{"id":20179,"date":"2020-06-27T18:52:24","date_gmt":"2020-06-27T10:52:24","guid":{"rendered":"https:\/\/swarma.org\/?p=20179"},"modified":"2020-06-27T18:52:24","modified_gmt":"2020-06-27T10:52:24","slug":"%e5%88%a9%e7%94%a8%e7%9e%ac%e6%80%81%e5%8a%a8%e5%8a%9b%e5%ad%a6%e5%92%8c%e6%89%b0%e5%8a%a8%ef%bc%8c%e6%8e%a8%e5%af%bc%e5%8a%a8%e5%8a%9b%e7%b3%bb%e7%bb%9f%e5%9b%a0%e6%9e%9c%e7%bd%91%e7%bb%9c-%e7%bd%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20179","title":{"rendered":"\u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c\u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc |\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901225\u7bc7"},"content":{"rendered":"
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    \u8fde\u7eed\u65f6\u95f4\u4e8b\u4ef6\u65f6\u6001\u7f51\u7edc\u7684 Hawkes \u8fb9\u754c\u5212\u5206\u6a21\u578b\uff1b<\/span><\/h2>\n<\/li>\n
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    <\/span><\/p>\n

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    \u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c<\/strong><\/span><\/p>\n

    \u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc<\/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

    Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.13154<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    George Stepaniants,Bingni W. Brunton,J. Nathan Kutz<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given data. We explore in depth a strategy to disambiguate between possible underlying causal networks by perturbing the network, where the actuations are either targeted or applied at random. The resulting transient dynamics provide the critical information necessary to infer causality. Two methods are shown to provide accurate causal reconstructions: Granger causality (GC) with perturbations, and our proposed perturbation cascade inference (PCI). Perturbed GC is capable of inferring smaller networks under low coupling strength regimes. Our proposed PCI method demonstrated consistently strong performance in inferring causal relations for small (2-5 node) and large (10-20 node) networks, with both linear and nonlinear dynamics. Thus the ability to apply a large and diverse set of perturbations\/actuations to the network is critical for successfully and accurately determining causal relations and disambiguating between various viable networks.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4ece\u65f6\u95f4\u5e8f\u5217\u6d4b\u91cf\u6570\u636e\u63a8\u65ad\u56e0\u679c\u5173\u7cfb\u662f\u4e00\u4e2a\u4e0d\u9002\u5b9a\u7684\u6570\u5b66\u95ee\u9898\uff0c\u901a\u5e38\u6709\u65e0\u9650\u591a\u7684\u6f5c\u5728\u89e3\u53ef\u4ee5\u91cd\u73b0\u7ed9\u5b9a\u7684\u6570\u636e\u3002\u6211\u4eec\u6df1\u5165\u63a2\u8ba8\u4e86\u4e00\u79cd\u7b56\u7565\uff0c\u901a\u8fc7\u6270\u4e71\u7f51\u7edc\uff0c\u6d88\u9664\u53ef\u80fd\u7684\u6f5c\u5728\u56e0\u679c\u7f51\u7edc\u4e4b\u95f4\u7684\u6b67\u4e49\uff0c\u5176\u4e2d\u7684\u6267\u884c\u8981\u4e48\u662f\u6709\u9488\u5bf9\u6027\u7684\uff0c\u8981\u4e48\u662f\u968f\u673a\u5e94\u7528\u3002\u7531\u6b64\u4ea7\u751f\u7684\u77ac\u6001\u52a8\u529b\u5b66\u63d0\u4f9b\u4e86\u63a8\u65ad\u56e0\u679c\u5173\u7cfb\u6240\u5fc5\u9700\u7684\u5173\u952e\u4fe1\u606f\u3002\u6709\u4e24\u79cd\u65b9\u6cd5\u53ef\u4ee5\u63d0\u4f9b\u7cbe\u786e\u7684\u56e0\u679c\u91cd\u6784: \u5e26\u6709\u6270\u52a8\u7684\u683c\u5170\u6770\u56e0\u679c\u5173\u7cfb\u91cd\u6784(GC)\u548c\u6211\u4eec\u63d0\u51fa\u7684\u6270\u52a8\u7ea7\u8054\u63a8\u7406(PCI)\u3002\u5728\u4f4e\u8026\u5408\u5f3a\u5ea6\u7684\u60c5\u51b5\u4e0b\uff0c\u6270\u52a8 GC \u80fd\u591f\u63a8\u65ad\u51fa\u8f83\u5c0f\u7684\u7f51\u7edc\u3002\u6211\u4eec\u63d0\u51fa\u7684 PCI \u65b9\u6cd5\u5728\u63a8\u65ad\u7ebf\u6027\u548c\u975e\u7ebf\u6027\u52a8\u6001\u7684\u5c0f\u578b(2-5\u8282\u70b9)\u548c\u5927\u578b(10-20\u8282\u70b9)\u7f51\u7edc\u7684\u56e0\u679c\u5173\u7cfb\u65b9\u9762\u4e00\u8d2f\u8868\u73b0\u51fa\u5f3a\u5927\u7684\u6027\u80fd\u3002\u56e0\u6b64\uff0c\u5bf9\u7f51\u7edc\u5e94\u7528\u5927\u91cf\u548c\u591a\u79cd\u591a\u6837\u7684\u6270\u52a8 \/ \u9a71\u52a8\u7684\u80fd\u529b\u5bf9\u4e8e\u6210\u529f\u548c\u51c6\u786e\u5730\u786e\u5b9a\u5404\u79cd\u53ef\u884c\u7f51\u7edc\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u548c\u6d88\u9664\u6b67\u4e49\u81f3\u5173\u91cd\u8981\u3002<\/span><\/section>\n


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    \u5229\u7528\u591a\u65e5\u51fa\u884c\u8ba2\u5355\u6570\u636e<\/strong><\/span><\/p>\n

    \u63cf\u8ff0\u53eb\u8f66\u6d41\u52a8\u6027: <\/strong><\/span><\/p>\n

    \u4e2d\u56fd\u5317\u4eac\u7684\u6848\u4f8b\u7814\u7a76<\/strong><\/span><\/p>\n

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

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

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

    Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12937<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Zhengbing He<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over the world. Although it attracts much attention of scientific community, the understanding of ride-hailing is still very limited due to a lack of related data. For the first time, this paper introduces ride-hailing drivers’ multi-day trip order data in Beijing, China and portrays ride-hailing.pdf mobility from the regional and driver perspectives. The analyses from the regional perspective help to understand the spatiotemporal flowing of the ride-hailing demand, and those from the driver perspective characterize the ride-hailing drivers’ preference in providing ride-hailing services. A series of findings are obtained, such as the observations of the shrinking and expanding processes of the ride-hailing demand and the two categories of the ride-hailing drivers in term of the correlations between the activity region and working time. Those findings contribute to the understanding of the ride-hailing activities, the prediction of the ride-hailing demand, the modeling of the ride-hailing drivers’ preferences, and the management of the ride-hailing services.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4f5c\u4e3a\u79fb\u52a8\u4e92\u8054\u7f51\u65f6\u4ee3\u7684\u4e00\u79cd\u65b0\u5174\u65c5\u884c\u6a21\u5f0f\uff0c\u901a\u8fc7\u5728\u7ebf\u5e73\u53f0\u5c06\u4e58\u5ba2\u4e0e\u79c1\u5bb6\u8f66\u53f8\u673a\u8054\u7cfb\u8d77\u6765\u7684\u4e58\u8f66\u670d\u52a1\u5728\u4e16\u754c\u8303\u56f4\u5185\u975e\u5e38\u6d41\u884c\u3002\u672c\u6587\u9996\u6b21\u4ecb\u7ecd\u4e86\u4e2d\u56fd\u5317\u4eac\u53eb\u8f66\u53f8\u673a\u7684\u591a\u65e5\u65c5\u884c\u8ba2\u5355\u6570\u636e\uff0c\u5e76\u4ece\u533a\u57df\u548c\u9a7e\u9a76\u5458\u7684\u89d2\u5ea6\u63cf\u7ed8\u4e86ride-hailing.pdf\u7684\u51fa\u884c\u65b9\u5f0f\u3002\u4ece\u533a\u57df\u89d2\u5ea6\u8fdb\u884c\u7684\u5206\u6790\u6709\u52a9\u4e8e\u7406\u89e3\u4e58\u8f66\u9700\u6c42\u7684\u65f6\u7a7a\u6d41\u52a8\uff0c\u800c\u4ece\u9a7e\u9a76\u5458\u89d2\u5ea6\u8fdb\u884c\u7684\u5206\u6790\u5219\u4f53\u73b0\u4e86\u4e58\u8f66\u9a7e\u9a76\u5458\u5728\u63d0\u4f9b\u4e58\u8f66\u670d\u52a1\u65b9\u9762\u7684\u504f\u597d\u3002\u83b7\u5f97\u4e86\u4e00\u7cfb\u5217\u53d1\u73b0\uff0c\u4f8b\u5982\uff0c\u5c31\u6d3b\u52a8\u533a\u57df\u548c\u5de5\u4f5c\u65f6\u95f4\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u800c\u8a00\uff0c\u5bf9\u4e58\u8f66\u9700\u6c42\u548c\u4e58\u8f66\u53f8\u673a\u4e24\u7c7b\u7c7b\u522b\u7684\u6536\u7f29\u548c\u6269\u5c55\u8fc7\u7a0b\u7684\u89c2\u5bdf\u3002\u8fd9\u4e9b\u53d1\u73b0\u6709\u52a9\u4e8e\u4eba\u4eec\u4e86\u89e3\u4e58\u8f66\u6d3b\u52a8\uff0c\u9884\u6d4b\u4e58\u8f66\u9700\u6c42\uff0c\u6a21\u62df\u4e58\u8f66\u9a7e\u9a76\u5458\u7684\u504f\u597d\u4ee5\u53ca\u7ba1\u7406\u4e58\u8f66\u670d\u52a1\u3002<\/span><\/section>\n

