\u7684\u6570\u5b66\u6a21\u578b\u3002\u7406\u8bba\u4e0a\uff0c\u5bf9 CA \u7684\u7814\u7a76\u8868\u660e CA \u80fd\u591f\u4ea7\u751f\u5404\u79cd\u5404\u6837\u7684\u8fdb\u5316\u6a21\u5f0f\u3002\u7136\u800c\uff0c\u975e\u7ebf\u6027\u5143\u80de\u81ea\u52a8\u673a\u6216\u9ad8\u7ef4\u5143\u80de\u81ea\u52a8\u673a\u7684\u8ba1\u7b97\u53ef\u80fd\u6bd4\u8f83\u590d\u6742\uff0c\u800c ivt \u5219\u6bd4\u8f83\u5bb9\u6613\u64cd\u4f5c\u3002\u672c\u6587\u7684\u4e3b\u8981\u76ee\u7684\u662f\u7814\u7a76\u4e00\u7ef4 CA \u7684\u8f6c\u79fb\u51fd\u6570\u4e0e ivt \u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5728\u6570\u5b66\u4e0a\uff0c\u6211\u4eec\u8fd8\u5efa\u7acb\u4e86\u4e00\u7ef4\u5143\u80de\u81ea\u52a8\u673a\u8f6c\u79fb\u51fd\u6570\u96c6\u7684\u4ee3\u6570\u7ed3\u6784\uff0c\u4ee5\u53ca\u7528\u4e8c\u8fdb\u5236\u8fd0\u7b97\u5efa\u7acb\u7684\u4e00\u7ec4\u5143\u80de\u81ea\u52a8\u673a\u8f6c\u79fb\u51fd\u6570\u7684\u4ee3\u6570\u7ed3\u6784\u3002\u6b64\u5916\uff0cDNA \u5e8f\u5217\u8fdb\u5316\u5df2\u7ecf\u6a21\u62df\u4f7f\u7528 ivt\u3002<\/span><\/p>\n


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    \u516c\u5171\u6c7d\u8f66\u6df7\u5408\u4ea4\u901a<\/strong><\/span><\/p>\n

    \u5143\u80de\u81ea\u52a8\u673a\u6a21\u578b\u4e2d\u7684\u4ea4\u53c9\u8f6c\u6362<\/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><\/h2>\n

    Crossover transitions in a bus-car mixed-traffic cellular automata model<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13532<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Damian N. Dailisan,May T. Lim<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We modify the Nagel-Schreckenberg (NaSch) cellular automata model to study mixed-traffic dynamics. We focus on the interplay between passenger availability and bus-stopping constraints. Buses stop next to occupied cells of a discretized sidewalk model. By parametrizing the spacing distance between designated stops, our simulation covers the range of load-anywhere behavior to that of well-spaced stops. The interplay of passenger arrival rates and bus densities drives crossover transitions from platooning to non-platooned (free-flow and congested) states. We show that platoons can be dissolved by either decreasing the passenger arrival rate or increasing the bus density. The critical passenger arrival rate at which platoons are dissolved is an exponential function of vehicle density. We also find that at low densities, spacing stops close together induces platooned states, which reduces system speeds and increases waiting times of passengers.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u4fee\u6539 Nagel-Schreckenberg (NaSch)\u7ec6\u80de\u81ea\u52a8\u673a\u6a21\u578b\u6765\u7814\u7a76\u6df7\u5408\u4ea4\u901a\u52a8\u529b\u5b66\u3002\u6211\u4eec\u7740\u91cd\u4e8e\u4e58\u5ba2\u53ef\u7528\u6027\u548c\u516c\u5171\u6c7d\u8f66\u505c\u8f66\u9650\u5236\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u3002\u516c\u5171\u6c7d\u8f66\u505c\u9760\u5728\u4e00\u4e2a\u79bb\u6563\u5316\u7684\u4eba\u884c\u9053\u6a21\u578b\u7684\u5360\u7528\u5355\u5143\u65c1\u8fb9\u3002\u901a\u8fc7\u53c2\u6570\u5316\u6307\u5b9a\u505c\u6b62\u4e4b\u95f4\u7684\u95f4\u8ddd\uff0c\u6211\u4eec\u7684\u6a21\u62df\u8986\u76d6\u4e86\u4ece\u4efb\u4f55\u5730\u65b9\u7684\u8d1f\u8f7d\u884c\u4e3a\u5230\u9002\u5f53\u95f4\u9694\u505c\u6b62\u7684\u8303\u56f4\u3002\u4e58\u5ba2\u5230\u8fbe\u7387\u548c\u516c\u4ea4\u8f66\u5bc6\u5ea6\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u4fc3\u4f7f\u4ea4\u53c9\u8def\u53e3\u4ece\u6392\u961f\u72b6\u6001\u8fc7\u6e21\u5230\u975e\u6392\u961f\u72b6\u6001(\u81ea\u7531\u6d41\u548c\u62e5\u6324)\u3002\u6211\u4eec\u8868\u660e\uff0c\u6392\u53ef\u4ee5\u901a\u8fc7\u964d\u4f4e\u4e58\u5ba2\u5230\u8fbe\u7387\u6216\u589e\u52a0\u516c\u5171\u6c7d\u8f66\u5bc6\u5ea6\u6765\u89e3\u6563\u3002\u89e3\u6563\u6392\u7684\u4e34\u754c\u4e58\u5ba2\u5230\u8fbe\u7387\u662f\u8f66\u8f86\u5bc6\u5ea6\u7684\u4e00\u4e2a\u6307\u6570\u51fd\u6570\u3002\u6211\u4eec\u8fd8\u53d1\u73b0\uff0c\u5728\u4f4e\u5bc6\u5ea6\u6761\u4ef6\u4e0b\uff0c\u95f4\u9694\u7ad9\u9760\u5f97\u5f88\u8fd1\u5bfc\u81f4\u4e86\u5e73\u6392\u72b6\u6001\uff0c\u964d\u4f4e\u4e86\u7cfb\u7edf\u901f\u5ea6\uff0c\u589e\u52a0\u4e86\u4e58\u5ba2\u7684\u7b49\u5f85\u65f6\u95f4\u3002<\/span><\/p>\n


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

    \n
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    <\/p>\n

    \u5728\u751f\u7269\u7f51\u7edc\u4e2d\uff0c<\/strong><\/span><\/p>\n

    \u5177\u6709\u65ad\u88c2\u7ea4\u7ef4\u5316\u5bf9\u79f0\u6027<\/strong><\/span><\/p>\n

    \u7684\u7535\u8def\u6267\u884c\u6838\u5fc3\u903b\u8f91\u8ba1\u7b97<\/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

    Circuits with broken fibration symmetries perform core logic computations in biological networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13334<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Ian Leifer,Flaviano Morone,Saulo D. S. Reis,Jose S. Andrade Jr.,Mariano Sigman,Hernan A. Makse<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8bc1\u660e\uff0c\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u4e2d\u7684\u903b\u8f91\u8ba1\u7b97\u7535\u8def\u662f\u7531\u7f51\u7edc\u7ed3\u6784\u4e2d\u7684\u7ea4\u7ef4\u5bf9\u79f0\u6027\u7834\u7f3a\u4ea7\u751f\u7684\u3002\u4ece\u8fd9\u4e2a\u60f3\u6cd5\u51fa\u53d1\uff0c\u6211\u4eec\u5b9e\u73b0\u4e86\u4e00\u4e2a\u5efa\u8bbe\u6027\u7684\u8fc7\u7a0b\uff0c\u63ed\u793a\u4e86\u57fa\u56e0\u7535\u8def\u7684\u5c42\u6b21\u7ed3\u6784\uff0c\u8fd9\u4e9b\u57fa\u56e0\u7535\u8def\u5728\u7269\u79cd\u95f4\u65e0\u5904\u4e0d\u5728\uff0c\u5b83\u4eec\u4e0e\u56fa\u6001\u7535\u5b50\u5668\u4ef6\u7684\u5178\u578b\u7535\u8def\u60ca\u4eba\u5730\u76f8\u4f3c: \u4ece\u6676\u4f53\u7ba1\u5f00\u59cb\uff0c\u9010\u6e10\u53d1\u5c55\u6210\u73af\u5f62\u632f\u8361\u5668\uff0c\u7535\u6d41-\u955c\u50cf\u7535\u8def\u5230\u5207\u6362\u5f00\u5173\u548c\u89e6\u53d1\u5668\u3002\u8fd9\u4e9b\u89c4\u8303\u53d8\u91cf\u670d\u52a1\u4e8e\u540c\u6b65\u548c\u65f6\u949f(\u5904\u4e8e\u5bf9\u79f0\u72b6\u6001)\u4ee5\u53ca\u5185\u5b58\u5b58\u50a8(\u5904\u4e8e\u5bf9\u79f0\u7834\u7f3a\u72b6\u6001)\u7684\u57fa\u672c\u64cd\u4f5c\u3002\u8fd9\u4e9b\u7ed3\u8bba\u4e3a\u5bfb\u627e\u751f\u7269\u7f51\u7edc\u4e2d\u7684\u8ba1\u7b97\u6a21\u5757\u63d0\u4f9b\u4e86\u4e00\u79cd\u7406\u8bba\u4e0a\u7684\u539f\u5219\u6027\u7b56\u7565\uff0c\u5e76\u4e3a\u5408\u6210\u751f\u7269\u7535\u8def\u7684\u8bbe\u8ba1\u63d0\u4f9b\u4e86\u4e00\u6761\u7cfb\u7edf\u8def\u7ebf\u3002<\/span><\/p>\n


    <\/p>\n

    \n
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    <\/p>\n

    \n
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    <\/p>\n

    \u6c14\u5019\u53d8\u5316\u7edf\u8ba1\u529b\u5b66\u7279\u520a\u7b80\u4ecb<\/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

    Introduction to the Special Issue on the Statistical Mechanics of Climate<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13495<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Valerio Lucarini<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We introduce the special issue on the Statistical Mechanics of Climate published on the Journal of Statistical Physics by presenting an informal discussion of some theoretical aspects of climate dynamics that make it a topic of great interest for mathematicians and theoretical physicists. In particular, we briefly discuss its nonequilibrium and multiscale properties, the relationship between natural climate variability and climate change, the different regimes of climate response to perturbations, and critical transitions.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u5728\u300a\u7edf\u8ba1\u7269\u7406\u5b66\u6742\u5fd7\u300b\u4e0a\u53d1\u8868\u4e86\u4e00\u7bc7\u5173\u4e8e\u6c14\u5019\u53d8\u5316\u7edf\u8ba1\u529b\u5b66\u7684\u7279\u520a\uff0c\u901a\u8fc7\u5bf9 Climate dynamics \u7684\u4e00\u4e9b\u7406\u8bba\u65b9\u9762\u7684\u975e\u6b63\u5f0f\u8ba8\u8bba\u6765\u4ecb\u7ecd\u5b83\uff0c\u8fd9\u4f7f\u5b83\u6210\u4e3a\u6570\u5b66\u5bb6\u548c\u7406\u8bba\u7269\u7406\u5b66\u5bb6\u4eec\u6781\u611f\u5174\u8da3\u7684\u8bdd\u9898\u3002\u7279\u522b\u662f\uff0c\u6211\u4eec\u7b80\u8981\u5730\u8ba8\u8bba\u4e86\u5b83\u7684\u975e\u5e73\u8861\u548c\u591a\u5c3a\u5ea6\u7279\u6027\uff0c\u81ea\u7136\u6c14\u5019\u53d8\u5316\u548c\u6c14\u5019\u53d8\u5316\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u6c14\u5019\u5bf9\u6270\u52a8\u7684\u4e0d\u540c\u53cd\u5e94\u673a\u5236\uff0c\u4ee5\u53ca\u4e34\u754c\u8fc7\u6e21\u3002<\/span><\/p>\n


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


    <\/p>\n

    \n
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    <\/p>\n

    \u7cbe\u786e\u903c\u8fd1\u6781\u503c\u7edf\u8ba1\u91cf<\/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

    Accurately approximating extreme value statistics<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13677<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Lior Zarfaty,Eli Barkai,David A. Kessler<\/span><\/p>\n