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    \u6709\u754c\u5e73\u9762\u5730\u6708\u7cfb\u4e2d\u7684\u5e8f-<\/strong><\/span><\/p>\n

    \u6df7\u6c8c\u5e8f\u548c\u4e0d\u53d8\u6d41\u5f62<\/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

    Order-chaos-order and invariant manifolds in the bounded planar Earth-Moon system<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.13111<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Vitor M. de Oliveira,Priscilla A. Sousa-Silva,Iber\u00ea L. Caldas<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>I<\/span>n this work, we investigate the Earth-Moon system, as modeled by the planar<\/span> circular restricted three-body problem, and relate its dynamical properties to the underlying structure associated to specific invariant manifolds. We consider a range of Jacobi constant values for which the neck around the Lagrangian point<\/span>L1 is always open but the orbits are bounded due to Hill stability. First, we show that the system displays three different dynamical scenarios in a neighborhood of the Moon: two mixed ones, with regular and chaotic orbits, and an almost entirely chaotic one in between. We then analyze the transitions between these scenarios using the Monodromy matrix theory and determine that they are given by two specific types of bifurcations. After that, we illustrate how the phase space configurations, particularly the shapes of stability regions and stickiness, are intrinsically related to the hyperbolic invariant manifolds of the Lyapunov orbits around <\/span>L1 and also to the ones of some particular unstable periodic orbits. Lastly, we define transit time in a manner which is useful to depict dynamical trapping and show that the traced geometrical structures are also connected to the transport properties of the system.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u7531\u5e73\u9762\u5706\u5f62\u9650\u5236\u7684\u5730\u6708\u7cfb\u4e09\u4f53\u95ee\u9898\u6a21\u578b\uff0c\u5e76\u5c06\u5176\u52a8\u529b\u5b66\u6027\u8d28\u4e0e\u7279\u5b9a\u4e0d\u53d8\u6d41\u5f62\u7684\u5e95\u5c42\u7ed3\u6784\u8054\u7cfb\u8d77\u6765\u3002\u6211\u4eec\u8003\u8651\u4e00\u7cfb\u5217\u7684 Jacobi \u5e38\u6570\u503c\uff0c\u8fd9\u4e9b\u5e38\u6570\u662f\u56f4\u7ed5\u7740\u62c9\u683c\u6717\u65e5\u70b9\u7684<\/span>L1 \u603b\u662f\u5f00\u653e\u7684\uff0c\u4f46\u8f68\u9053\u662f\u6709\u754c\u7684\uff0c\u7531\u4e8e\u5e0c\u5c14\u7a33\u5b9a\u6027\u3002\u9996\u5148\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u8be5\u7cfb\u7edf\u5728\u6708\u7403\u9644\u8fd1\u5448\u73b0\u51fa\u4e09\u79cd\u4e0d\u540c\u7684\u52a8\u529b\u5b66\u573a\u666f: \u4e24\u79cd\u6df7\u5408\u7684\u573a\u666f\uff0c\u6709\u89c4\u5219\u548c\u6df7\u6c8c\u7684\u8f68\u9053\uff0c\u4ee5\u53ca\u4e00\u79cd\u51e0\u4e4e\u5b8c\u5168\u6df7\u6c8c\u7684\u573a\u666f\u3002\u7136\u540e\uff0c\u6211\u4eec\u7528\u5355\u503c\u77e9\u9635\u7406\u8bba\u5206\u6790\u4e86\u8fd9\u4e9b\u573a\u666f\u4e4b\u95f4\u7684\u8f6c\u6362\uff0c\u5e76\u786e\u5b9a\u5b83\u4eec\u662f\u7531\u4e24\u79cd\u7279\u5b9a\u7c7b\u578b\u7684\u5206\u5c94\u7ed9\u51fa\u7684\u3002\u7136\u540e\uff0c\u6211\u4eec\u8bf4\u660e\u4e86\u76f8\u7a7a\u95f4\u6784\u578b\uff0c\u7279\u522b\u662f\u7a33\u5b9a\u533a\u57df\u7684\u5f62\u72b6\u548c\u7c98\u6027\uff0c\u4e0e\u5468\u56f4 Lyapunov \u8f68\u9053\u7684\u53cc\u66f2\u4e0d\u53d8\u6d41\u5f62\u6709\u7740\u5185\u5728\u7684\u8054\u7cfb<\/span>L1 \u8fd8\u6709\u4e00\u4e9b\u7279\u6b8a\u7684\u4e0d\u7a33\u5b9a\u5468\u671f\u8f68\u9053\u3002\u6700\u540e\uff0c\u6211\u4eec\u4ee5\u4e00\u79cd\u6709\u7528\u7684\u65b9\u5f0f\u5b9a\u4e49\u6e21\u8d8a\u65f6\u95f4\uff0c\u4ee5\u63cf\u8ff0\u52a8\u529b\u5b66\u9677\u9631\uff0c\u5e76\u8868\u660e\u8ddf\u8e2a\u7684\u51e0\u4f55\u7ed3\u6784\u4e5f\u8fde\u63a5\u5230\u7cfb\u7edf\u7684\u8f93\u8fd0\u6027\u8d28\u3002<\/span><\/section>\n

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    \u538b\u7f29\u76f8\u7a7a\u95f4\u68c0\u6d4b<\/strong><\/span><\/p>\n

    \u975e\u7ebf\u6027\u52a8\u6001\u7cfb\u7edf\u7684\u72b6\u6001\u53d8\u5316<\/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

    Compressing phase space detects state changes in nonlinear dynamical systems<\/span><\/h2>\n

    \u5730\u5740<\/span><\/strong>\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12842<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Valeria d’Andrea,Manlio De Domenico<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Equations governing the nonlinear dynamics of complex systems are usually<\/span> unknown and indirect methods are used to reconstruct their manifolds. In turn, they depend on embedding parameters requiring other methods and long temporal sequences to be accurate. In this paper, we show that an optimal reconstruction can be achieved by lossless compression of system’s time course, providing a self-consistent analysis of its dynamics and a measure of its complexity, even for short sequences. Our measure of complexity detects system’s state changes such as weak synchronization phenomena, characterizing many systems, in one step, integrating results from Lyapunov and fractal analysis.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u63a7\u5236\u590d\u6742\u7cfb\u7edf\u975e\u7ebf\u6027\u52a8\u529b\u5b66\u7684\u65b9\u7a0b\u901a\u5e38\u662f\u672a\u77e5\u7684\uff0c\u7528\u95f4\u63a5\u7684\u65b9\u6cd5\u91cd\u5efa\u5b83\u4eec\u7684\u6d41\u5f62\u3002\u53cd\u8fc7\u6765\uff0c\u5b83\u4eec\u4f9d\u8d56\u4e8e\u9700\u8981\u5176\u4ed6\u65b9\u6cd5\u548c\u957f\u65f6\u95f4\u5e8f\u5217\u624d\u80fd\u51c6\u786e\u7684\u5d4c\u5165\u53c2\u6570\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u4e00\u4e2a\u6700\u4f73\u7684\u91cd\u5efa\u53ef\u4ee5\u901a\u8fc7\u65e0\u635f\u6570\u636e\u538b\u7f29\u7684\u7cfb\u7edf\u7684\u65f6\u95f4\u8fc7\u7a0b\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u81ea\u6d3d\u7684\u52a8\u6001\u5206\u6790\u548c\u5176\u590d\u6742\u6027\u7684\u63aa\u65bd\uff0c\u5373\u4f7f\u5bf9\u77ed\u5e8f\u5217\u3002\u6211\u4eec\u7684\u590d\u6742\u6027\u5ea6\u91cf\u7cfb\u7edf\u7684\u72b6\u6001\u53d8\u5316\uff0c\u5982\u5f31\u540c\u6b65\u73b0\u8c61\uff0c\u8868\u5f81\u591a\u4e2a\u7cfb\u7edf\uff0c\u5728\u4e00\u4e2a\u6b65\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u9aa4\u4e2d\uff0c\u7ed3\u5408\u674e\u96c5\u666e\u8bfa\u592b\u548c\u5206\u5f62\u5206\u6790\u7684\u7ed3\u679c\u3002<\/span><\/section>\n

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    \u57284\u5ea6\u4e34\u754c\u70b9\u5730\u56fe\u4e2d\u5b58\u5728\u500d\u5468\u671f<\/strong><\/span><\/p>\n

    \u91cd\u6574\u5316\u4e0d\u52a8\u70b9\u7684\u4e25\u683c\u7535\u8111\u534f\u52a9\u8bc1\u660e<\/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

    Rigorous computer-assisted proof for existence of period doubling renormalisation fixed points in maps with critical point of degree 4<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13127<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Andrew D Burbanks,Andrew H Osbaldestin,Judi A Thurlby<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We gain tight rigorous bounds on the renormalisation fixed point for period doubling in families of unimodal maps with degree<\/span>4 <\/span>4<\/span><\/mjx-assistive-mml><\/mjx-container> critical point. We prove that the fixed point is hyperbolic and use a contraction mapping argument to bound essential eigenfunctions and eigenvalues for the linearisation and for the scaling of additive noise. We find analytic extensions of the fixed point function to larger domains. We use multi-precision arithmetic with rigorous directed rounding to bound operations in a space of analytic functions yielding tight bounds on power series and universal constants.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u5f97\u5230\u4e86\u5355\u5cf0\u5730\u56fe\u65cf\u4e2d\u5468\u671f\u500d\u7684\u91cd\u6574\u5316\u4e0d\u52a8\u70b9\u7684\u4e25\u683c\u754c<\/span>4 \u4e34\u754c\u70b9\u3002\u6211\u4eec\u8bc1\u660e\u4e86\u8fd9\u4e2a\u4e0d\u52a8\u70b9\u662f\u53cc\u66f2\u578b\u7684\uff0c\u5e76\u4e14\u4f7f\u7528\u538b\u7f29\u6620\u5c04\u53d8\u91cf\u6765\u7ea6\u675f\u57fa\u672c\u7279\u5f81\u51fd\u6570\u548c\u7279\u5f81\u503c\u4ee5\u5b9e\u73b0\u7ebf\u6027\u5316\u548c\u52a0\u6027\u566a\u58f0\u7684\u7f29\u653e\u3002\u6211\u4eec\u627e\u5230\u4e86\u4e0d\u52a8\u70b9\u51fd\u6570\u5728\u66f4\u5927\u533a\u57df\u4e0a\u7684\u89e3\u6790\u6269\u5f20\u3002\u6211\u4eec\u5728\u89e3\u6790\u51fd\u6570\u7a7a\u95f4\u4e2d\u4f7f\u7528\u4e25\u683c\u6709\u5411\u820d\u5165\u7684\u591a\u7cbe\u5ea6\u7b97\u672f\uff0c\u4ea7\u751f\u5e42\u7ea7\u6570\u548c\u666e\u200d\u200d\u9002\u5e38\u6570\u7684\u4e25\u683c\u754c\u3002<\/span><\/section>\n

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    \u673a\u5668\u5b66\u4e60\u4e3b\u52a8\u5411\u5217\u76f8\u6d41\u4f53\u529b\u5b66<\/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

    Machine learning active-nematic hydrodynamics<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13203<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Jonathan Colen,Ming Han,Rui Zhang,Steven A. Redford,Linnea M. Lemma,Link Morgan,Paul V. Ruijgrok,Raymond Adkins,Zev Bryant,Zvonimir Dogic,Margaret L. Gardel,Juan J. De Pablo,Vincenzo Vitelli<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood. Here, we use active nematics to demonstrate that neural networks can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin and actin-myosin experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6c34\u52a8\u529b\u5b66\u7406\u8bba\u7528\u4e00\u4e9b\u5b8f\u89c2\u53c2\u6570\u6709\u6548\u5730\u63cf\u8ff0\u4e86\u591a\u4f53\u7cfb\u7edf\u5931\u53bb\u5e73\u8861\u7684\u60c5\u51b5\u3002\u7136\u800c\uff0c\u8fd9\u6837\u7684\u6d41\u4f53\u52a8\u529b\u5b66\u53c2\u6570\u5f88\u96be\u4ece\u663e\u5fae\u955c\u5f97\u5230\u3002\u8fd9\u79cd\u6311\u6218\u5f88\u5c11\u6bd4\u5728\u6d3b\u52a8\u7269\u8d28\u4e2d\u66f4\u660e\u663e\uff0c\u56e0\u4e3a\u5bf9\u4e8e\u80fd\u91cf\u7ea7\u8054\u673a\u5236\u8d1f\u8d23\u81ea\u4e3b\u7684\u5927\u89c4\u6a21\u52a8\u6001\u7684\u4e86\u89e3\u5f88\u5c11\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528\u4e3b\u52a8\u5411\u5217\u76f8\u5b66\u6765\u8bc1\u660e\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u76f4\u63a5\u4ece\u5b9e\u9a8c\u4e2d\u63d0\u53d6\u6c34\u52a8\u529b\u53c2\u6570\u7684\u65f6\u7a7a\u53d8\u5316\u3002\u6211\u4eec\u7684\u7b97\u6cd5\u5206\u6790\u5fae\u7ba1\u6fc0\u52a8\u7d20\u548c\u808c\u52a8\u86cb\u767d\u808c\u7403\u86cb\u767d\u5b9e\u9a8c\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u95ee\u9898\u3002\u4e0e\u73b0\u6709\u7684\u65b9\u6cd5\u4e0d\u540c\uff0c\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u786e\u5b9a\u591a\u4e2a\u53c2\u6570\uff0c\u5982\u6d3b\u6027\u548c\u5f39\u6027\u5e38\u6570\u5982\u4f55\u53d8\u5316\u7684 ATP \u548c\u8fd0\u52a8\u6d53\u5ea6\u3002\u6b64\u5916\uff0c\u6211\u4eec\u53ef\u4ee5\u9884\u6d4b\u8fd9\u4e9b\u6df7\u6c8c\u591a\u4f53\u7cfb\u7edf\u7684\u53d1\u5c55\u6f14\u53d8\u5b8c\u5168\u4ece\u56fe\u50cf\u5e8f\u5217\u7684\u8fc7\u53bb\u7ed3\u5408\u81ea\u52a8\u7f16\u7801\u5668\u548c\u5faa\u73af\u7f51\u7edc\u4e0e\u5269\u4f59\u7ed3\u6784\u3002\u6211\u4eec\u7684\u7814\u7a76\u4e3a\u4eba\u5de5\u667a\u80fd\u89d2\u8272\u5851\u9020\u548c\u5728\u4e0d\u540c\u7684\u7269\u7406\u548c\u751f\u7269\u7cfb\u7edf\u4e2d\u63a7\u5236\u8026\u5408\u7684\u6df7\u6c8c\u9886\u57df\u94fa\u5e73\u4e86\u9053\u8def\uff0c\u5373\u4f7f\u6ca1\u6709\u6f5c\u5728\u7684\u52a8\u529b\u5b66\u77e5\u8bc6\u5b58\u5728\u3002\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d<\/span><\/section>\n

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    \u5177\u6709\u5468\u671f\u6027\u5e94\u53d8\u7684\u80ba\u6ce1\u6a21\u62df\u7269<\/strong><\/span><\/p>\n

    \u53ca\u5176\u5bf9\u7ec6\u80de\u5c42\u5f62\u6210\u7684\u5f71\u54cd<\/strong><\/span><\/p>\n

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

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

    Alveolar mimics with periodic strain and its effect on the cell layer formation<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13141<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Milad Radiom,Yong He,Juan Peng,Armelle Baeza-Squiban,Jean-Franccois Berret,Yong Chen<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We report on the development of a new model of alveolar air-tissue interface on a chip. The model consists of an array of suspended hexagonal monolayers of gelatin nanofibers supported by microframes and a microfluidic device for the patch integration. The suspended monolayers are deformed to a central displacement of 40-80 um at the air-liquid interface by application of air pressure in the range of 200-1000 Pa. With respect to the diameter of the monolayers that is 500 um, this displacement corresponds to a linear strain of 2-10% in agreement with the physiological strain range in the lung alveoli. The culture of A549 cells on the monolayers for an incubation time 1-3 days showed viability in the model. We exerted a periodic strain of 5% at a frequency of 0.2 Hz during 1 hour to the cells. We found that the cells were strongly coupled to the nanofibers, but the strain reduced the coupling and induced remodeling of the actin cytoskeleton, which led to a better tissue formation. Our model can serve as a versatile tool in lung investigations such as in inhalation toxicology and therapy.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u62a5\u544a\u4e86\u4e00\u79cd\u65b0\u7684\u80ba\u6ce1\u6c14-\u7ec4\u7ec7\u754c\u9762\u6a21\u578b\u82af\u7247\u7684\u5f00\u53d1\u3002\u8be5\u6a21\u578b\u7531\u4e00\u7cfb\u5217\u60ac\u6d6e\u7684\u516d\u89d2\u5f62\u660e\u80f6\u7eb3\u7c73\u7ea4\u7ef4\u5355\u5206\u5b50\u819c\u548c\u4e00\u4e2a\u5fae\u6d41\u63a7\u5668\u4ef6\u7ec4\u6210\u3002\u5728200\u30fc1000 pa \u7684\u6c14\u538b\u4f5c\u7528\u4e0b\uff0c\u60ac\u6d6e\u5355\u5206\u5b50\u819c\u5728\u6c14\u6db2\u754c\u9762\u4e0a\u53d1\u751f\u4e8640\u30fc80\u5fae\u7c73\u7684\u4e2d\u5fc3\u4f4d\u79fb\u3002\u5bf9\u4e8e\u5355\u5c42\u819c\u7684\u76f4\u5f84\u4e3a500\u5fae\u7c73\uff0c\u8fd9\u4e2a\u4f4d\u79fb\u76f8\u5f53\u4e8e2-10% \u7684\u7ebf\u6027\u5e94\u53d8\uff0c\u4e0e\u80ba\u6ce1\u5185\u7684\u751f\u7406\u5e94\u53d8\u8303\u56f4\u76f8\u4e00\u81f4\u3002\u5728\u5355\u5c42\u57f9\u517b1-3\u5929\u7684 A549\u7ec6\u80de\u5728\u6a21\u578b\u4e2d\u8868\u73b0\u51fa\u6d3b\u6027\u3002\u6211\u4eec\u57281\u5c0f\u65f6\u5185\u4ee50.2\u8d6b\u5179\u7684\u9891\u7387\u5411\u7ec6\u80de\u65bd\u52a05% \u7684\u5468\u671f\u6027\u5e94\u53d8\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u7ec6\u80de\u4e0e\u7eb3\u7c73\u7ea4\u7ef4\u5f3a\u70c8\u8026\u5408\uff0c\u4f46\u5e94\u53d8\u964d\u4f4e\u4e86\u8026\u5408\u548c\u808c\u52a8\u86cb\u767d\u7ec6\u80de\u9aa8\u67b6\u7684\u91cd\u5851\uff0c\u4ece\u800c\u5bfc\u81f4\u66f4\u597d\u7684\u7ec4\u7ec7\u5f62\u6210\u3002\u6211\u4eec\u7684\u6a21\u578b\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u4e2a\u591a\u529f\u80fd\u7684\u5de5\u5177\u5728\u80ba\u90e8\u8c03\u67e5\uff0c\u5982\u5728\u5438\u5165\u6bd2\u7406\u5b66\u548c\u6cbb\u7597\u3002<\/span><\/section>\n