    <\/p>\n

    A<\/span><\/strong>bstract\uff1a<\/strong>We consider the extreme value statistics of<\/span><\/nobr>N independent and identically distributed random variables, which is a classic problem in probability theory. When <\/span><\/nobr>N\u2192\u221e, small fluctuations around the renormalized maximum of the variables are described by the Fisher-Tippett-Gnedenko theorem, which states that the distribution of maxima converges to one out of three forms. However, for a wide class of distributions, the convergence rate with <\/span><\/nobr>N is ultra-slow. Here, we apply the Lambert scaling method which greatly accelerates the rate of convergence to the limiting Gumbel form. We also find this scaling useful when large deviations from the mean extreme value are considered.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8003\u8651\u7684\u6781\u503c\u7edf\u8ba1<\/span><\/nobr>N \u72ec\u7acb\u548c\u540c\u5206\u5e03\u7684\u968f\u673a\u53d8\u91cf\uff0c\u8fd9\u662f\u4e00\u4e2a\u7ecf\u5178\u7684\u95ee\u9898\u572821\u6982\u7387\u8bba<\/span><\/nobr>N\u2192\u221e,\u672c\u6587\u5229\u7528 Fisher-Tippett-Gnedenko \u5b9a\u7406\u63cf\u8ff0\u4e86\u53d8\u91cf\u91cd\u6574\u5316\u6700\u5927\u503c\u5468\u56f4\u7684\u5c0f\u6ce2\u52a8\uff0c\u8be5\u5b9a\u7406\u6307\u51fa\u6700\u5927\u503c\u7684\u5206\u5e03\u6536\u655b\u4e8e\u4e09\u79cd\u5f62\u5f0f\u4e2d\u7684\u4e00\u79cd\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u4e00\u7c7b\u5e7f\u6cdb\u7684\u5206\u5e03\uff0c\u6536\u655b\u901f\u5ea6\u4e0e<\/span><\/nobr>N \u662f\u8d85\u6162\u7684\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5e94\u7528\u6717\u4f2f\u5b9a\u6807\u65b9\u6cd5\uff0c\u5927\u5927\u52a0\u5feb\u4e86\u6536\u655b\u901f\u5ea6\u5230\u6781\u9650 Gumbel \u5f62\u5f0f\u3002\u6211\u4eec\u8fd8\u53d1\u73b0\uff0c\u5f53\u8003\u8651\u5e73\u5747\u6781\u503c\u7684\u5927\u504f\u5dee\u65f6\uff0c\u8fd9\u79cd\u6807\u5ea6\u4e5f\u662f\u6709\u7528\u7684\u3002<\/span><\/p>\n


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    \u4f18\u5148\u8fde\u63a5\u7f51\u7edc\u4e2d\u5e73\u5747\u573a\u8fd1\u4f3c\u7684\u6279\u5224<\/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 critique of the Mean Field Approximation in preferential attachment networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13295<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Matthijs Ruijgrok<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The Mean Field Approximation (MFA), or continuum method, is often used in courses on Networks to derive the degree distribution of preferential attachment networks. This method is simple and the outcome is close to the correct answer. However, this paper shows that the method is flawed in several aspects, leading to unresolvable contradictions. More importantly, the MFA is not explicitly derived from a mathematical model. An analysis of the implied model shows that it makes an approximation which is far from the truth and another one which can not be motivated in general. The success of the MFA for preferential attachment networks is therefore accidental and the method is not suitable for teaching undergraduates.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5e73\u5747\u573a\u8fd1\u4f3c\u6cd5(MFA) \uff0c\u6216\u79f0\u8fde\u7eed\u7edf\u8ba1\u6cd5\uff0c\u662f\u7f51\u7edc\u8bfe\u7a0b\u4e2d\u5e38\u7528\u7684\u63a8\u5bfc\u4f18\u5148\u8fde\u63a5\u7f51\u7edc\u5ea6\u5206\u5e03\u7684\u65b9\u6cd5\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\uff0c\u7ed3\u679c\u63a5\u8fd1\u6b63\u786e\u7b54\u6848\u3002\u7136\u800c\uff0c\u672c\u6587\u6307\u51fa\uff0c\u8be5\u65b9\u6cd5\u5b58\u5728\u4e00\u4e9b\u7f3a\u9677\uff0c\u5bfc\u81f4\u4e86\u65e0\u6cd5\u89e3\u51b3\u7684\u77db\u76fe\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0cMFA \u6ca1\u6709\u660e\u786e\u5730\u4ece\u6570\u5b66\u6a21\u578b\u6d3e\u751f\u51fa\u6765\u3002\u5bf9\u9690\u542b\u6a21\u578b\u7684\u5206\u6790\u8868\u660e\uff0c\u9690\u542b\u6a21\u578b\u6240\u4f5c\u7684\u8fd1\u4f3c\u4e0e\u4e8b\u5b9e\u76f8\u53bb\u751a\u8fdc\uff0c\u800c\u4e14\u662f\u4e00\u822c\u60c5\u51b5\u4e0b\u65e0\u6cd5\u6fc0\u53d1\u7684\u8fd1\u4f3c\u3002\u4f18\u5148\u8fde\u63a5\u7f51\u7edc MFA \u7684\u6210\u529f\u662f\u5076\u7136\u7684\uff0c\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u9002\u7528\u4e8e\u672c\u79d1\u6559\u5b66\u3002<\/span><\/p>\n


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

    \u58f0\u5b66-\u8349\u76ae: <\/strong><\/span><\/p>\n

    \u57fa\u4e8e\u58f0\u5b66\u4fdd\u62a4\u9690\u79c1\u7684<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u63a5\u89e6\u8ffd\u8e2a<\/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

    ACOUSTIC-TURF: Acoustic-based Privacy-Preserving COVID-19 Contact Tracing<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13362<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Yuxiang Luo,Cheng Zhang,Yunqi Zhang,Chaoshun Zuo,Dong Xuan,Zhiqiang Lin,Adam C. Champion,Ness Shroff<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this paper, we propose a new privacy-preserving, automated contact tracing system, ACOUSTIC-TURF, to fight COVID-19 using acoustic signals sent from ubiquitous mobile devices. At a high level, ACOUSTIC-TURF adaptively broadcasts inaudible ultrasonic signals with randomly generated IDs in the vicinity. Simultaneously, the system receives other ultrasonic signals sent from nearby (e.g., 6 feet) users. In such a system, individual user IDs are not disclosed to others and the system can accurately detect encounters in physical proximity with 6-foot granularity. We have implemented a prototype of ACOUSTIC-TURF on Android and evaluated its performance in terms of acoustic-signal-based encounter detection accuracy and power consumption at different ranges and under various occlusion scenarios. Experimental results show that ACOUSTIC-TURF can detect multiple contacts within a 6-foot range for mobile phones placed in pockets and outside pockets. Furthermore, our acoustic-signal-based system achieves greater precision than wireless-signal-based approaches when contact tracing is performed through walls. ACOUSTIC-TURF correctly determines that people on opposite sides of a wall are not in contact with one another, whereas the Bluetooth-based approaches detect nonexistent contacts among them.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u4fdd\u62a4\u9690\u79c1\u7684\u81ea\u52a8\u63a5\u89e6\u8ffd\u8e2a\u7cfb\u7edf acoutic-turf\uff0c\u5b83\u53ef\u4ee5\u5229\u7528\u65e0\u5904\u4e0d\u5728\u7684\u79fb\u52a8\u8bbe\u5907\u53d1\u51fa\u7684\u58f0\u97f3\u4fe1\u53f7\u6765\u5bf9\u6297\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u3002\u5728\u9ad8\u6c34\u5e73\u4e0a\uff0cACOUSTIC-TURF \u5229\u7528\u9644\u8fd1\u968f\u673a\u4ea7\u751f\u7684 id \u81ea\u9002\u5e94\u5e7f\u64ad\u4e0d\u53ef\u542c\u89c1\u7684\u8d85\u58f0\u6ce2\u4fe1\u53f7\u3002\u540c\u65f6\uff0c\u7cfb\u7edf\u63a5\u6536\u6765\u81ea\u9644\u8fd1\u7528\u6237(\u4f8b\u5982\uff0c6\u82f1\u5c3a)\u7684\u5176\u4ed6\u8d85\u58f0\u6ce2\u4fe1\u53f7\u3002\u5728\u8fd9\u6837\u4e00\u4e2a\u7cfb\u7edf\u4e2d\uff0c\u4e2a\u4eba\u7528\u6237 id \u4e0d\u4f1a\u5411\u5176\u4ed6\u7528\u6237\u516c\u5f00\uff0c\u7cfb\u7edf\u53ef\u4ee5\u5728\u7269\u7406\u8ddd\u79bb\u8fd1\u52306\u82f1\u5c3a\u7684\u7c92\u5ea6\u4e0a\u51c6\u786e\u5730\u63a2\u6d4b\u5230\u906d\u9047\u3002\u6211\u4eec\u5df2\u7ecf\u5728 Android \u5e73\u53f0\u4e0a\u5b9e\u73b0\u4e86 acoutic-turf \u7684\u539f\u578b\u673a\uff0c\u5e76\u6839\u636e\u57fa\u4e8e\u58f0\u5b66\u4fe1\u53f7\u7684\u906d\u9047\u68c0\u6d4b\u7cbe\u5ea6\u548c\u5728\u4e0d\u540c\u8303\u56f4\u548c\u4e0d\u540c\u906e\u6321\u60c5\u51b5\u4e0b\u7684\u529f\u8017\u5bf9\u5176\u6027\u80fd\u8fdb\u884c\u4e86\u8bc4\u4f30\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cACOUSTIC-TURF \u53ef\u4ee5\u68c0\u6d4b6\u82f1\u5c3a\u8303\u56f4\u5185\u7684\u591a\u4e2a\u63a5\u89e6\uff0c\u4e3a\u624b\u673a\u653e\u7f6e\u5728\u53e3\u888b\u548c\u5916\u90e8\u53e3\u888b\u3002\u6b64\u5916\uff0c\u6211\u4eec\u7684\u58f0\u5b66\u4fe1\u53f7\u4e3a\u57fa\u7840\u7684\u7cfb\u7edf\u8fbe\u5230\u66f4\u9ad8\u7684\u7cbe\u5ea6\u6bd4\u65e0\u7ebf\u4fe1\u53f7\u4e3a\u57fa\u7840\u7684\u65b9\u6cd5\u65f6\uff0c\u63a5\u89e6\u8ddf\u8e2a\u662f\u901a\u8fc7\u5899\u58c1\u6267\u884c\u3002\u58f0\u5b66-\u8349\u76ae\u53ef\u4ee5\u6b63\u786e\u5730\u5224\u65ad\u5899\u58c1\u4e24\u4fa7\u7684\u4eba\u4eec\u4e4b\u95f4\u6ca1\u6709\u63a5\u89e6\uff0c\u800c\u57fa\u4e8e\u84dd\u7259\u7684\u65b9\u6cd5\u53ef\u4ee5\u68c0\u6d4b\u5230\u4ed6\u4eec\u4e4b\u95f4\u4e0d\u5b58\u5728\u7684\u63a5\u89e6\u3002<\/span><\/p>\n


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    <\/p>\n

    \n
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    <\/p>\n

    \u8d62\u5f97\u7ade\u4e89: <\/strong><\/span><\/p>\n

    \u5728\u7c7b\u4f3cSIS \u7684\u6d41\u884c\u8fc7\u7a0b\u4e2d\u52a0\u5f3a\u5bf9\u6297\u4f20\u67d3<\/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

    Winning the competition: enhancing counter-contagion in SIS-like epidemic processes<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13395<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Argyris Kalogeratos,Stefano Sarao Mannelli<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this paper we consider the epidemic competition between two generic diffusion processes, where each competing side is represented by a different state of a stochastic process. For this setting, we present the Generalized Largest Reduction in Infectious Edges (gLRIE) dynamic resource allocation strategy to advantage the preferred state against the other. Motivated by social epidemics, we apply this method to a generic continuous-time SIS-like diffusion model where we allow for: i) arbitrary node transition rate functions that describe the dynamics of propagation depending on the network state, and ii) competition between the healthy (positive) and infected (negative) states, which are both diffusive at the same time, yet mutually exclusive on each node. Finally we use simulations to compare empirically the proposed gLRIE against competitive approaches from literature.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8003\u8651\u4e86\u4e24\u4e2a\u901a\u7528\u6269\u6563\u8fc7\u7a0b\u4e4b\u95f4\u7684\u4f20\u67d3\u75c5\u7ade\u4e89\uff0c\u5176\u4e2d\u6bcf\u4e2a\u7ade\u4e89\u65b9\u7531\u4e00\u4e2a\u968f\u673a\u8fc7\u7a0b\u7684\u4e0d\u540c\u72b6\u6001\u8868\u793a\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u5e7f\u4e49\u6700\u5927\u611f\u67d3\u8fb9\u7ea6\u7b80(gLRIE)\u52a8\u6001\u8d44\u6e90\u5206\u914d\u7b56\u7565\uff0c\u4ee5\u4f18\u5316\u9996\u9009\u72b6\u6001\u3002\u53d7\u793e\u4f1a\u6d41\u884c\u75c5\u7684\u5f71\u54cd\uff0c\u6211\u4eec\u5c06\u8fd9\u79cd\u65b9\u6cd5\u5e94\u7528\u5230\u4e00\u822c\u7684\u8fde\u7eed\u65f6\u95f4 SIS- \u7c7b\u6269\u6563\u6a21\u578b\u4e2d\uff0c\u5176\u4e2d\u6211\u4eec\u5141\u8bb8: i)\u4efb\u610f\u7684\u8282\u70b9\u8f6c\u79fb\u7387\u51fd\u6570\u63cf\u8ff0\u4f9d\u8d56\u4e8e\u7f51\u7edc\u72b6\u6001\u7684\u4f20\u64ad\u52a8\u6001\uff0cii)\u5065\u5eb7(\u6b63)\u548c\u611f\u67d3(\u8d1f)\u72b6\u6001\u4e4b\u95f4\u7684\u7ade\u4e89\uff0c\u8fd9\u4e24\u79cd\u72b6\u6001\u540c\u65f6\u6269\u6563\uff0c\u4f46\u5728\u6bcf\u4e2a\u8282\u70b9\u4e0a\u76f8\u4e92\u6392\u65a5\u3002\u6700\u540e\uff0c\u6211\u4eec\u5229\u7528\u6a21\u62df\u5b9e\u9a8c\u6765\u6bd4\u8f83\u6240\u63d0\u51fa\u7684 gLRIE \u4e0e\u6587\u732e\u4e2d\u7684\u7ade\u4e89\u65b9\u6cd5\u3002<\/span><\/p>\n


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    \u7ef4\u57fa\u767e\u79d1\u548c\u5a01\u65af\u654f\u65af\u7279: <\/strong><\/span><\/p>\n

    \u82f1\u56fd\u653f\u5ba2\u7ef4\u57fa\u767e\u79d1\u9875\u9762\u7684\u8d28\u91cf\u548c\u52a8\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

    Wikipedia and Westminster: Quality and Dynamics of Wikipedia Pages about UK Politicians<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13400<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Pushkal Agarwal,Miriam Redi,Nishanth Sastry,Edward Wood,Andrew Blick<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Wikipedia is a major source of information providing a large variety of content online, trusted by readers from around the world. Readers go to Wikipedia to get reliable information about different subjects, one of the most popular being living people, and especially politicians. While a lot is known about the general usage and information consumption on Wikipedia, less is known about the life-cycle and quality of Wikipedia articles in the context of politics. The aim of this study is to quantify and qualify content production and consumption for articles about politicians, with a specific focus on UK Members of Parliament (MPs). First, we analyze spatio-temporal patterns of readers’ and editors’ engagement with MPs’ Wikipedia pages, finding huge peaks of attention during election times, related to signs of engagement on other social media (e.g. Twitter). Second, we quantify editors’ polarisation and find that most editors specialize in a specific party and choose specific news outlets as references. Finally we observe that the average citation quality is pretty high, with statements on ‘Early life and career’ missing citations most often (18%).<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7ef4\u57fa\u767e\u79d1\u662f\u63d0\u4f9b\u5927\u91cf\u5728\u7ebf\u5185\u5bb9\u7684\u4e3b\u8981\u4fe1\u606f\u6765\u6e90\uff0c\u53d7\u5230\u4e16\u754c\u5404\u5730\u8bfb\u8005\u7684\u4fe1\u4efb\u3002\u8bfb\u8005\u53bb\u7ef4\u57fa\u767e\u79d1\u662f\u4e3a\u4e86\u83b7\u5f97\u5173\u4e8e\u4e0d\u540c\u4e3b\u9898\u7684\u53ef\u9760\u4fe1\u606f\uff0c\u800c\u8fd9\u4e9b\u4e3b\u9898\u662f\u5f53\u4ee3\u6700\u53d7\u6b22\u8fce\u7684\u4eba\u7269\u4e4b\u4e00\uff0c\u5c24\u5176\u662f\u653f\u6cbb\u5bb6\u3002\u867d\u7136\u4eba\u4eec\u5bf9\u7ef4\u57fa\u767e\u79d1\u7684\u4e00\u822c\u4f7f\u7528\u60c5\u51b5\u548c\u4fe1\u606f\u6d88\u8017\u4e86\u89e3\u5f88\u591a\uff0c\u4f46\u5bf9\u4e8e\u7ef4\u57fa\u767e\u79d1\u6587\u7ae0\u5728\u653f\u6cbb\u80cc\u666f\u4e0b\u7684\u751f\u547d\u5468\u671f\u548c\u8d28\u91cf\u5374\u77e5\u4e4b\u751a\u5c11\u3002\u8fd9\u9879\u7814\u7a76\u7684\u76ee\u7684\u662f\u91cf\u5316\u548c\u9650\u5236\u6709\u5173\u653f\u6cbb\u5bb6\u7684\u6587\u7ae0\u5185\u5bb9\u7684\u751f\u4ea7\u548c\u6d88\u8d39\uff0c\u7279\u522b\u5173\u6ce8\u4e8e\u82f1\u56fd\u56fd\u4f1a\u8bae\u5458(\u4e0b\u9662\u8bae\u5458)\u3002\u9996\u5148\uff0c\u6211\u4eec\u5206\u6790\u4e86\u8bfb\u8005\u548c\u7f16\u8f91\u53c2\u4e0e\u56fd\u4f1a\u8bae\u5458\u7ef4\u57fa\u767e\u79d1\u9875\u9762\u7684\u65f6\u7a7a\u6a21\u5f0f\uff0c\u53d1\u73b0\u5728\u9009\u4e3e\u671f\u95f4\uff0c\u4e0e\u5176\u4ed6\u793e\u4ea4\u5a92\u4f53(\u5982 Twitter)\u53c2\u4e0e\u8ff9\u8c61\u76f8\u5173\u7684\u5173\u6ce8\u5ea6\u8fbe\u5230\u4e86\u9ad8\u5cf0\u3002\u5176\u6b21\uff0c\u6211\u4eec\u91cf\u5316\u4e86\u7f16\u8f91\u7684\u4e24\u6781\u5206\u5316\uff0c\u53d1\u73b0\u5927\u591a\u6570\u7f16\u8f91\u4e13\u6ce8\u4e8e\u7279\u5b9a\u7684\u515a\u6d3e\uff0c\u5e76\u9009\u62e9\u7279\u5b9a\u7684\u65b0\u95fb\u5a92\u4f53\u4f5c\u4e3a\u53c2\u8003\u3002\u6700\u540e\uff0c\u6211\u4eec\u89c2\u5bdf\u5230\u5e73\u5747\u5f15\u7528\u8d28\u91cf\u76f8\u5f53\u9ad8\uff0c\u201c\u65e9\u671f\u751f\u6d3b\u548c\u804c\u4e1a\u201d\u7684\u9648\u8ff0\u6700\u5e38\u7f3a\u5c11\u5f15\u7528(18%)\u3002<\/span><\/p>\n


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    \u5728\u7ebf\u7ade\u4e89\u5f71\u54cd\u529b\u6700\u5927\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

    Online Competitive Influence Maximization<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13411<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Jinhang Zuo,Xutong Liu,Carlee Joe-Wong,John C. S. Lui,Wei Chen<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., products, news stories) propagate in the same network and influence probabilities on edges are unknown. We adapt the combinatorial multi-armed bandit (CMAB) framework for the OCIM problem, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem. We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds, and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases. We propose an OCIM-OIFU algorithm with such an oracle that achieves logarithmic regret. We also design an OCIM-ETC algorithm that has worse regret bound but requires less feedback and easier offline computation. Our experimental evaluations demonstrate the effectiveness of our algorithms.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u5f71\u54cd\u529b\u6700\u5927\u5316\u4f5c\u4e3a\u4e00\u79cd\u5728\u5b66\u4e60\u672a\u77e5\u7f51\u7edc\u53c2\u6570\u503c\u7684\u540c\u65f6\uff0c\u901a\u8fc7\u793e\u4f1a\u7f51\u7edc\u6700\u5927\u5316\u5f71\u54cd\u529b\u4f20\u64ad\u7684\u65b9\u5f0f\uff0c\u5df2\u7ecf\u5f15\u8d77\u4e86\u4eba\u4eec\u7684\u5e7f\u6cdb\u5173\u6ce8\u3002\u4ee5\u5f80\u7684\u7814\u7a76\u5927\u591a\u96c6\u4e2d\u5728\u5355\u9879\u6269\u6563\u4e0a\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u5728\u7ebf\u7ade\u4e89\u5f71\u54cd\u529b\u6700\u5927\u5316\u95ee\u9898(OCIM) \uff0c\u5176\u4e2d\u4e24\u4e2a\u7ade\u4e89\u9879(\u5982\u4ea7\u54c1\u3001\u65b0\u95fb\u62a5\u9053)\u5728\u540c\u4e00\u7f51\u7edc\u4e2d\u4f20\u64ad\uff0c\u8fb9\u7f18\u7684\u5f71\u54cd\u6982\u7387\u662f\u672a\u77e5\u7684\u3002\u6211\u4eec\u5c06\u7ec4\u5408\u591a\u81c2\u8001\u864e\u673a\u6846\u67b6\u5e94\u7528\u4e8e OCIM \u95ee\u9898\uff0c\u4f46\u4e0e\u975e\u7ade\u4e89\u6027\u8bbe\u7f6e\u4e0d\u540c\u7684\u662f\uff0c\u7531\u4e8e\u4f20\u64ad\u7684\u7ade\u4e89\u6027\u8d28\uff0c\u91cd\u8981\u7684\u5355\u8c03\u6027\u8d28(\u5f71\u54cd\u6269\u6563\u968f\u7740\u5f71\u54cd\u8fb9\u6982\u7387\u7684\u589e\u52a0\u800c\u589e\u52a0)\u4e0d\u518d\u6210\u7acb\uff0c\u8fd9\u7ed9\u8be5\u95ee\u9898\u5e26\u6765\u4e86\u65b0\u7684\u91cd\u5927\u6311\u6218\u3002\u8bc1\u660e\u4e86 CMAB \u7684\u89e6\u53d1\u6982\u7387\u8c03\u5236(TPM)\u6761\u4ef6\u4ecd\u7136\u6210\u7acb\uff0c\u5e76\u5229\u7528\u7ade\u4e89\u6269\u6563\u6027\u8d28\u5f15\u5165\u4e86\u4e00\u4e2a\u65b0\u7684\u79bb\u7ebf\u9884\u8a00\uff0c\u8ba8\u8bba\u4e86\u5728\u4e0d\u540c\u60c5\u51b5\u4e0b\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e2a\u65b0\u7684\u9884\u8a00\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd OCIM-OIFU \u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u6570\u9057\u61be\u3002\u6211\u4eec\u8fd8\u8bbe\u8ba1\u4e86\u4e00\u4e2a OCIM-ETC \u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u5177\u6709\u8f83\u5dee\u7684\u540e\u6094\u754c\uff0c\u4f46\u9700\u8981\u8f83\u5c11\u7684\u53cd\u9988\u548c\u66f4\u5bb9\u6613\u7684\u8131\u673a\u8ba1\u7b97\u3002\u6211\u4eec\u7684\u5b9e\u9a8c\u8bc4\u4f30\u8bc1\u660e\u4e86\u6211\u4eec\u7684\u7b97\u6cd5\u7684\u6709\u6548\u6027\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u8054\u5408\u6f14\u5458\u8868\u5f81\u548c<\/strong><\/span><\/p>\n

    \u793e\u4ea4\u5a92\u4f53\u60c5\u611f\u7684\u7535\u5f71\u7968\u623f\u9884\u6d4b<\/strong><\/span><\/p>\n