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

    \n
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    <\/p>\n

    \u590d\u6742\u4e09\u7ef4\u5730\u5f62\u4e2d<\/strong><\/span><\/p>\n

    \u8fd0\u52a8\u8dc3\u8fc1\u7684\u80fd\u91cf\u666f\u89c2\u65b9\u6cd5<\/strong><\/span><\/p>\n

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

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

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

    An energy landscape approach to locomotor transitions in complex 3D terrain<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12717<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Ratan Othayoth,George Thoms,Chen Li<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near-steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex 3-D terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3-D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely towards lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free energy landscapes for multi-pathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multi-pathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multi-scale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u81ea\u7136\u754c\u4e2d\uff0c\u6709\u6548\u7684\u8fd0\u52a8\u662f\u901a\u8fc7\u8de8\u8d8a\u591a\u79cd\u6a21\u5f0f(\u4f8b\u5982\uff0c\u8d70\u8def\u3001\u8dd1\u6b65\u3001\u722c\u5c71)\u6765\u5b9e\u73b0\u7684\u3002\u5c3d\u7ba1\u5982\u6b64\uff0c\u5173\u4e8e\u9646\u5730\u8fd0\u52a8\u7684\u66f4\u591a\u7684\u673a\u68b0\u7406\u89e3\u662f\u5173\u4e8e\u5982\u4f55\u5728\u5355\u4e00\u6a21\u5f0f\u4e0b\u4ea7\u751f\u548c\u7a33\u5b9a\u5468\u56f4\u7684\u8fd1\u7a33\u6001\u8fd0\u52a8\u3002\u5bf9\u4e8e\u590d\u6742\u5730\u5f62\u4e0b\u7684\u7269\u7406\u4f5c\u7528\u662f\u5982\u4f55\u4ea7\u751f\u8fd0\u52a8\u8fc7\u6e21\u7684\uff0c\u6211\u4eec\u77e5\u4e4b\u751a\u5c11\u3002\u56e0\u6b64\uff0c\u673a\u5668\u4eba\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f9d\u8d56\u4e8e\u51e0\u4f55\u5730\u56fe\u6765\u907f\u5f00\u969c\u788d\u7269\uff0c\u800c\u4e0d\u662f\u7a7f\u8d8a\u5b83\u4eec\u3002\u6700\u8fd1\u7684\u7814\u7a76\u8868\u660e\uff0c\u5728\u590d\u6742\u7684\u4e09\u7ef4\u5730\u5f62\u4e2d\uff0c\u8fd0\u52a8\u7684\u8f6c\u53d8\u662f\u4ee5\u6982\u7387\u7684\u65b9\u5f0f\u901a\u8fc7\u591a\u79cd\u9014\u5f84\u53d1\u751f\u7684\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u80fd\u6e90\u666f\u89c2\u65b9\u6cd5\u9610\u660e\u4e86\u57fa\u672c\u7684\u7269\u7406\u539f\u7406\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u52a8\u7269\u548c\u673a\u5668\u4eba\u81ea\u6211\u63a8\u8fdb\u7a7f\u8d8a\u590d\u6742\u7684\u4e09\u7ef4\u5730\u5f62\u65f6\u7684\u8fd0\u52a8\u8f6c\u53d8\u4e0e\u6f5c\u5728\u80fd\u91cf\u666f\u89c2\u4e2d\u8de8\u8d8a\u969c\u788d\u7684\u8f6c\u53d8\u76f8\u5bf9\u5e94\u3002\u8fd0\u52a8\u6a21\u5f0f\u88ab\u52bf\u80fd\u969c\u788d\u5206\u9694\u7684\u666f\u89c2\u76c6\u5730\u6240\u5438\u5f15\u3002\u7531\u632f\u8361\u81ea\u63a8\u8fdb\u4ea7\u751f\u7684\u52a8\u80fd\u6ce2\u52a8\u5e2e\u52a9\u7cfb\u7edf\u968f\u673a\u5730\u8131\u79bb\u4e00\u4e2a\u6c34\u6c60\u5230\u8fbe\u53e6\u4e00\u4e2a\u6c34\u6c60\u8fdb\u884c\u8fc7\u6e21\u3002\u9003\u9038\u66f4\u6709\u53ef\u80fd\u5411\u8f83\u4f4e\u7684\u969c\u788d\u65b9\u5411\u3002\u8fd9\u4e9b\u539f\u7406\u4e0e\u90a3\u4e9b\u63a5\u8fd1\u5e73\u8861\u7684\u5fae\u89c2\u7cfb\u7edf\u60ca\u4eba\u5730\u76f8\u4f3c\u3002\u7c7b\u4f3c\u4e8e\u86cb\u767d\u8d28\u591a\u9014\u5f84\u6298\u53e0\u8f6c\u6362\u7684\u81ea\u7531\u80fd\u666f\u89c2\uff0c\u6211\u4eec\u4ece\u7b2c\u4e00\u6027\u539f\u7406\u51fa\u53d1\u7684\u80fd\u91cf\u666f\u89c2\u65b9\u6cd5\u662f\u590d\u6742\u5730\u5f62\u4e2d\u591a\u9014\u5f84\u8fd0\u52a8\u8f6c\u6362\u7684\u7edf\u8ba1\u7269\u7406\u5b66\u7406\u8bba\u7684\u5f00\u7aef\u3002\u8fd9\u4e0d\u4ec5\u6709\u52a9\u4e8e\u7406\u89e3\u52a8\u7269\u884c\u4e3a\u7684\u7ec4\u7ec7\u662f\u5982\u4f55\u4ece\u5b83\u4eec\u7684\u795e\u7ecf\u7cfb\u7edf\u3001\u673a\u68b0\u7cfb\u7edf\u548c\u7269\u7406\u73af\u5883\u4e4b\u95f4\u7684\u591a\u5c3a\u5ea6\u76f8\u4e92\u4f5c\u7528\u4e2d\u4ea7\u751f\u7684\uff0c\u800c\u4e14\u8fd8\u53ef\u4ee5\u6307\u5bfc\u673a\u5668\u4eba\u8bbe\u8ba1\u3001\u63a7\u5236\u548c\u89c4\u5212\u5927\u578b\u7684\u3001\u96be\u4ee5\u5904\u7406\u7684\u8fd0\u52a8-\u5730\u5f62\u53c2\u6570\u7a7a\u95f4\uff0c\u4ece\u800c\u5728\u73b0\u5b9e\u4e16\u754c\u4e2d\u4ea7\u751f\u5f3a\u5927\u7684\u8fd0\u52a8\u8fc7\u6e21\u3002<\/span><\/section>\n

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    \u7528\u6709\u6548\u5a92\u4ecb\u7406\u8bba\u89e3\u8bfb\u5168\u606f\u5206\u5b50\u7ed3\u5408\u6d4b\u5b9a<\/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