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


    <\/h2>\n

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

    Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13417<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Dezhou Shen<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In recent years, driven by the Asian film industry, such as China and India, the global box office has maintained a steady growth trend. Previous studies have rarely used long-term, full-sample film data in analysis, lack of research on actors’ social networks. Existing film box office prediction algorithms only use film meta-data, lack of using social network characteristics and the model is less interpretable. I propose a FC-GRU-CNN binary classification model in of box office prediction task, combining five characteristics, including the film meta-data, Sina Weibo text sentiment, actors’ social network measurement, all pairs shortest path and actors’ art contribution. Exploiting long-term memory ability of GRU layer in long sequences and the mapping ability of CNN layer in retrieving all pairs shortest path matrix features, proposed model is 14% higher in accuracy than the current best C-LSTM model.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74\u6765\uff0c\u5728\u4e2d\u56fd\u3001\u5370\u5ea6\u7b49\u4e9a\u6d32\u7535\u5f71\u4ea7\u4e1a\u7684\u63a8\u52a8\u4e0b\uff0c\u5168\u7403\u7968\u623f\u4fdd\u6301\u4e86\u7a33\u6b65\u589e\u957f\u7684\u8d8b\u52bf\u3002\u4ee5\u524d\u7684\u7814\u7a76\u5f88\u5c11\u4f7f\u7528\u957f\u671f\u7684\u3001\u5168\u6837\u672c\u7684\u7535\u5f71\u6570\u636e\u8fdb\u884c\u5206\u6790\uff0c\u7f3a\u4e4f\u5bf9\u6f14\u5458\u793e\u4ea4\u7f51\u7edc\u7684\u7814\u7a76\u3002\u73b0\u6709\u7684\u7535\u5f71\u7968\u623f\u9884\u6d4b\u7b97\u6cd5\u4ec5\u4f7f\u7528\u7535\u5f71\u5143\u6570\u636e\uff0c\u7f3a\u4e4f\u5229\u7528\u793e\u4f1a\u7f51\u7edc\u7684\u7279\u70b9\u548c\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u3002\u7ed3\u5408\u7535\u5f71\u5143\u6570\u636e\u3001\u65b0\u6d6a\u5fae\u535a\u6587\u672c\u60c5\u611f\u3001\u6f14\u5458\u793e\u4ea4\u7f51\u7edc\u6d4b\u91cf\u3001\u6240\u6709\u5bf9\u7684\u6700\u77ed\u8def\u5f84\u548c\u6f14\u5458\u7684\u827a\u672f\u8d21\u732e\u7b49\u4e94\u4e2a\u7279\u5f81\uff0c\u63d0\u51fa\u4e86\u4e00\u4e2a FC-GRU-CNN \u7968\u623f\u9884\u6d4b\u4efb\u52a1\u7684\u4e8c\u5143\u5206\u7c7b\u6a21\u578b\u3002\u5229\u7528\u957f\u5e8f\u5217\u4e2d GRU \u5c42\u7684\u957f\u65f6\u8bb0\u5fc6\u80fd\u529b\u548c CNN \u5c42\u7684\u6620\u5c04\u80fd\u529b\u63d0\u53d6\u6240\u6709\u5bf9\u7684\u6700\u77ed\u8def\u5f84\u77e9\u9635\u7279\u5f81\uff0c\u8be5\u6a21\u578b\u7684\u51c6\u786e\u7387\u6bd4\u76ee\u524d\u6700\u4f18\u7684 C-LSTM \u6a21\u578b\u9ad814%\u3002<\/span><\/p>\n


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    \u53ef\u8bc1\u660e\u548c\u6709\u6548\u5730\u4f7f\u7528 <\/strong><\/span>Tur\u00e1n <\/strong><\/p>\n

    \u9634\u5f71\u8fd1\u4f3c\u8fd1\u6d3e\u7cfb: PEANUTS<\/strong><\/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

    Provably and Efficiently Approximating Near-cliques using the Tur\u00e1n Shadow: PEANUTS<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13483<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Shweta Jain,C. Seshadhri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Clique and near-clique counts are important graph properties with applications in graph generation, graph modeling, graph analytics, community detection among others. They are the archetypal examples of dense subgraphs. While there are several different definitions of near-cliques, most of them share the attribute that they are cliques that are missing a small number of edges. Clique counting is itself considered a challenging problem. Counting near-cliques is significantly harder more so since the search space for near-cliques is orders of magnitude larger than that of cliques. We give a formulation of a near-clique as a clique that is missing a constant number of edges. We exploit the fact that a near-clique contains a smaller clique, and use techniques for clique sampling to count near-cliques. This method allows us to count near-cliques with 1 or 2 missing edges, in graphs with tens of millions of edges. To the best of our knowledge, there was no known efficient method for this problem, and we obtain a 10x – 100x speedup over existing algorithms for counting near-cliques. Our main technique is a space-efficient adaptation of the Tur’an Shadow sampling approach, recently introduced by Jain and Seshadhri (WWW 2017). This approach constructs a large recursion tree (called the Tur’an Shadow) that represents cliques in a graph. We design a novel algorithm that builds an estimator for near-cliques, using an online, compact construction of the Tur’an Shadow.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u56e2\u8ba1\u6570\u548c\u8fd1\u56e2\u8ba1\u6570\u662f\u56fe\u7684\u91cd\u8981\u5c5e\u6027\uff0c\u5728\u56fe\u5f62\u751f\u6210\u3001\u56fe\u5f62\u5efa\u6a21\u3001\u56fe\u5f62\u5206\u6790\u3001\u793e\u533a\u68c0\u6d4b\u7b49\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5b83\u4eec\u662f\u7a20\u5bc6\u5b50\u56fe\u7684\u5178\u578b\u4f8b\u5b50\u3002\u867d\u7136\u8fd1\u6d3e\u7cfb\u6709\u51e0\u79cd\u4e0d\u540c\u7684\u5b9a\u4e49\uff0c\u4f46\u5927\u591a\u6570\u90fd\u6709\u4e00\u4e2a\u5171\u540c\u7684\u5c5e\u6027\uff0c\u90a3\u5c31\u662f\u5b83\u4eec\u90fd\u7f3a\u5c11\u4e00\u4e9b\u8fb9\u3002\u96c6\u56e2\u8ba1\u6570\u672c\u8eab\u5c31\u88ab\u8ba4\u4e3a\u662f\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\u3002\u8ba1\u7b97\u8fd1\u6d3e\u7cfb\u7684\u96be\u5ea6\u8981\u5927\u5f97\u591a\uff0c\u56e0\u4e3a\u8fd1\u6d3e\u7cfb\u7684\u641c\u7d22\u7a7a\u95f4\u6bd4\u6570\u91cf\u7ea7\u7684\u641c\u7d22\u7a7a\u95f4\u5927\u5f97\u591a\u3002\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u8fd1\u6d3e\u7684\u516c\u5f0f\u4f5c\u4e3a\u4e00\u4e2a\u5c0f\u56e2\u662f\u7f3a\u5c11\u4e00\u4e2a\u56fa\u5b9a\u6570\u91cf\u7684\u8fb9\u3002\u6211\u4eec\u5229\u7528\u4e86\u8fd9\u6837\u4e00\u4e2a\u4e8b\u5b9e\uff0c\u5373\u8fd1\u56e2\u4f53\u5305\u542b\u4e00\u4e2a\u8f83\u5c0f\u7684\u56e2\u4f53\uff0c\u5e76\u4f7f\u7528\u56e2\u4f53\u62bd\u6837\u6280\u672f\u8ba1\u6570\u8fd1\u56e2\u4f53\u3002\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u8ba1\u7b97\u5177\u6709\u6570\u5343\u4e07\u6761\u8fb9\u7684\u56fe\u4e2d\u7f3a\u59311\u6761\u62162\u6761\u8fb9\u7684\u8fd1\u4f3c\u56e2\u3002\u636e\u6211\u4eec\u6240\u77e5\uff0c\u6ca1\u6709\u5df2\u77e5\u7684\u6709\u6548\u65b9\u6cd5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u83b7\u5f97\u4e86\u4e00\u4e2a10\u500d-100\u500d\u7684\u52a0\u901f\u6bd4\u73b0\u6709\u7684\u7b97\u6cd5\u8ba1\u7b97\u8fd1\u6d3e\u7cfb\u3002\u6211\u4eec\u7684\u4e3b\u8981\u6280\u672f\u662f\u5bf9\u6700\u8fd1\u7531 Jain \u548c Seshadhri (WWW 2017)\u5f15\u5165\u7684 Tur\u2018 an \u9634\u5f71\u91c7\u6837\u65b9\u6cd5\u7684\u6709\u6548\u7a7a\u95f4\u9002\u5e94\u3002\u8fd9\u79cd\u65b9\u6cd5\u6784\u9020\u4e86\u4e00\u4e2a\u5927\u578b\u7684\u9012\u5f52\u6811(\u79f0\u4e3a Tur\u2018 an Shadow) \uff0c\u7528\u4e8e\u8868\u793a\u56fe\u4e2d\u7684\u5c0f\u56e2\u3002\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u65b0\u7684\u7b97\u6cd5\uff0c\u4f7f\u7528\u5728\u7ebf\uff0c\u7d27\u51d1\u7684\u56fe\u5c14\u5b89\u9634\u5f71\u6784\u9020\u8fd1\u6d3e\u7cfb\u7684\u4f30\u8ba1\u5668\u3002<\/span><\/p>\n


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    \u5c11\u5373\u662f\u591a: <\/strong><\/span><\/p>\n

    \u5229\u7528\u793e\u4f1a\u4fe1\u4efb\u63d0\u9ad8\u6b3a\u9a97\u653b\u51fb\u7684\u6709\u6548\u6027<\/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

    Less is More: Exploiting Social Trust to Increase the Effectiveness of a Deception Attack<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13499<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Shahryar Baki,Rakesh M. Verma,Arjun Mukherjee,Omprakash Gnawali<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Cyber attacks such as phishing, IRS scams, etc., still are successful in fooling Internet users. Users are the last line of defense against these attacks since attackers seem to always find a way to bypass security systems. Understanding users’ reason about the scams and frauds can help security providers to improve users security hygiene practices. In this work, we study the users’ reasoning and the effectiveness of several variables within the context of the company representative fraud. Some of the variables that we study are: 1) the effect of using LinkedIn as a medium for delivering the phishing message instead of using email, 2) the effectiveness of natural language generation techniques in generating phishing emails, and 3) how some simple customizations, e.g., adding sender’s contact info to the email, affect participants perception. The results obtained from the within-subject study show that participants are not prepared even for a well-known attack – company representative fraud. Findings include: approximately 65% mean detection rate and insights into how the success rate changes with the facade and correspondent (sender\/receiver) information. A significant finding is that a smaller set of well-chosen strategies is better than a large `mess’ of strategies. We also find significant differences in how males and females approach the same company representative fraud. Insights from our work could help defenders in developing better strategies to evaluate their defenses and in devising better training strategies.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u653b\u51fb\u5982\u7f51\u7edc\u9493\u9c7c\u3001\u56fd\u7a0e\u5c40\u8bc8\u9a97\u7b49\uff0c\u4ecd\u7136\u80fd\u591f\u6210\u529f\u5730\u6b3a\u9a97\u4e92\u8054\u7f51\u7528\u6237\u3002\u7528\u6237\u662f\u62b5\u5fa1\u8fd9\u4e9b\u653b\u51fb\u7684\u6700\u540e\u4e00\u9053\u9632\u7ebf\uff0c\u56e0\u4e3a\u653b\u51fb\u8005\u4f3c\u4e4e\u603b\u80fd\u627e\u5230\u7ed5\u8fc7\u5b89\u5168\u7cfb\u7edf\u7684\u65b9\u6cd5\u3002\u4e86\u89e3\u7528\u6237\u5bf9\u8bc8\u9a97\u548c\u6b3a\u8bc8\u7684\u7406\u7531\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u5b89\u63d0\u4f9b\u8005\u6539\u5584\u7528\u6237\u7684\u5b89\u5168\u536b\u751f\u505a\u6cd5\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u5728\u516c\u53f8\u4ee3\u8868\u4eba\u6b3a\u8bc8\u7684\u80cc\u666f\u4e0b\uff0c\u7528\u6237\u7684\u63a8\u7406\u548c\u51e0\u4e2a\u53d8\u91cf\u7684\u6709\u6548\u6027\u3002\u6211\u4eec\u7814\u7a76\u7684\u4e00\u4e9b\u53d8\u91cf\u662f: 1)\u4f7f\u7528 LinkedIn \u4f5c\u4e3a\u4f20\u9012\u9493\u9c7c\u4fe1\u606f\u7684\u5a92\u4ecb\u800c\u4e0d\u662f\u4f7f\u7528\u7535\u5b50\u90ae\u4ef6\u7684\u6548\u679c; 2)\u81ea\u7136\u8bed\u8a00\u751f\u6210\u6280\u672f\u5728\u4ea7\u751f\u9493\u9c7c\u90ae\u4ef6\u65b9\u9762\u7684\u6709\u6548\u6027; 3)\u4e00\u4e9b\u7b80\u5355\u7684\u5b9a\u5236\uff0c\u4f8b\u5982\uff0c\u5728\u7535\u5b50\u90ae\u4ef6\u4e2d\u6dfb\u52a0\u53d1\u4ef6\u4eba\u7684\u8054\u7cfb\u4fe1\u606f\uff0c\u5982\u4f55\u5f71\u54cd\u53c2\u4e0e\u8005\u7684\u611f\u77e5\u3002\u4ece\u5185\u90e8\u7814\u7a76\u5f97\u51fa\u7684\u7ed3\u679c\u8868\u660e\uff0c\u53c2\u4e0e\u8005\u751a\u81f3\u5bf9\u4f17\u6240\u5468\u77e5\u7684\u653b\u51fb\u6027\u516c\u53f8\u4ee3\u8868\u6b3a\u8bc8\u90fd\u6ca1\u6709\u51c6\u5907\u3002\u7814\u7a76\u7ed3\u679c\u5305\u62ec: \u5927\u7ea665% \u7684\u5e73\u5747\u68c0\u6d4b\u7387\u548c\u6210\u529f\u7387\u5982\u4f55\u53d8\u5316\u7684\u5916\u89c2\u548c\u901a\u4fe1\u8005(\u53d1\u9001\u8005 \/ \u63a5\u6536\u8005)\u4fe1\u606f\u7684\u6d1e\u5bdf\u529b\u3002\u4e00\u4e2a\u91cd\u8981\u7684\u53d1\u73b0\u662f\uff0c\u4e00\u7ec4\u7cbe\u5fc3\u9009\u62e9\u7684\u7b56\u7565\u6bd4\u4e00\u5927\u5806\u7b56\u7565\u8981\u597d\u3002\u6211\u4eec\u8fd8\u53d1\u73b0\uff0c\u7537\u6027\u548c\u5973\u6027\u5728\u5982\u4f55\u5904\u7406\u540c\u4e00\u5bb6\u516c\u53f8\u7684\u4ee3\u8868\u6b3a\u8bc8\u884c\u4e3a\u4e0a\u4e5f\u5b58\u5728\u663e\u8457\u5dee\u5f02\u3002\u4ece\u6211\u4eec\u7684\u5de5\u4f5c\u4e2d\u5f97\u5230\u7684\u89c1\u89e3\u53ef\u4ee5\u5e2e\u52a9\u9632\u5b88\u8005\u5236\u5b9a\u66f4\u597d\u7684\u6218\u7565\u6765\u8bc4\u4f30\u4ed6\u4eec\u7684\u9632\u5b88\uff0c\u5e76\u8bbe\u8ba1\u66f4\u597d\u7684\u8bad\u7ec3\u7b56\u7565\u3002<\/span><\/p>\n


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    \u5728\u7ebf\u6ee5\u7528\u884c\u4e3a\u6570\u636e\u96c6\u4e2d\u6ce8\u91ca\u4e00\u81f4\u6027\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

    On Analyzing Annotation Consistency in Online Abusive Behavior Datasets<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13507<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Md Rabiul Awal,Rui Cao,Roy Ka-Wei Lee,Sandra Mitrovi\u0107<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Online abusive behavior is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have proposed, collected, and annotated online abusive content datasets. These datasets play a critical role in facilitating the research on online hate speech and abusive behaviors. However, the annotation of such datasets is a difficult task; it is often contentious on what should be the true label of a given text as the semantic difference of the labels may be blurred (e.g., abusive and hate) and often subjective. In this study, we proposed an analytical framework to study the annotation consistency in online hate and abusive content datasets. We applied our proposed framework to evaluate the consistency of the annotation in three popular datasets that are widely used in online hate speech and abusive behavior studies. We found that there is still a substantial amount of annotation inconsistency in the existing datasets, particularly when the labels are semantically similar.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u8650\u5f85\u884c\u4e3a\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u95ee\u9898\uff0c\u5b83\u7834\u574f\u4e86\u7f51\u7edc\u793e\u533a\u7684\u51dd\u805a\u529b\uff0c\u751a\u81f3\u5f15\u53d1\u4e86\u793e\u4f1a\u4e2d\u7684\u516c\u5171\u5b89\u5168\u95ee\u9898\u3002\u53d7\u8fd9\u4e00\u65b0\u5174\u95ee\u9898\u7684\u63a8\u52a8\uff0c\u7814\u7a76\u4eba\u5458\u63d0\u51fa\u3001\u6536\u96c6\u5e76\u6ce8\u91ca\u4e86\u5728\u7ebf\u8fb1\u9a82\u6027\u5185\u5bb9\u6570\u636e\u96c6\u3002\u8fd9\u4e9b\u6570\u636e\u96c6\u5728\u4fc3\u8fdb\u5bf9\u7f51\u7edc\u4ec7\u6068\u8a00\u8bba\u548c\u8650\u5f85\u884c\u4e3a\u7684\u7814\u7a76\u65b9\u9762\u53d1\u6325\u4e86\u5173\u952e\u4f5c\u7528\u3002\u7136\u800c\uff0c\u8fd9\u7c7b\u6570\u636e\u96c6\u7684\u6ce8\u91ca\u662f\u4e00\u9879\u56f0\u96be\u7684\u4efb\u52a1; \u5bf9\u4e8e\u7ed9\u5b9a\u6587\u672c\u7684\u771f\u5b9e\u6807\u7b7e\u5f80\u5f80\u5b58\u5728\u4e89\u8bae\uff0c\u56e0\u4e3a\u6807\u7b7e\u7684\u8bed\u4e49\u5dee\u5f02\u53ef\u80fd\u6a21\u7cca\u4e0d\u6e05(\u4f8b\u5982\u8fb1\u9a82\u548c\u4ec7\u6068) \uff0c\u800c\u4e14\u5f80\u5f80\u662f\u4e3b\u89c2\u7684\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u5206\u6790\u6846\u67b6\u6765\u7814\u7a76\u5728\u7ebf\u8ba8\u538c\u548c\u6ee5\u7528\u5185\u5bb9\u6570\u636e\u96c6\u4e2d\u7684\u6ce8\u91ca\u4e00\u81f4\u6027\u3002\u6211\u4eec\u5e94\u7528\u6211\u4eec\u63d0\u51fa\u7684\u6846\u67b6\u6765\u8bc4\u4f30\u4e09\u4e2a\u6d41\u884c\u6570\u636e\u96c6\u4e2d\u7684\u6ce8\u91ca\u7684\u4e00\u81f4\u6027\uff0c\u8fd9\u4e09\u4e2a\u6570\u636e\u96c6\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5728\u7ebf\u4ec7\u6068\u8a00\u8bba\u548c\u8fb1\u9a82\u884c\u4e3a\u7814\u7a76\u3002\u6211\u4eec\u53d1\u73b0\u5728\u73b0\u6709\u7684\u6570\u636e\u96c6\u4e2d\u4ecd\u7136\u5b58\u5728\u5927\u91cf\u7684\u6ce8\u91ca\u4e0d\u4e00\u81f4\uff0c\u7279\u522b\u662f\u5f53\u6807\u7b7e\u5728\u8bed\u4e49\u4e0a\u76f8\u4f3c\u65f6\u3002<\/span><\/p>\n


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    \u6982\u7387\u8282\u70b9\u5931\u6548\u6a21\u578b\u4e0b\u7684\u7f51\u7edc\u8fde\u901a\u6027<\/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

    Network connectivity under a probabilistic node failure model<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.13551<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Lucia Cavallaro,Stefania Costantini,Pasquale De Meo,Antonio Liotta,Giovanni Stilo<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller sub-components. The node–removal process is generally used to test network robustness against failures. Most of the available studies assume that the node removal task is always successful. Yet, we argue that this assumption is unrealistic. Indeed, the removal process should take into account also the strength of the targeted node itself, to simulate the failure scenarios in a more effective and realistic fashion. Unlike previous literature, herein a {em probabilistic node failure model} is proposed, in which nodes may fail with a particular probability, considering two variants, namely: {em Uniform} (in which the nodes survival-to-failure probability is fixed) and {em Best Connected} (BC) (where the nodes survival probability is proportional to their degree). To evaluate our method, we consider five popular centrality metrics carrying out an experimental, comparative analysis to evaluate them in terms of {em effectiveness} and {em coverage}, on four real-world graphs. By effectiveness and coverage we mean the ability of selecting nodes whose removal decreases graph connectivity the most. Specifically, the graph spectral radius reduction works as a proxy indicator of effectiveness, and the reduction of the largest connected component (LCC) size is a parameter to assess coverage. The metric that caused the biggest drop has been then compared with the Benchmark analysis (i.e, the non-probabilistic degree centrality node removal process) to compare the two approaches. The main finding has been that significant differences emerged through this comparison with a deviation range that varies from 2% up to 80% regardless of the dataset used that highlight the existence of a gap between the common practice with a more realistic approach.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e2d\u5fc3\u6027\u5ea6\u91cf\u88ab\u5e7f\u6cdb\u5730\u5e94\u7528\u4e8e\u8bc6\u522b\u56fe\u4e2d\u7684\u8282\u70b9\uff0c\u5b83\u7684\u53bb\u9664\u53ef\u4ee5\u6709\u6548\u5730\u5c06\u56fe\u5206\u89e3\u4e3a\u66f4\u5c0f\u7684\u5b50\u7ec4\u4ef6\u3002\u8282\u70b9\u5220\u9664\u8fc7\u7a0b\u901a\u5e38\u7528\u4e8e\u6d4b\u8bd5\u7f51\u7edc\u5bf9\u5931\u8d25\u7684\u5065\u58ee\u6027\u3002\u73b0\u6709\u7684\u5927\u591a\u6570\u7814\u7a76\u5047\u5b9a\u8282\u70b9\u79fb\u9664\u4efb\u52a1\u603b\u662f\u6210\u529f\u7684\u3002\u7136\u800c\uff0c\u6211\u4eec\u8ba4\u4e3a\u8fd9\u79cd\u5047\u8bbe\u662f\u4e0d\u73b0\u5b9e\u7684\u3002\u4e8b\u5b9e\u4e0a\uff0c\u79fb\u9664\u8fc7\u7a0b\u8fd8\u5e94\u8be5\u8003\u8651\u5230\u76ee\u6807\u8282\u70b9\u672c\u8eab\u7684\u5f3a\u5ea6\uff0c\u4ee5\u66f4\u6709\u6548\u548c\u66f4\u73b0\u5b9e\u7684\u65b9\u5f0f\u6a21\u62df\u6545\u969c\u573a\u666f\u3002\u4e0d\u540c\u4e8e\u4ee5\u5f80\u7684\u6587\u732e\uff0c\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a{ em \u6982\u7387\u8282\u70b9\u5931\u6548\u6a21\u578b} \uff0c\u8be5\u6a21\u578b\u8003\u8651\u4e24\u4e2a\u53d8\u91cf\uff0c\u5373{ em \u4e00\u81f4\u6027}(\u5176\u4e2d\u8282\u70b9\u7684\u751f\u5b58\u5230\u5931\u6548\u6982\u7387\u662f\u56fa\u5b9a\u7684)\u548c{ em \u6700\u4f73\u8fde\u63a5}(BC)(\u5176\u4e2d\u8282\u70b9\u7684\u751f\u5b58\u6982\u7387\u4e0e\u8282\u70b9\u7684\u5ea6\u6210\u6b63\u6bd4)\u3002\u4e3a\u4e86\u8bc4\u4f30\u6211\u4eec\u7684\u65b9\u6cd5\uff0c\u6211\u4eec\u8003\u8651\u4e86\u4e94\u4e2a\u6d41\u884c\u7684\u4e2d\u5fc3\u6027\u5ea6\u91cf\u8fdb\u884c\u4e86\u4e00\u4e2a\u5b9e\u9a8c\uff0c\u6bd4\u8f83\u5206\u6790\uff0c\u4ee5\u8bc4\u4f30\u4ed6\u4eec\u7684{ em \u6709\u6548\u6027}\u548c{ em \u8986\u76d6\u7387} \uff0c\u5728\u56db\u4e2a\u73b0\u5b9e\u4e16\u754c\u7684\u56fe\u3002\u6240\u8c13\u6709\u6548\u6027\u548c\u8986\u76d6\u7387\uff0c\u662f\u6307\u9009\u62e9\u5220\u9664\u7387\u964d\u4f4e\u6700\u591a\u7684\u8282\u70b9\u7684\u80fd\u529b\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u56fe\u8c31\u534a\u5f84\u7f29\u51cf\u4f5c\u4e3a\u6709\u6548\u6027\u7684\u4e00\u4e2a\u4ee3\u7406\u6307\u6807\uff0c\u800c\u6700\u5927\u8fde\u63a5\u5143\u4ef6(\u56fe\u8bba)\u7f29\u51cf(LCC)\u5927\u5c0f\u662f\u8bc4\u4f30\u8986\u76d6\u7387\u7684\u4e00\u4e2a\u53c2\u6570\u3002\u7136\u540e\u5c06\u5bfc\u81f4\u6700\u5927\u964d\u4f4e\u7684\u5ea6\u91cf\u4e0e Benchmark \u5206\u6790(\u5373\u975e\u6982\u7387\u5ea6\u4e2d\u5fc3\u6027\u8282\u70b9\u5220\u9664\u8fc7\u7a0b)\u8fdb\u884c\u6bd4\u8f83\uff0c\u4ee5\u6bd4\u8f83\u4e24\u79cd\u65b9\u6cd5\u3002\u4e3b\u8981\u7684\u53d1\u73b0\u662f\uff0c\u901a\u8fc7\u8fd9\u79cd\u6bd4\u8f83\uff0c\u51fa\u73b0\u4e86\u663e\u8457\u7684\u5dee\u5f02\uff0c\u504f\u5dee\u8303\u56f4\u4ece2% \u523080% \u4e0d\u7b49\uff0c\u800c\u4e0d\u7ba1\u4f7f\u7528\u7684\u6570\u636e\u96c6\u7a81\u51fa\u8868\u660e\u4e86\u666e\u901a\u5b9e\u8df5\u4e0e\u66f4\u73b0\u5b9e\u7684\u65b9\u6cd5\u4e4b\u95f4\u5b58\u5728\u5dee\u8ddd\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u6a21\u7cca\u56fe\u53cd\u9988\u7684\u5728\u7ebf\u7a20\u5bc6\u5b50\u56fe\u53d1\u73b0<\/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