    Interpreting Holographic Molecular Binding Assays with Effective Medium Theory<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13134<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Lauren E. Altman,David G. Grier<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Holographic molecular binding assays use holographic video microscopy to directly detect molecules binding to the surfaces of micrometer-scale colloidal beads by monitoring associated changes in the beads’ light-scattering properties. Holograms of individual spheres are analyzed by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Each fit yields an estimate of a probe bead’s diameter and refractive index with sufficient precision to watch the beads grow as molecules bind. Rather than modeling the molecular-scale coating, however, these fits use effective medium theory, treating the coated sphere as if it were homogeneous. This effective-sphere analysis is rapid and numerically robust and so is useful for practical implementations of label-free immunoassays. Here, we assess how effective-sphere properties reflect the properties of molecular-scale coatings by modeling coated spheres with the discrete-dipole approximation and analyzing their holograms with the effective-sphere model.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5168\u606f\u5206\u5b50\u7ed3\u5408\u5206\u6790\u5229\u7528\u5168\u606f\u89c6\u9891\u663e\u5fae\u955c\uff0c\u901a\u8fc7\u76d1\u6d4b\u80f6\u73e0\u5149\u6563\u5c04\u7279\u6027\u7684\u76f8\u5173\u53d8\u5316\uff0c\u76f4\u63a5\u63a2\u6d4b\u4e0e\u5fae\u7c73\u7ea7\u80f6\u73e0\u8868\u9762\u7ed3\u5408\u7684\u5206\u5b50\u3002\u57fa\u4e8e\u6d1b\u4f26\u5179-\u7c73\u6c0f\u7406\u8bba\uff0c\u901a\u8fc7\u62df\u5408\u751f\u6210\u6a21\u578b\u5206\u6790\u4e86\u5355\u4e2a\u7403\u9762\u7684\u5168\u606f\u56fe\uff0c\u5f97\u5230\u4e86\u7403\u9762\u5168\u606f\u56fe\u7684\u5149\u6563\u5c04\u3002\u6bcf\u6b21\u8bd5\u9a8c\u90fd\u4f1a\u5f97\u5230\u4e00\u4e2a\u63a2\u9488\u73e0\u5b50\u7684\u76f4\u5f84\u548c\u6298\u5c04\u7387\u7684\u4f30\u8ba1\u503c\uff0c\u8fd9\u4e2a\u4f30\u8ba1\u503c\u8db3\u591f\u7cbe\u786e\uff0c\u53ef\u4ee5\u770b\u5230\u63a2\u9488\u73e0\u5b50\u968f\u7740\u5206\u5b50\u7684\u7ed3\u5408\u800c\u751f\u957f\u3002\u4e0e\u5176\u6a21\u62df\u5206\u5b50\u5c3a\u5ea6\u7684\u6d82\u5c42\uff0c\u4e0d\u8fc7\uff0c\u8fd9\u4e9b\u9002\u5408\u4f7f\u7528\u6709\u6548\u4ecb\u8d28\u7406\u8bba\uff0c\u5bf9\u5f85\u6d82\u5c42\u7403\u4f53\u597d\u50cf\u5b83\u662f\u5747\u5300\u7684\u3002\u8fd9\u79cd\u6709\u6548\u8303\u56f4\u7684\u5206\u6790\u662f\u5feb\u901f\u548c\u6570\u503c\u7a33\u5b9a\u7684\uff0c\u56e0\u6b64\u5bf9\u4e8e\u5b9e\u9645\u5b9e\u65bd\u65e0\u6807\u8bb0\u514d\u75ab\u5206\u6790\u662f\u6709\u7528\u7684\u3002\u672c\u6587\u901a\u8fc7\u7528\u79bb\u6563\u5076\u6781\u5b50\u8fd1\u4f3c\u6a21\u62df\u6d82\u5c42\u7403\u4f53\uff0c\u5e76\u7528\u6709\u6548\u7403\u6a21\u578b\u5206\u6790\u6d82\u5c42\u7403\u4f53\u7684\u5168\u606f\u56fe\uff0c\u8bc4\u4ef7\u4e86\u6709\u6548\u7403\u4f53\u7684\u6027\u8d28\u5982\u4f55\u53cd\u6620\u5206\u5b50\u5c3a\u5ea6\u6d82\u5c42\u7684\u6027\u8d28\u3002<\/span><\/section>\n

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    \n
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    \u5173\u4e8e\u884c\u4e3a\u8702\u7fa4\u7684\u6570\u5b66\u7406\u8bba<\/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

    Towards a mathematical theory of behavioral swarms<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12932<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Nicola Bellomo,Seung-Yeal Ha,Nisrine Outada<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>This paper presents a unified mathematical theory of swarms where the dynamics of social behaviors interacts with the mechanical dynamics of self-propelled particles. The term behavioral swarms is introduced to characterize the specific object of the theory which is subsequently followed by applications. As concrete examples for our unified approach, we show that several Cucker-Smale type models with internal variables fall down to our framework. Subsequently the modeling goes beyond the Cucker-Smale approach and looks ahead to research perspectives.<\/span><\/section>\n
    \u6458\u8981\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a\u5173\u4e8e\u7fa4\u7684\u7edf\u4e00\u6570\u5b66\u7406\u8bba\uff0c\u5176\u4e2d\u793e\u4f1a\u884c\u4e3a\u7684\u52a8\u529b\u5b66\u4e0e\u81ea\u9a71\u52a8\u7c92\u5b50\u7684\u529b\u5b66\u52a8\u529b\u5b66\u76f8\u4e92\u4f5c\u7528\u3002\u884c\u4e3a\u8702\u7fa4\u8fd9\u4e2a\u672f\u8bed\u88ab\u5f15\u5165\u6765\u63cf\u8ff0\u7406\u8bba\u7684\u7279\u5b9a\u5bf9\u8c61\uff0c\u968f\u540e\u88ab\u5e94\u7528\u3002\u4f5c\u4e3a\u6211\u4eec\u7edf\u4e00\u65b9\u6cd5\u7684\u5177\u4f53\u4f8b\u5b50\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u4e00\u4e9b\u5185\u90e8\u53d8\u91cf\u7684 Cucker-Smale \u7c7b\u578b\u6a21\u578b\u843d\u5230\u6211\u4eec\u7684\u6846\u67b6\u4e2d\u3002\u968f\u540e\u7684\u5efa\u6a21\u8d85\u8d8a\u4e86 Cucker-Smale \u65b9\u6cd5\uff0c\u5e76\u5c55\u671b\u4e86\u7814\u7a76\u524d\u666f\u3002<\/span><\/section>\n

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

    \u5177\u6709\u5206\u914d\u89c4\u5219\u7684\u6811\u7684\u751f\u957f: <\/strong><\/span><\/p>\n

    \u7b2c2\u90e8\u5206\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

    Growth of a tree with allocations rules: Part 2 Dynamics<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12944<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Olivier Bui,Xavier Leoncini<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Following up on a previous work we examine a model of transportation network in some source-sink flow paradigm subjected to growth and resource allocation. The model is inspired from plants, and we add rules and factors that are analogous to what plants are subjected to. We study how different resource allocation schemes affect the tree and how the schemes interact with additional factors such as embedding the network into a 3D space and applying gravity or shading. The different outcomes are discussed. PACS. 05.45.-a Nonlinear dynamics and chaos-05.65.+b Self-organized systems<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u4ee5\u524d\u5de5\u4f5c\u7684\u57fa\u7840\u4e0a\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u4e00\u4e2a\u5728\u6e90\u6c47\u6d41\u8303\u5f0f\u4e0b\u7684\u4ea4\u901a\u7f51\u7edc\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u53d7\u5230\u589e\u957f\u548c\u8d44\u6e90\u5206\u914d\u7684\u5f71\u54cd\u3002\u8fd9\u4e2a\u6a21\u578b\u7684\u7075\u611f\u6765\u81ea\u4e8e\u690d\u7269\uff0c\u6211\u4eec\u6dfb\u52a0\u4e86\u4e00\u4e9b\u89c4\u5219\u548c\u56e0\u7d20\uff0c\u8fd9\u4e9b\u89c4\u5219\u548c\u56e0\u7d20\u7c7b\u4f3c\u4e8e\u690d\u7269\u6240\u53d7\u5230\u7684\u5f71\u54cd\u3002\u6211\u4eec\u7814\u7a76\u4e86\u4e0d\u540c\u7684\u8d44\u6e90\u5206\u914d\u65b9\u6848\u5982\u4f55\u5f71\u54cd\u6811\uff0c\u4ee5\u53ca\u8fd9\u4e9b\u65b9\u6848\u5982\u4f55\u4e0e\u5176\u4ed6\u56e0\u7d20\u76f8\u4e92\u4f5c\u7528\uff0c\u5982\u5d4c\u5165\u7f51\u7edc\u5230\u4e00\u4e2a\u4e09\u7ef4\u7a7a\u95f4\u548c\u5e94\u7528\u91cd\u529b\u6216\u9634\u5f71\u3002\u8ba8\u8bba\u4e86\u4e0d\u540c\u7684\u7ed3\u679c\u3002\u533b\u5b66\u5f71\u50cf\u5b58\u50a8\u7cfb\u7edf\u300205.45.-a \u975e\u7ebf\u6027\u52a8\u529b\u5b66\u4e0e\u6df7\u6c8c -05.65\u3002+ b \u81ea\u7ec4\u7ec7\u7cfb\u7edf\u3002<\/span><\/section>\n

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    \u53bf\u7ea7\u81ea\u9002\u5e94\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u9884\u6d4b\u6a21\u578b:<\/strong><\/span><\/p>\n