    Online Dense Subgraph Discovery via Blurred-Graph Feedback<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.13642<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n

    Yuko Kuroki,Atsushi Miyauchi,Junya Honda,Masashi Sugiyama<\/span><\/p>\n


    <\/p>\n

    Abstract<\/span><\/strong>\uff1a<\/span><\/strong>Dense subgraph discovery aims to find a dense component in edge-weighted graphs. This is a fundamental graph-mining task with a variety of applications and thus has received much attention recently. Although most existing methods assume that each individual edge weight is easily obtained, such an assumption is not necessarily valid in practice. In this paper, we introduce a novel learning problem for dense subgraph discovery in which a learner queries edge subsets rather than only single edges and observes a noisy sum of edge weights in a queried subset. For this problem, we first propose a polynomial-time algorithm that obtains a nearly-optimal solution with high probability. Moreover, to deal with large-sized graphs, we design a more scalable algorithm with a theoretical guarantee. Computational experiments using real-world graphs demonstrate the effectiveness of our algorithms.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7a20\u5bc6\u5b50\u56fe\u53d1\u73b0\u65e8\u5728\u5bfb\u627e\u8fb9\u52a0\u6743\u56fe\u4e2d\u7684\u7a20\u5bc6\u5206\u91cf\u3002\u8fd9\u662f\u4e00\u9879\u57fa\u672c\u7684\u56fe\u5f62\u6316\u6398\u4efb\u52a1\uff0c\u5e94\u7528\u5e7f\u6cdb\uff0c\u8fd1\u5e74\u6765\u53d7\u5230\u5e7f\u6cdb\u5173\u6ce8\u3002\u867d\u7136\u73b0\u6709\u7684\u5927\u591a\u6570\u65b9\u6cd5\u5047\u8bbe\u6bcf\u4e2a\u5355\u72ec\u7684\u8fb9\u6743\u91cd\u662f\u5bb9\u6613\u83b7\u5f97\u7684\uff0c\u8fd9\u6837\u7684\u5047\u8bbe\u5728\u5b9e\u8df5\u4e2d\u4e0d\u4e00\u5b9a\u6709\u6548\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u7a20\u5bc6\u5b50\u56fe\u53d1\u73b0\u5b66\u4e60\u95ee\u9898\uff0c\u5176\u4e2d\u5b66\u4e60\u8005\u67e5\u8be2\u8fb9\u5b50\u56fe\uff0c\u800c\u4e0d\u4ec5\u4ec5\u662f\u67e5\u8be2\u5355\u4e2a\u8fb9\uff0c\u5e76\u5728\u67e5\u8be2\u5b50\u56fe\u4e2d\u89c2\u5bdf\u8fb9\u6743\u91cd\u7684\u566a\u58f0\u548c\u3002\u5bf9\u4e8e\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u9996\u5148\u63d0\u51fa\u4e86\u4e00\u4e2a\u591a\u9879\u5f0f\u65f6\u95f4\u7b97\u6cd5\uff0c\u5f97\u5230\u4e86\u4e00\u4e2a\u8fd1\u4f3c\u6700\u4f18\u7684\u89e3\u4e0e\u9ad8\u6982\u7387\u3002\u6b64\u5916\uff0c\u4e3a\u4e86\u5904\u7406\u5927\u5c3a\u5bf8\u7684\u56fe\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u5177\u6709\u7406\u8bba\u4fdd\u8bc1\u7684\u66f4\u52a0\u53ef\u4f38\u7f29\u7684\u7b97\u6cd5\u3002\u4f7f\u7528\u771f\u5b9e\u4e16\u754c\u56fe\u8868\u7684\u8ba1\u7b97\u5b9e\u9a8c\u8bc1\u660e\u4e86\u6211\u4eec\u7684\u7b97\u6cd5\u7684\u6709\u6548\u6027\u3002<\/span><\/p>\n

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    \u590d\u6742\u7f51\u7edc\u5206\u6790\u4e2d <\/strong><\/span><\/p>\n

    laplace \u4f2a\u9006\u7684\u5bf9\u89d2\u903c\u8fd1<\/strong><\/span><\/strong><\/p>\n

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


    <\/h2>\n

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

    Approximation of the Diagonal of a Laplacian’s Pseudoinverse for Complex Network Analysis<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13679<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n
    Eugenio Angriman,Maria Predari,Alexander van der Grinten,Henning Meyerhenke<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The ubiquity of massive graph data sets in numerous applications requires fast algorithms for extracting knowledge from these data. We are motivated here by three electrical measures for the analysis of large small-world graphs <\/span><\/nobr>G=(V,E) — i.e., graphs with diameter in <\/span><\/nobr>O(log|V|), which are abundant in complex network analysis. From a computational point of view, the three measures have in common that their crucial component is the diagonal of the graph Laplacian’s pseudoinverse, <\/span><\/nobr>L\u2020. Computing diag<\/span><\/nobr>(L\u2020) exactly by pseudoinversion, however, is as expensive as dense matrix multiplication — and the standard tools in practice even require cubic time. Moreover, the pseudoinverse requires quadratic space — hardly feasible for large graphs. Resorting to approximation by, e.g., using the Johnson-Lindenstrauss transform, requires the solution of <\/span><\/nobr>O(log|V|\/\u03f52) Laplacian linear systems to guarantee a relative error, which is still very expensive for large inputs.
    In this paper, we present a novel approximation algorithm that requires the solution of only one Laplacian linear system. The remaining parts are purely combinatorial — mainly sampling uniform spanning trees, which we relate to diag<\/span>    <\/nobr>(L\u2020) via effective resistances. For small-world networks, our algorithm obtains a <\/span><\/nobr>\u00b1\u03f5-approximation with high probability, in a time that is nearly-linear in <\/span><\/nobr>|E| and quadratic in <\/span><\/nobr>1\/\u03f5. Another positive aspect of our algorithm is its parallel nature due to independent sampling. We thus provide two parallel implementations of our algorithm: one using OpenMP, one MPI + OpenMP. In our experiments against the state of the art, our algorithm (i) yields more accurate results, (ii) is much faster and more memory-efficient, and (iii) obtains good parallel speedups, in particular in the distributed setting.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6d77\u91cf\u56fe\u5f62\u6570\u636e\u96c6\u5728\u4f17\u591a\u5e94\u7528\u4e2d\u65e0\u5904\u4e0d\u5728\uff0c\u9700\u8981\u5feb\u901f\u7684\u7b97\u6cd5\u4ece\u8fd9\u4e9b\u6570\u636e\u4e2d\u63d0\u53d6\u77e5\u8bc6\u3002\u6211\u4eec\u5728\u8fd9\u91cc\u7684\u52a8\u673a\u662f\u4e3a\u4e86\u5206\u6790\u5927\u7684\u5c0f\u4e16\u754c\u56fe\u7684\u4e09\u4e2a\u7535\u6c14\u63aa\u65bd<\/span><\/nobr>G=(V,E)<\/span> \u5728\u590d\u6742\u7f51\u7edc\u5206\u6790\u4e2d\uff0c\u7f51\u7edc\u5206\u6790\u662f\u4e00\u79cd\u975e\u5e38\u4e30\u5bcc\u7684\u6280\u672f\u3002\u4ece\u8ba1\u7b97\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u8fd9\u4e09\u4e2a\u6d4b\u5ea6\u6709\u4e00\u4e2a\u5171\u540c\u70b9\uff0c\u5b83\u4eec\u7684\u5173\u952e\u90e8\u5206\u662f\u56fe\u7684\u62c9\u666e\u62c9\u65af\u8d5d\u9006\u7684\u5bf9\u89d2\u7ebf,<\/span><\/nobr>L\u2020<\/span>. \u3002\u8ba1\u7b97\u8bca\u6240<\/span><\/nobr>(L\u2020)<\/span> \u7136\u800c\uff0c\u51c6\u786e\u5730\u901a\u8fc7\u4f2a\u9006\u8fd0\u7b97\u5c31\u50cf\u7a20\u5bc6\u7684\u77e9\u9635\u4e58\u6cd5\u4e00\u6837\u6602\u8d35\u2014\u2014\u800c\u4e14\u5b9e\u9645\u4e2d\u7684\u6807\u51c6\u5de5\u5177\u751a\u81f3\u9700\u8981\u4e09\u6b21\u65b9\u7684\u65f6\u95f4\u3002\u6b64\u5916\uff0c\u4f2a\u9006\u9700\u8981\u4e8c\u6b21\u7a7a\u95f4\u2014\u2014\u5bf9\u4e8e\u5927\u56fe\u6765\u8bf4\u51e0\u4e4e\u4e0d\u53ef\u884c\u3002\u501f\u52a9\u4e8e\u8fd1\u4f3c\uff0c\u4f8b\u5982\uff0c\u4f7f\u7528\u7ea6\u7ff0\u900a-\u6797\u767b-\u65bd\u7279\u52b3\u65af\u53d8\u6362\uff0c\u9700\u8981\u6c42\u89e3<\/span><\/nobr>O(log|V|\/\u03f52)<\/span> \u62c9\u666e\u62c9\u65af\u7ebf\u6027\u7cfb\u7edf\uff0c\u4ee5\u4fdd\u8bc1\u76f8\u5bf9\u8bef\u5dee\uff0c\u8fd9\u4ecd\u7136\u662f\u975e\u5e38\u6602\u8d35\u7684\u5927\u578b\u8f93\u5165\uff0c \u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u8fd1\u4f3c\u6f14\u7b97\u6cd5\uff0c\u5b83\u53ea\u9700\u8981\u4e00\u4e2a\u62c9\u666e\u62c9\u65af\u7ebf\u6027\u7cfb\u7edf\u7684\u89e3\u3002\u5176\u4f59\u90e8\u5206\u662f\u7eaf\u7cb9\u7684\u7ec4\u5408\uff0c\u4e3b\u8981\u662f\u62bd\u6837\u5747\u5300\u751f\u6210\u6811\uff0c\u6211\u4eec\u6d89\u53ca diag<\/span><\/nobr>(L\u2020)<\/span>  \u9ad8\u6982\u7387\u8fd1\u4f3c\uff0c\u5728\u4e00\u5b9a\u6761\u4ef6\u4e0b\u8fd1\u4f3c\u4e3a\u7ebf\u6027<\/span><\/nobr>|E|<\/span> \uff0c\u7136\u540e\u5e73\u65b9<\/span><\/nobr>1\/\u03f5<\/span>.  \u53e6\u4e00\u4e2a\u79ef\u6781\u7684\u65b9\u9762\uff0c\u6211\u4eec\u7684\u7b97\u6cd5\u662f\u5b83\u7684\u5e76\u884c\u6027\u8d28\uff0c\u7531\u4e8e\u72ec\u7acb\u91c7\u6837\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u63d0\u4f9b\u4e86\u4e24\u4e2a\u5e76\u884c\u5b9e\u73b0\u6211\u4eec\u7684\u7b97\u6cd5: \u4e00\u4e2a\u4f7f\u7528 OpenMP\uff0c\u4e00\u4e2a MPI + OpenMP\u3002\u5728\u6211\u4eec\u9488\u5bf9\u73b0\u6709\u6280\u672f\u7684\u5b9e\u9a8c\u4e2d\uff0c\u6211\u4eec\u7684\u7b97\u6cd5(i)\u4ea7\u751f\u66f4\u51c6\u786e\u7684\u7ed3\u679c\uff0c(ii)\u66f4\u5feb\u548c\u5185\u5b58\u6548\u7387\u66f4\u9ad8\uff0c(iii)\u83b7\u5f97\u826f\u597d\u7684\u5e76\u884c\u52a0\u901f\uff0c\u7279\u522b\u662f\u5728\u5206\u5e03\u5f0f\u8bbe\u7f6e\u4e2d\u3002<\/span><\/section>\n