    \u5206\u6790\u4e0e\u6539\u8fdb<\/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

    Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12617<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Stewart W Doe,Tyler Russell Seekins,David Fitzpatrick,Dawsin Blanchard,Salimeh Yasaei Sekeh<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Accurately forecasting county level COVID-19 confirmed cases is crucial to optimizing medical resources. Forecasting emerging outbreaks pose a particular challenge because many existing forecasting techniques learn from historical seasons trends. Recurrent neural networks (RNNs) with LSTM-based cells are a logical choice of model due to their ability to learn temporal dynamics. In this paper, we adapt the state and county level influenza model, TDEFSI-LONLY, proposed in Wang et a. [l2020] to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We analyze the two week ahead forecasting capabilities of the TDEFSI-LONLY model with combinations of regularization techniques. Effective training of the TDEFSI-LONLY model requires data augmentation, to overcome this challenge we utilize an SEIR model and present an inter-county mixing extension to this model to simulate sufficient training data. Further, we propose an alternate forecast model, {it County Level Epidemiological Inference Recurrent Network} (alg{}) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern and utilizes a time distributed dense layer to learn individual county confirmed case changes each day for a two weeks forecast. We show that the best, worst, and median state forecasts made using CLEIR-Net model are respectively New York, South Carolina, and Montana.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u51c6\u786e\u9884\u6d4b\u53bf\u7ea7\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u786e\u8bca\u75c5\u4f8b\u5bf9\u4e8e\u4f18\u5316\u533b\u7597\u8d44\u6e90\u81f3\u5173\u91cd\u8981\u3002\u7531\u4e8e\u8bb8\u591a\u73b0\u6709\u7684\u9884\u6d4b\u6280\u672f\u501f\u9274\u4e86\u5386\u53f2\u5b63\u8282\u8d8b\u52bf\uff0c\u56e0\u6b64\u9884\u6d4b\u65b0\u51fa\u73b0\u7684\u75ab\u60c5\u66b4\u53d1\u5c24\u5176\u5177\u6709\u6311\u6218\u6027\u3002\u57fa\u4e8e lstm \u7ec6\u80de\u7684\u56de\u5f52\u795e\u7ecf\u7f51\u7edc(RNNs)\u5177\u6709\u5b66\u4e60\u65f6\u95f4\u52a8\u6001\u7684\u80fd\u529b\uff0c\u662f\u4e00\u79cd\u5408\u7406\u7684\u6a21\u578b\u9009\u62e9\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06 Wang \u7b49[12020]\u63d0\u51fa\u7684\u5dde\u548c\u53bf\u4e24\u7ea7\u6d41\u611f\u6a21\u578b(tfsi-lonly)\u9002\u7528\u4e8e\u56fd\u5bb6\u548c\u53bf\u4e24\u7ea7\u6d41\u611f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6570\u636e\u3002\u6211\u4eec\u8868\u660e\uff0c\u8fd9\u4e2a\u6a21\u578b\u5bf9\u5f53\u524d\u5927\u6d41\u884c\u7684\u9884\u6d4b\u5f88\u5dee\u3002\u6211\u4eec\u5206\u6790\u4e86\u4e24\u5468\u524d\u7684\u9884\u6d4b\u80fd\u529b\u7684 TDEFSI-LONLY \u6a21\u578b\u4e0e\u6b63\u5219\u5316\u6280\u672f\u7684\u7ec4\u5408\u3002\u4e3a\u4e86\u514b\u670d\u8fd9\u4e00\u56f0\u96be\uff0c\u6211\u4eec\u5229\u7528\u4e86\u4e00\u4e2a SEIR \u6a21\u578b\uff0c\u5e76\u5bf9\u8be5\u6a21\u578b\u8fdb\u884c\u4e86\u8de8\u53bf\u6df7\u5408\u6269\u5c55\uff0c\u4ee5\u6a21\u62df\u8db3\u591f\u7684\u8bad\u7ec3\u6570\u636e\u3002\u8fdb\u4e00\u6b65\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u66ff\u4ee3\u7684\u9884\u6d4b\u6a21\u578b\uff0c{ it \u53bf\u7ea7\u6d41\u884c\u75c5\u5b66\u63a8\u65ad\u5faa\u73af\u7f51\u7edc}(alg {}) \uff0c\u8be5\u6a21\u578b\u5728\u5168\u56fd\u786e\u8bca\u75c5\u4f8b\u4e0a\u8bad\u7ec3 LSTM \u9aa8\u5e72\u6765\u5b66\u4e60\u4f4e\u7ef4\u65f6\u95f4\u6a21\u5f0f\uff0c\u5e76\u5229\u7528\u65f6\u95f4\u5206\u5e03\u5bc6\u96c6\u5c42\u6765\u5b66\u4e60\u6bcf\u4e2a\u53bf\u786e\u8bca\u75c5\u4f8b\u6bcf\u5929\u7684\u53d8\u5316\uff0c\u4e3a\u671f\u4e24\u5468\u7684\u9884\u6d4b\u3002\u6211\u4eec\u8868\u660e\uff0c\u6700\u597d\uff0c\u6700\u574f\uff0c\u548c\u4e2d\u4f4d\u6570\u5dde\u9884\u6d4b\u4f7f\u7528 CLEIR-Net \u6a21\u578b\u5206\u522b\u662f\u7ebd\u7ea6\uff0c\u5357\u5361\u7f57\u6765\u7eb3\u5dde\u548c\u8499\u5927\u62ff\u5dde\u3002<\/span><\/section>\n

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    \u751f\u957f\u65e0\u6807\u5ea6\u5355\u7247\u673a<\/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

    Growing scale-free simplices<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12899<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    K. Kovalenko,I. Sendi\u00f1a-Nadal,N. Khalil,A. Dainiak,D. Musatov,K. Alfaro-Bittner,B. Barzel,S. Boccaletti<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The past two decades have seen significant successes in our understanding of complex networked systems, from the mapping of real-world social, biological and technological networks to the establishment of generative models recovering their observed macroscopic patterns. These advances, however, are restricted to pairwise interactions, captured by dyadic links, and provide limited insight into higher-order structure, in which a group of several components represents the basic interaction unit. Such multi-component interactions can only be grasped through simplicial complexes, which have recently found applications in social and biological contexts, as well as in engineering and brain science. What, then, are the generative models recovering the patterns observed in real-world simplicial complexes? Here we introduce, study, and characterize a model to grow simplicial complexes of order two, i.e. nodes, links and triangles, that yields a highly flexible range of empirically relevant simplicial network ensembles. Specifically, through a combination of preferential and\/or non preferential attachment mechanisms, the model constructs networks with a scale-free degree distribution and an either bounded or scale-free generalized degree distribution – the latter accounting for the number of triads surrounding each link. Allowing to analytically control the scaling exponents we arrive at a highly general scheme by which to construct ensembles of synthetic complexes displaying desired statistical properties.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fc7\u53bb20\u5e74\u4e2d\uff0c\u6211\u4eec\u5728\u7406\u89e3\u590d\u6742\u7684\u7f51\u7edc\u7cfb\u7edf\u65b9\u9762\u53d6\u5f97\u4e86\u91cd\u5927\u6210\u529f\uff0c\u4ece\u7ed8\u5236\u771f\u5b9e\u4e16\u754c\u7684\u793e\u4f1a\u3001\u751f\u7269\u548c\u6280\u672f\u7f51\u7edc\u56fe\uff0c\u5230\u5efa\u7acb\u6062\u590d\u89c2\u5bdf\u5230\u7684\u5b8f\u89c2\u6a21\u5f0f\u7684\u751f\u6210\u6a21\u578b\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u8fdb\u6b65\u4ec5\u9650\u4e8e\u4e24\u4e24\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u7531\u5e76\u77e2\u94fe\u63a5\u6355\u83b7\uff0c\u5e76\u63d0\u4f9b\u4e86\u5bf9\u9ad8\u9636\u7ed3\u6784\u7684\u6709\u9650\u6d1e\u5bdf\uff0c\u5728\u9ad8\u9636\u7ed3\u6784\u4e2d\uff0c\u4e00\u7ec4\u7531\u82e5\u5e72\u4e2a\u7ec4\u4ef6\u4ee3\u8868\u57fa\u672c\u7684\u76f8\u4e92\u4f5c\u7528\u5355\u5143\u3002\u8fd9\u79cd\u591a\u7ec4\u5206\u7684\u76f8\u4e92\u4f5c\u7528\u53ea\u80fd\u901a\u8fc7\u7b80\u5355\u7684\u590d\u5408\u4f53\u6765\u638c\u63e1\uff0c\u8fd9\u79cd\u590d\u5408\u4f53\u6700\u8fd1\u5728\u793e\u4f1a\u548c\u751f\u7269\u5b66\u9886\u57df\uff0c\u4ee5\u53ca\u5728\u5de5\u7a0b\u548c\u8111\u79d1\u5b66\u4e2d\u5f97\u5230\u4e86\u5e94\u7528\u3002\u90a3\u4e48\uff0c\u4ec0\u4e48\u662f\u751f\u6210\u6a21\u578b\u6062\u590d\u6a21\u5f0f\u89c2\u5bdf\u5230\u7684\u73b0\u5b9e\u4e16\u754c\u5355\u7eaf\u590d\u5f62\uff1f\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4ecb\u7ecd\uff0c\u7814\u7a76\u548c\u523b\u753b\u4e86\u4e00\u4e2a\u6a21\u578b\u751f\u957f\u4e8c\u9636\u5355\u7eaf\u590d\u5f62\uff0c\u5373\u8282\u70b9\uff0c\u94fe\u8def\u548c\u4e09\u89d2\u5f62\uff0c\u751f\u6210\u4e00\u4e2a\u9ad8\u5ea6\u7075\u6d3b\u7684\u8303\u56f4\u7ecf\u9a8c\u76f8\u5173\u7684\u5355\u7eaf\u7f51\u7edc\u96c6\u6210\u3002\u5177\u4f53\u800c\u8a00\uff0c\u901a\u8fc7\u4f18\u5148\u548c \/ \u6216\u975e\u4f18\u5148\u8fde\u63a5\u673a\u5236\u7684\u7ec4\u5408\uff0c\u8be5\u6a21\u578b\u6784\u9020\u4e86\u65e0\u6807\u5ea6\u5206\u5e03\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u6709\u754c\u6216\u65e0\u6807\u5ea6\u7684\u5e7f\u4e49\u5ea6\u5206\u5e03\u2014\u2014\u540e\u8005\u5360\u636e\u6bcf\u4e2a\u8fde\u63a5\u5468\u56f4\u7684\u4e09\u5143\u6570\u3002\u901a\u8fc7\u5206\u6790\u63a7\u5236\u6807\u5ea6\u6307\u6570\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u4e00\u4e2a\u9ad8\u5ea6\u901a\u7528\u7684\u65b9\u6848\uff0c\u901a\u8fc7\u8fd9\u4e2a\u65b9\u6848\u53ef\u4ee5\u6784\u9020\u51fa\u5177\u6709\u6240\u9700\u7edf\u8ba1\u7279\u6027\u7684\u5408\u6210\u590d\u5408\u7269\u7684\u6574\u4f53\u3002<\/span><\/section>\n