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    \u8fde\u63a5\u7684\u529b\u91cf: <\/strong><\/span><\/p>\n

    \u5229\u7528\u7f51\u7edc\u5206\u6790\u4fc3\u8fdb\u5e94\u6536\u8d26\u6b3e\u878d\u8d44<\/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

    The Power of Connection: Leveraging Network Analysis to Advance Receivable Financing<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13738<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n
    Ilaria Bordino,Francesco Gullo,Giacomo Legnaro<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Receivable financing is the process whereby cash is advanced to firms against receivables their customers have yet to pay: a receivable can be sold to a funder, which immediately gives the firm cash in return for a small percentage of the receivable amount as a fee. Receivable financing has been traditionally handled in a centralized way, where every request is processed by the funder individually and independently of one another. In this work we propose a novel, network-based approach to receivable financing, which enables customers of the same funder to autonomously pay each other as much as possible, and gives benefits to both the funder (reduced cash anticipation and exposure risk) and its customers (smaller fees and lightweight service establishment). Our main contributions consist in providing a principled formulation of the network-based receivable-settlement strategy, and showing how to achieve all algorithmic challenges posed by the design of this strategy. We formulate network-based receivable financing as a novel combinatorial-optimization problem on a multigraph of receivables. We show that the problem is NP-hard, and devise an exact branch-and-bound algorithm, as well as algorithms to efficiently find effective approximate solutions. Our more efficient algorithms are based on cycle enumeration and selection, and exploit a theoretical characterization in terms of a knapsack problem, as well as a refining strategy that properly adds paths between cycles. We also investigate the real-world issue of avoiding temporary violations of the problem constraints, and design methods for handling it. An extensive experimental evaluation is performed on real receivable data. Results attest the good performance of our methods.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5e94\u6536\u6b3e\u878d\u8d44\u662f\u6307\u5c06\u73b0\u91d1\u9884\u4ed8\u7ed9\u4f01\u4e1a\uff0c\u4f5c\u4e3a\u5176\u5ba2\u6237\u5c1a\u672a\u652f\u4ed8\u7684\u5e94\u6536\u6b3e\u7684\u62b5\u62bc: \u5e94\u6536\u6b3e\u53ef\u4ee5\u51fa\u552e\u7ed9\u6295\u8d44\u8005\uff0c\u6295\u8d44\u8005\u7acb\u5373\u5c06\u5e94\u6536\u6b3e\u7684\u4e00\u5c0f\u90e8\u5206\u4f5c\u4e3a\u8d39\u7528\u8fd4\u8fd8\u7ed9\u4f01\u4e1a\u73b0\u91d1\u3002\u5e94\u6536\u6b3e\u878d\u8d44\u4f20\u7edf\u4e0a\u662f\u4ee5\u96c6\u4e2d\u65b9\u5f0f\u5904\u7406\u7684\uff0c\u5373\u6bcf\u4e00\u9879\u8bf7\u6c42\u90fd\u7531\u51fa\u8d44\u65b9\u5355\u72ec\u6216\u76f8\u4e92\u72ec\u7acb\u5730\u5904\u7406\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u9896\u7684\u3001\u57fa\u4e8e\u7f51\u7edc\u7684\u5e94\u6536\u8d26\u6b3e\u878d\u8d44\u65b9\u6cd5\uff0c\u8fd9\u79cd\u65b9\u6cd5\u4f7f\u540c\u4e00\u6295\u8d44\u8005\u7684\u5ba2\u6237\u80fd\u591f\u5c3d\u53ef\u80fd\u81ea\u4e3b\u5730\u652f\u4ed8\u5f7c\u6b64\u7684\u8d26\u6b3e\uff0c\u5e76\u4f7f\u6295\u8d44\u8005\u53ca\u5176\u5ba2\u6237\u53cc\u65b9\u90fd\u53d7\u76ca(\u964d\u4f4e\u73b0\u91d1\u9884\u671f\u548c\u66b4\u9732\u98ce\u9669)(\u6536\u8d39\u8f83\u5c11\u548c\u670d\u52a1\u673a\u6784\u8f83\u8f7b)\u3002\u6211\u4eec\u7684\u4e3b\u8981\u8d21\u732e\u5728\u4e8e\u4e3a\u57fa\u4e8e\u7f51\u7edc\u7684\u5e94\u6536\u8d26\u6b3e\u7ed3\u7b97\u6218\u7565\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6709\u539f\u5219\u7684\u5236\u5b9a\u65b9\u6848\uff0c\u5e76\u5c55\u793a\u4e86\u5982\u4f55\u5b9e\u73b0\u8be5\u6218\u7565\u8bbe\u8ba1\u5e26\u6765\u7684\u6240\u6709\u7b97\u6cd5\u6311\u6218\u3002\u6211\u4eec\u5c06\u57fa\u4e8e\u7f51\u7edc\u7684\u5e94\u6536\u8d26\u6b3e\u878d\u8d44\u95ee\u9898\u63cf\u8ff0\u4e3a\u4e00\u4e2a\u591a\u5e94\u6536\u8d26\u6b3e\u56fe\u4e0a\u7684\u65b0\u7684\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u3002\u6211\u4eec\u8bc1\u660e\u4e86\u8be5\u95ee\u9898\u662f np \u96be\u7684\uff0c\u5e76\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u7cbe\u786e\u7684\u5206\u679d\u5b9a\u754c\u7b97\u6cd5\uff0c\u4ee5\u53ca\u6709\u6548\u5730\u627e\u5230\u6709\u6548\u7684\u8fd1\u4f3c\u89e3\u7684\u7b97\u6cd5\u3002\u6211\u4eec\u66f4\u6709\u6548\u7684\u7b97\u6cd5\u662f\u57fa\u4e8e\u5faa\u73af\u679a\u4e3e\u548c\u9009\u62e9\uff0c\u5229\u7528\u4e00\u4e2a\u7406\u8bba\u4e0a\u7684\u89d2\u8272\u5851\u9020\u80cc\u5305\u95ee\u9898\uff0c\u4ee5\u53ca\u4e00\u4e2a\u6539\u8fdb\u7b56\u7565\uff0c\u6b63\u786e\u5730\u589e\u52a0\u5faa\u73af\u4e4b\u95f4\u7684\u8def\u5f84\u3002\u6211\u4eec\u8fd8\u8c03\u67e5\u4e86\u907f\u514d\u4e34\u65f6\u8fdd\u53cd\u95ee\u9898\u7ea6\u675f\u7684\u73b0\u5b9e\u95ee\u9898\uff0c\u4ee5\u53ca\u5904\u7406\u5b83\u7684\u8bbe\u8ba1\u65b9\u6cd5\u3002\u5bf9\u5b9e\u9645\u5e94\u6536\u6570\u636e\u8fdb\u884c\u4e86\u5e7f\u6cdb\u7684\u5b9e\u9a8c\u8bc4\u4f30\u3002\u5b9e\u9a8c\u7ed3\u679c\u8bc1\u660e\u4e86\u8be5\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002<\/span><\/section>\n

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    \u56e2\u4f53\u8fd0\u52a8\u6bd4\u8d5b\u4e2d\u7684\u7ade\u6280\u5e73\u8861<\/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>
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    Competitive Balance in Team Sports Games<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
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    http:\/\/arxiv.org\/abs\/2006.13763<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n
    Sofia M Nikolakaki,Ogheneovo Dibie,Ahmad Beirami,Nicholas Peterson,Navid Aghdaie,Kazi Zaman<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u591a\u4eba\u5728\u7ebf\u6e38\u620f\u4e2d\uff0c\u7ade\u4e89\u662f\u73a9\u5bb6\u6ee1\u610f\u5ea6\u548c\u53c2\u4e0e\u5ea6\u7684\u4e3b\u8981\u9a71\u52a8\u529b\u3002\u4f20\u7edf\u7684\u5339\u914d\u7cfb\u7edf\u65e8\u5728\u521b\u9020\u5339\u914d\uff0c\u5305\u62ec\u5177\u6709\u76f8\u4f3c\u805a\u5408\u4e2a\u4eba\u6280\u80fd\u6c34\u5e73\u7684\u56e2\u961f\uff0c\u5982 Elo \u5f97\u5206\u6216 TrueSkill\u3002\u7136\u800c\uff0c\u56e2\u961f\u52a8\u529b\u5b66\u4e0d\u80fd\u4ec5\u4ec5\u7528\u8fd9\u6837\u7684\u7ebf\u6027\u9884\u6d4b\u6765\u6355\u6349\u3002\u6700\u8fd1\u7684\u7814\u7a76\u8868\u660e\uff0c\u975e\u7ebf\u6027\u9884\u6d4b\u7684\u76ee\u6807\u5b66\u4e60\u6982\u7387\u7684\u6210\u529f\u7387\u4f5c\u4e3a\u4e00\u4e2a\u51fd\u6570\u7684\u7403\u5458\u548c\u56e2\u961f\u7279\u5f81\u660e\u663e\u4f18\u4e8e\u8fd9\u4e9b\u7ebf\u6027\u6280\u80fd\u4e3a\u57fa\u7840\u7684\u65b9\u6cd5\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8868\u660e\uff0c\u4f7f\u7528\u6700\u7ec8\u5f97\u5206\u5dee\u5f02\u63d0\u4f9b\u4e86\u4e00\u4e2a\u66f4\u597d\u7684\u7ade\u4e89\u5e73\u8861\u7684\u9884\u6d4b\u5ea6\u91cf\u3002\u6211\u4eec\u8fd8\u8868\u660e\uff0c\u4e00\u4e2a\u7ebf\u6027\u6a21\u578b\u8bad\u7ec3\u4e86\u4e00\u7ec4\u7cbe\u5fc3\u9009\u62e9\u7684\u56e2\u961f\u548c\u4e2a\u4eba\u7279\u5f81\uff0c\u5b9e\u73b0\u4e86\u51e0\u4e4e\u66f4\u5f3a\u5927\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u6027\u80fd\uff0c\u540c\u65f6\u63d0\u4f9b\u4e862\u4e2a\u6570\u91cf\u7ea7 \/ \u63a8\u7406\u901f\u5ea6\u63d0\u9ad8\u3002\u8fd9\u663e\u793a\u4e86\u5728\u7ebf\u5339\u914d\u7cfb\u7edf\u4e2d\u5b9e\u73b0\u7684\u91cd\u8981\u524d\u666f\u3002<\/span><\/section>\n

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    \u91cf\u5316\u5e94\u5bf9\u5168\u7403\u7d27\u6025\u60c5\u51b5\u7684\u653f\u7b56:<\/strong><\/span><\/p>\n

    \u6765\u81ea\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2<\/strong><\/span><\/p>\n

    \u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u7684\u542f\u793a<\/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

    Quantifying Policy Responses to a Global Emergency: Insights from the COVID-19 Pandemic<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13853<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n
    Jian Gao,Yian Yin,Benjamin F. Jones,Dashun Wang<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Public policy must confront emergencies that evolve in real time and in uncertain directions, yet little is known about the nature of policy response. Here we take the coronavirus pandemic as a global and extraordinarily consequential case, and study the global policy response by analyzing a novel dataset recording policy documents published by government agencies, think tanks, and intergovernmental organizations (IGOs) across 114 countries (37,725 policy documents from Jan 2nd through May 26th 2020). Our analyses reveal four primary findings. (1) Global policy attention to COVID-19 follows a remarkably similar trajectory as the total confirmed cases of COVID-19, yet with evolving policy focus from public health to broader social issues. (2) The COVID-19 policy frontier disproportionately draws on the latest, peer-reviewed, and high-impact scientific insights. Moreover, policy documents that cite science appear especially impactful within the policy domain. (3) The global policy frontier is primarily interconnected through IGOs, such as the WHO, which produce policy documents that are central to the COVID19 policy network and draw especially strongly on scientific literature. Removing IGOs’ contributions fundamentally alters the global policy landscape, with the policy citation network among government agencies increasingly fragmented into many isolated clusters. (4) Countries exhibit highly heterogeneous policy attention to COVID-19. Most strikingly, a country’s early policy attention to COVID-19 shows a surprising degree of predictability for the country’s subsequent deaths. Overall, these results uncover fundamental patterns of policy interactions and, given the consequential nature of emergent threats and the paucity of quantitative approaches to understand them, open up novel dimensions for assessing and effectively coordinating global and local responses to COVID-19 and beyond.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u516c\u5171\u653f\u7b56\u5fc5\u987b\u9762\u5bf9\u5b9e\u65f6\u548c\u4e0d\u786e\u5b9a\u65b9\u5411\u6f14\u53d8\u7684\u7d27\u6025\u60c5\u51b5\uff0c\u7136\u800c\u5bf9\u4e8e\u653f\u7b56\u53cd\u5e94\u7684\u6027\u8d28\u5374\u77e5\u4e4b\u751a\u5c11\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u628a\u51a0\u72b6\u75c5\u6bd2\u5927\u6d41\u884c\u4f5c\u4e3a\u4e00\u4e2a\u5168\u7403\u6027\u548c\u975e\u5e38\u91cd\u8981\u7684\u6848\u4f8b\uff0c\u5e76\u901a\u8fc7\u5206\u6790\u4e00\u4e2a\u65b0\u7684\u6570\u636e\u96c6\u8bb0\u5f55\u653f\u7b56\u6587\u4ef6\uff0c\u7531\u653f\u5e9c\u673a\u6784\uff0c\u667a\u56ca\u56e2\u548c\u653f\u5e9c\u95f4\u7ec4\u7ec7(\u653f\u5e9c\u95f4\u7ec4\u7ec7)\u53d1\u8868\u5728114\u4e2a\u56fd\u5bb6(37,725\u653f\u7b56\u6587\u4ef6\u4ece1\u67082\u65e5\u81f32020\u5e745\u670826\u65e5)\u7684\u5168\u7403\u653f\u7b56\u54cd\u5e94\u3002\u6211\u4eec\u7684\u5206\u6790\u63ed\u793a\u4e86\u56db\u4e2a\u4e3b\u8981\u7684\u53d1\u73b0\u3002(1)\u5168\u7403\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u653f\u7b56\u5173\u6ce8\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u786e\u8bca\u75c5\u4f8b\u603b\u6570\u7684\u8f68\u8ff9\u975e\u5e38\u76f8\u4f3c\uff0c\u4f46\u653f\u7b56\u91cd\u70b9\u4ece\u516c\u5171\u536b\u751f\u5230\u66f4\u5e7f\u6cdb\u7684\u793e\u4f1a\u95ee\u9898\u4e0d\u65ad\u53d8\u5316\u3002(2)\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u653f\u7b56\u524d\u6cbf\u4e0d\u6210\u6bd4\u4f8b\u5730\u5438\u6536\u4e86\u6700\u65b0\u7684\u3001\u7ecf\u8fc7\u540c\u884c\u8bc4\u8bae\u7684\u3001\u5f71\u54cd\u529b\u5de8\u5927\u7684\u79d1\u5b66\u89c1\u89e3\u3002\u6b64\u5916\uff0c\u5f15\u7528\u79d1\u5b66\u7684\u653f\u7b56\u6587\u4ef6\u5728\u653f\u7b56\u9886\u57df\u5185\u663e\u5f97\u5c24\u5176\u6709\u5f71\u54cd\u529b\u3002(3)\u5168\u7403\u653f\u7b56\u524d\u6cbf\u4e3b\u8981\u901a\u8fc7\u8bf8\u5982\u536b\u751f\u7ec4\u7ec7\u7b49\u653f\u5e9c\u95f4\u7ec4\u7ec7\u76f8\u4e92\u8054\u7cfb\uff0c\u536b\u751f\u7ec4\u7ec7\u7f16\u5199\u7684\u653f\u7b56\u6587\u4ef6\u662f COVID19\u653f\u7b56\u7f51\u7edc\u7684\u6838\u5fc3\uff0c\u5e76\u7279\u522b\u5927\u91cf\u5229\u7528\u79d1\u5b66\u6587\u732e\u3002\u6d88\u9664\u653f\u5e9c\u95f4\u7ec4\u7ec7\u7684\u8d21\u732e\u4ece\u6839\u672c\u4e0a\u6539\u53d8\u4e86\u5168\u7403\u653f\u7b56\u683c\u5c40\uff0c\u653f\u5e9c\u673a\u6784\u4e4b\u95f4\u7684\u653f\u7b56\u5f15\u7528\u7f51\u7edc\u8d8a\u6765\u8d8a\u5206\u6563\u6210\u8bb8\u591a\u5b64\u7acb\u7684\u96c6\u7fa4\u3002(4)\u5404\u56fd\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7ec4\u7ec7\u7684\u653f\u7b56\u5173\u6ce8\u9ad8\u5ea6\u5f02\u8d28\u5316\u3002\u6700\u5f15\u4eba\u6ce8\u76ee\u7684\u662f\uff0c\u4e00\u4e2a\u56fd\u5bb6\u65e9\u671f\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u653f\u7b56\u5173\u6ce8\uff0c\u663e\u793a\u4e86\u8fd9\u4e2a\u56fd\u5bb6\u968f\u540e\u6b7b\u4ea1\u7684\u60ca\u4eba\u53ef\u9884\u6d4b\u7a0b\u5ea6\u3002\u603b\u7684\u6765\u8bf4\uff0c\u8fd9\u4e9b\u7ed3\u679c\u63ed\u793a\u4e86\u653f\u7b56\u4e92\u52a8\u7684\u57fa\u672c\u6a21\u5f0f\uff0c\u5e76\u4e14\uff0c\u8003\u8651\u5230\u7d27\u6025\u5a01\u80c1\u7684\u91cd\u8981\u6027\u548c\u7f3a\u4e4f\u5b9a\u91cf\u65b9\u6cd5\u6765\u7406\u89e3\u5b83\u4eec\uff0c\u5f00\u8f9f\u4e86\u65b0\u7684\u7ef4\u5ea6\u6765\u8bc4\u4f30\u548c\u6709\u6548\u534f\u8c03\u5168\u7403\u548c\u5730\u65b9\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u53ca\u4ee5\u540e\u7684\u53cd\u5e94\u3002<\/span><\/section>\n

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    \u91cf\u5316\u53bf\u9645\u6d41\u52a8\u6a21\u5f0f\u5bf9\u7f8e\u56fd<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u66b4\u53d1\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>
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    Quantifying the influence of inter-county mobility patterns on the COVID-19 outbreak in the United States<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13860<\/span><\/h2>\n

    \u4f5c\u8005:<\/span><\/strong><\/span><\/h2>\n
    Qianqian Sun,Yixuan Pan,Weiyi Zhou,Chenfeng Xiong,Lei Zhang<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>As a highly infectious respiratory disease, COVID-19 has become a pandemic that threatens global health. Without an effective treatment, non-pharmaceutical interventions, such as travel restrictions, have been widely promoted to mitigate the outbreak. Current studies analyze mobility metrics such as travel distance; however, there is a lack of research on interzonal travel flow and its impact on the pandemic. Our study specifically focuses on the inter-county mobility pattern and its influence on the COVID-19 spread in the United States. To retrieve real-world mobility patterns, we utilize an integrated set of mobile device location data including over 100 million anonymous devices. We first investigate the nationwide temporal trend and spatial distribution of inter-county mobility. Then we zoom in on the epicenter of the U.S. outbreak, New York City, and evaluate the impacts of its outflow on other counties. Finally, we develop a “log-linear double-risk” model at the county level to quantify the influence of both “external risk” imported by inter-county mobility flows and the “internal risk” defined as the vulnerability of a county in terms of population with high-risk phenotypes. Our study enhances the situation awareness of inter-county mobility in the U.S. and can help improve non-pharmaceutical interventions for COVID-19.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4f5c\u4e3a\u4e00\u4e2a\u9ad8\u5ea6\u4f20\u67d3\u6027\u7684\u553f\u5438\u7cfb\u7edf\u75be\u75c5\uff0c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5df2\u7ecf\u6210\u4e3a\u5a01\u80c1\u5168\u7403\u5065\u5eb7\u7684\u6d41\u884c\u75c5\u3002\u5982\u679c\u6ca1\u6709\u6709\u6548\u7684\u6cbb\u7597\uff0c\u975e\u836f\u7269\u5e72\u9884\u63aa\u65bd\uff0c\u5982\u65c5\u884c\u9650\u5236\uff0c\u5df2\u88ab\u5e7f\u6cdb\u63a8\u5e7f\uff0c\u4ee5\u51cf\u8f7b\u75ab\u60c5\u3002\u76ee\u524d\u7684\u7814\u7a76\u5206\u6790\u6d41\u52a8\u6027\u6307\u6807\uff0c\u5982\u65c5\u884c\u8ddd\u79bb\uff0c\u7136\u800c\uff0c\u7f3a\u4e4f\u7814\u7a76\u8de8\u533a\u57df\u65c5\u884c\u6d41\u53ca\u5176\u5bf9\u6d41\u884c\u75c5\u7684\u5f71\u54cd\u3002\u6211\u4eec\u7684\u7814\u7a76\u4e3b\u8981\u96c6\u4e2d\u5728\u53bf\u9645\u6d41\u52a8\u6a21\u5f0f\u53ca\u5176\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u7f8e\u56fd\u4f20\u64ad\u7684\u5f71\u54cd\u3002\u4e3a\u4e86\u68c0\u7d22\u771f\u5b9e\u4e16\u754c\u7684\u79fb\u52a8\u6a21\u5f0f\uff0c\u6211\u4eec\u5229\u7528\u4e86\u4e00\u7ec4\u96c6\u6210\u7684\u79fb\u52a8\u8bbe\u5907\u4f4d\u7f6e\u6570\u636e\uff0c\u5176\u4e2d\u5305\u62ec\u8d85\u8fc71\u4ebf\u4e2a\u533f\u540d\u8bbe\u5907\u3002\u6211\u4eec\u9996\u5148\u8c03\u67e5\u4e86\u5168\u56fd\u53bf\u9645\u6d41\u52a8\u7684\u65f6\u95f4\u8d8b\u52bf\u548c\u7a7a\u95f4\u5206\u5e03\u3002\u7136\u540e\u6211\u4eec\u653e\u5927\u5230\u7f8e\u56fd\u75ab\u60c5\u7206\u53d1\u7684\u4e2d\u5fc3\uff0c\u7ebd\u7ea6\u5e02\uff0c\u5e76\u8bc4\u4f30\u5176\u5916\u6d41\u5bf9\u5176\u4ed6\u53bf\u7684\u5f71\u54cd\u3002\u6700\u540e\uff0c\u6211\u4eec\u5728\u53bf\u4e00\u7ea7\u5efa\u7acb\u4e86\u4e00\u4e2a\u201c\u5bf9\u6570\u7ebf\u6027\u53cc\u91cd\u98ce\u9669\u201d\u6a21\u578b\uff0c\u4ee5\u91cf\u5316\u53bf\u9645\u6d41\u52a8\u5e26\u6765\u7684\u201c\u5916\u90e8\u98ce\u9669\u201d\u548c\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u53bf\u5728\u9ad8\u98ce\u9669\u8868\u578b\u4eba\u53e3\u65b9\u9762\u7684\u8106\u5f31\u6027\u7684\u201c\u5185\u90e8\u98ce\u9669\u201d\u7684\u5f71\u54cd\u3002\u6211\u4eec\u7684\u7814\u7a76\u63d0\u9ad8\u4e86\u7f8e\u56fd\u53bf\u9645\u6d41\u52a8\u6027\u7684\u72b6\u51b5\u610f\u8bc6\uff0c\u5e76\u4e14\u53ef\u4ee5\u5e2e\u52a9\u6539\u5584\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u975e\u836f\u7269\u5e72\u9884\u3002<\/span><\/section>\n


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    <\/p>\n

    \n
    \n
    \n
    \n
    \n
    \n

    \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


    <\/p>\n

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

    \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":"

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