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    \u9a71\u52a8\u8c10\u632f\u5b50\u4e0e\u72ec\u7acb\u7684 Ising <\/strong><\/span><\/p>\n

    \u5728\u968f\u673a\u573a\u4e2d\u8026\u5408\u7684\u52a8\u529b\u5b66<\/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

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

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.12588<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Paul Zech,Andreas Otto,G\u00fcnter Radons<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We aim at an understanding of the dynamical properties of a periodically driven damped harmonic oscillator coupled to a ac{RFIM} at zero temperature, which is capable to show complex hysteresis. The system is a combination of a continuous (harmonic oscillator) and a discrete (ac{RFIM}) subsystem, which classifies it as a hybrid system. In this paper we focus on the hybrid nature of the system and consider only independent spins in quenched random local fields, which can already lead to complex dynamics such as chaos and multistability. We study the dynamic behavior of this system by using the theory of piecewise-smooth dynamical systems and discontinuity mappings. Specifically, we present bifurcation diagrams, Lyapunov exponents as well as results for the shape and the dimensions of the attractors and the self-averaging behavior of the attractor dimensions and the magnetization. Furthermore we investigate the dynamical behavior of the system for an increasing number of spins and the transition to the thermodynamic limit, where the system behaves like a driven harmonic oscillator with an additional nonlinear smooth external force.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7684\u76ee\u6807\u662f\u7406\u89e3\u5728\u96f6\u6e29\u4e0b\u5468\u671f\u9a71\u52a8\u7684\u963b\u5c3c\u8c10\u632f\u5b50\u4e0e\u4ea4\u6d41\u963b\u5c3c\u5668\u8026\u5408\u7684\u52a8\u529b\u5b66\u7279\u6027\uff0c\u5b83\u80fd\u591f\u663e\u793a\u590d\u6742\u7684\u6ede\u540e\u73b0\u8c61\u3002\u8fd9\u4e2a\u7cfb\u7edf\u662f\u4e00\u4e2a\u8fde\u7eed\u7684(\u8c10\u632f\u5b50)\u548c\u4e00\u4e2a\u79bb\u6563\u7684(ac { RFIM })\u5b50\u7cfb\u7edf\u7684\u7ec4\u5408\uff0c\u5b83\u88ab\u5f52\u7c7b\u4e3a\u4e00\u4e2a\u6df7\u5408\u7cfb\u7edf\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u7740\u773c\u4e8e\u7cfb\u7edf\u7684\u6df7\u5408\u672c\u8d28\uff0c\u4ec5\u8003\u8651\u6dec\u706d\u968f\u673a\u5c40\u57df\u573a\u4e2d\u7684\u72ec\u7acb\u81ea\u65cb\uff0c\u8fd9\u5df2\u7ecf\u5bfc\u81f4\u4e86\u6df7\u6c8c\u548c\u591a\u7a33\u5b9a\u6027\u7b49\u590d\u52a8\u529b\u5b66\u3002\u5229\u7528\u5206\u6bb5\u5149\u6ed1\u52a8\u529b\u7cfb\u7edf\u7406\u8bba\u548c\u4e0d\u8fde\u7eed\u6620\u5c04\u7814\u7a76\u4e86\u8be5\u7cfb\u7edf\u7684\u52a8\u529b\u5b66\u884c\u4e3a\u3002\u7ed9\u51fa\u4e86\u7cfb\u7edf\u7684\u5206\u5c94\u56fe\u3001\u674e\u96c5\u666e\u8bfa\u592b\u6307\u6570\u3001\u5438\u5f15\u5b50\u7684\u5f62\u72b6\u548c\u7ef4\u6570\u4ee5\u53ca\u5438\u5f15\u5b50\u7ef4\u6570\u548c\u78c1\u5316\u5f3a\u5ea6\u7684\u81ea\u5e73\u5747\u7279\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u7814\u7a76\u4e86\u7cfb\u7edf\u5728\u81ea\u65cb\u6570\u91cf\u589e\u52a0\u548c\u5411\u70ed\u529b\u5b66\u6781\u9650\u7684\u8dc3\u8fc1\u65f6\u7684\u52a8\u529b\u5b66\u884c\u4e3a\uff0c\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u7cfb\u7edf\u8868\u73b0\u5f97\u50cf\u4e00\u4e2a\u5e26\u6709\u9644\u52a0\u975e\u7ebf\u6027\u5149\u6ed1\u5916\u529b\u7684\u9a71\u52a8\u8c10\u632f\u5b50\u3002<\/span><\/section>\n

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    \u591a\u5143\u4e92\u4f9d\u7cfb\u7edf\u7684\u6269\u6563\u51e0\u4f55<\/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

    Diffusion Geometry of Multiplex and Interdependent Systems<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13032<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Giulia Bertagnolli,Manlio De Domenico<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Complex networks are characterized by latent geometries induced by their topology or by the dynamics on the top of them. In the latter case, different network-driven processes induce distinct geometric features that can be captured by adequate metrics. Random walks, a proxy for a broad spectrum of processes, from simple contagion to metastable synchronization and consensus, have been recently used in [Phys. Rev. Lett. 118, 168301 (2017)] to define the class of diffusion geometry and pinpoint the functional mesoscale organization of complex networks from a genuine geometric perspective. Here, we firstly extend this class to families of distinct random walk dynamics — including local and non-local information — on the top of multilayer networks — a paradigm for biological, neural, social, transportation, biological and financial systems — overcoming limitations such as the presence of isolated nodes and disconnected components, typical of real-world networks. Secondly, we characterize the multilayer diffusion geometry of synthetic and empirical systems, highlighting the role played by different random search dynamics in shaping the geometric features of the corresponding diffusion manifolds.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u590d\u6742\u7f51\u7edc\u662f\u7531\u5176\u62d3\u6251\u7ed3\u6784\u6216\u5176\u9876\u90e8\u7684\u52a8\u529b\u5b66\u8bf1\u5bfc\u7684\u62e5\u6709\u5c5e\u6027\u6f5c\u5728\u51e0\u4f55\u3002\u5728\u540e\u4e00\u79cd\u60c5\u51b5\u4e0b\uff0c\u4e0d\u540c\u7684\u7f51\u7edc\u9a71\u52a8\u8fc7\u7a0b\u4ea7\u751f\u4e86\u53ef\u4ee5\u88ab\u9002\u5f53\u7684\u5ea6\u91cf\u6240\u6355\u83b7\u7684\u4e0d\u540c\u7684\u51e0\u4f55\u7279\u5f81\u3002\u4ece\u7b80\u5355\u7684\u4f20\u67d3\u5230\u4e9a\u7a33\u5b9a\u7684\u540c\u6b65\u5316\u548c\u4e00\u81f4\u6027\uff0c\u968f\u673a\u6e38\u52a8\u4f5c\u4e3a\u4e00\u4e2a\u5e7f\u6cdb\u7684\u8fc7\u7a0b\u7684\u4ee3\u7406\uff0c\u6700\u8fd1\u5728[ Phys ]\u4e2d\u5f97\u5230\u4e86\u5e94\u7528\u3002\u96f7\u592b \u00b7 \u83b1\u7279\u3002118,168301(2017)]\u5b9a\u4e49\u6269\u6563\u51e0\u4f55\u5b66\u7684\u7c7b\u522b\uff0c\u5e76\u4ece\u771f\u6b63\u7684\u51e0\u4f55\u5b66\u89d2\u5ea6\u7cbe\u786e\u5b9a\u4f4d\u590d\u6742\u7f51\u7edc\u7684\u529f\u80fd\u6027\u4e2d\u5c3a\u5ea6\u7ec4\u7ec7\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u9996\u5148\u5c06\u8fd9\u4e2a\u7c7b\u6269\u5c55\u5230\u5177\u6709\u4e0d\u540c\u968f\u673a\u884c\u8d70\u52a8\u529b\u5b66\u7684\u5bb6\u65cf\u2014\u2014\u5305\u62ec\u5c40\u90e8\u548c\u975e\u5c40\u90e8\u4fe1\u606f\u2014\u2014\u5728\u591a\u5c42\u7f51\u7edc\u7684\u9876\u5c42\u2014\u2014\u751f\u7269\u3001\u795e\u7ecf\u3001\u793e\u4f1a\u3001\u4ea4\u901a\u3001\u751f\u7269\u548c\u91d1\u878d\u7cfb\u7edf\u7684\u8303\u4f8b\u2014\u2014\u514b\u670d\u4e86\u73b0\u5b9e\u4e16\u754c\u7f51\u7edc\u4e2d\u5178\u578b\u7684\u5b64\u7acb\u8282\u70b9\u548c\u4e0d\u8fde\u7eed\u7ec4\u4ef6\u7684\u5b58\u5728\u7b49\u5c40\u9650\u6027\u3002\u5176\u6b21\uff0c\u523b\u753b\u4e86\u5408\u6210\u6269\u6563\u7cfb\u7edf\u548c\u7ecf\u9a8c\u6269\u6563\u7cfb\u7edf\u7684\u591a\u5c42\u6269\u6563\u51e0\u4f55\u7279\u5f81\uff0c\u7a81\u51fa\u4e86\u4e0d\u540c\u7684\u968f\u673a\u641c\u7d22\u52a8\u529b\u5b66\u5728\u5f62\u6210\u76f8\u5e94\u6269\u6563\u6d41\u5f62\u7684\u51e0\u4f55\u7279\u5f81\u4e2d\u6240\u8d77\u7684\u4f5c\u7528\u3002<\/span><\/section>\n

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    \u5229\u7528\u56fe\u8bba\u548c\u793e\u4f1a\u5a92\u4f53\u6570\u636e<\/strong><\/span><\/p>\n

    \u8bc4\u4f30\u6cbf\u6d77\u5730\u533a\u7684\u6587\u5316\u751f\u6001\u7cfb\u7edf\u670d\u52a1: <\/strong><\/span><\/p>\n

    \u65b9\u6cd5\u53d1\u5c55\u548c\u5e94\u7528<\/strong><\/span><\/p>\n

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

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

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

    Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12495<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Ana Ruiz-Frau,Andres Ospina-Alvarez,Sebasti\u00e1n Villasante,Pablo Pita,Isidro Maya-Jariego,Silvia de Juan Mohan<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter’s bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different social media platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u793e\u4f1a\u5a92\u4f53(SM)\u6570\u636e\u7684\u4f7f\u7528\u5df2\u7ecf\u6210\u4e3a\u8bc4\u4f30\u6587\u5316\u751f\u6001\u7cfb\u7edf\u670d\u52a1(CES)\u7684\u4e00\u4e2a\u6709\u524d\u9014\u7684\u5de5\u5177\u3002\u5927\u591a\u6570\u7814\u7a76\u96c6\u4e2d\u5728\u5355\u4e00\u7684 SM \u5e73\u53f0\u7684\u4f7f\u7528\u548c\u7167\u7247\u5185\u5bb9\u7684\u5206\u6790\uff0c\u4ee5\u8bc4\u4f30\u5bf9 CES \u7684\u9700\u6c42\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u901a\u8fc7\u56fe\u8bba\u7f51\u7edc\u5206\u6790(GTNA)\u5bf9\u4e0e SM \u5e16\u5b50\u76f8\u5173\u7684 hashtag \u8fdb\u884c\u5206\u6790\uff0c\u5e76\u5c06\u5176\u4e0e\u7167\u7247\u5185\u5bb9\u5206\u6790\u8fdb\u884c\u6bd4\u8f83\uff0c\u4ece\u800c\u63d0\u51fa\u4e86\u4e00\u79cd\u5229\u7528 SM \u6570\u636e\u8bc4\u4f30 CES \u7684\u65b0\u65b9\u6cd5\u3002\u6211\u4eec\u5728 Instagram \u548c Twitter \u8fd9\u4e24\u4e2a SM \u5e73\u53f0\u4e0a\u5e94\u7528\u4e86\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\uff0c\u5e76\u57283\u4e2a\u4e16\u754c\u77e5\u540d\u7684\u6848\u4f8b\u7814\u7a76\u9886\u57df—- \u5927\u5821\u7901\u3001\u79d1\u9686\u7fa4\u5c9b\u548c\u590d\u6d3b\u8282\u5c9b—- \u8fdb\u884c\u4e86\u5e94\u7528\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u901a\u8fc7\u56fe\u8bba\u5bf9 # \u6807\u7b7e\u7684\u5206\u6790\u63d0\u4f9b\u4e86\u4e0e\u7167\u7247\u5185\u5bb9\u5206\u6790\u76f8\u4f3c\u7684\u529f\u80fd\uff0c\u7528\u4e8e\u8bc4\u4f30 CES \u670d\u52a1\u548c\u8bc6\u522b CES \u670d\u52a1\u63d0\u4f9b\u5546\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0cGTNA \u4e3a\u7167\u7247\u5185\u5bb9\u5206\u6790\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u8bc6\u522b\u5173\u7cfb\u4ef7\u503c\u548c\u4e0e\u81ea\u7136\u76f8\u5173\u7684\u771f\u5b9e\u6027\u65b9\u9762\u7684\u80fd\u529b\uff0c\u8fd9\u4e9b\u65b9\u9762\u96be\u4ee5\u6349\u6478\u3002\u6b64\u5916\uff0cGTNA \u6709\u52a9\u4e8e\u51cf\u5c11\u89e3\u91ca\u5668\u5bf9\u7167\u7247\u5185\u5bb9\u5206\u6790\u7684\u504f\u89c1\uff0c\u56e0\u4e3a GTNA \u662f\u57fa\u4e8e\u7528\u6237\u81ea\u5df1\u63d0\u4f9b\u7684\u6807\u7b7e\u3002\u8fd9\u9879\u7814\u7a76\u8fd8\u5f3a\u8c03\u4e86\u8003\u8651\u6765\u81ea\u4e0d\u540c\u793e\u4ea4\u5a92\u4f53\u5e73\u53f0\u7684\u6570\u636e\u7684\u91cd\u8981\u6027\uff0c\u56e0\u4e3a\u7528\u6237\u7c7b\u578b\u548c\u8fd9\u4e9b\u5e73\u53f0\u63d0\u4f9b\u7684\u4fe1\u606f\u53ef\u4ee5\u663e\u793a\u4e0d\u540c\u7684 CES \u5c5e\u6027\u3002\u5e94\u7528 GTNA \u7684\u65b9\u4fbf\u6027\u548c\u8ba1\u7b97\u5904\u7406\u65f6\u95f4\u77ed\uff0c\u4f7f\u5176\u6210\u4e3a\u4e00\u79cd\u5177\u6709\u6210\u672c\u6548\u76ca\u7684\u65b9\u6cd5\uff0c\u6709\u53ef\u80fd\u5e94\u7528\u4e8e\u5927\u89c4\u6a21\u7684\u5730\u7406\u8303\u56f4\u3002<\/span><\/section>\n

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    \u5177\u6709\u6218\u7565\u610f\u89c1\u62ab\u9732<\/strong><\/span><\/p>\n

    \u548c\u975e\u597d\u53cb\u7684\u610f\u89c1\u6269\u6563\u8f6f\u4ef6<\/strong><\/span><\/strong><\/p>\n

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


    <\/p>\n

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

    Opinion Diffusion Software with Strategic Opinion Revelation and Unfriending<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12572<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Patrick Shepherd,Mia Weaver,Judy Goldsmith<\/span><\/section>\n
     <\/span><\/section>\n
    Abstract\uff1a<\/span><\/strong>We present a novel software suite for social network modeling and opinion diffusion processes. Much research on social network science has assumed networks with static topologies. More recently, attention has been turned to networks that evolve. Although software for modeling both the topological evolution of networks and diffusion processes are constantly improving, very little attention has been paid to agent modeling. Our software is designed to be robust, modular, and extensible, providing the ability to model dynamic social network topologies and multidimensional diffusion processes, different styles of agent including non-homophilic paradigms, as well as a testing environment for multi-agent reinforcement learning (MARL) experiments with diverse sets of agent types. We also illustrate the value of diverse agent modeling, and environments that allow for strategic unfriending. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on individuals’ goals for the composition of their neighborhood’s opinions.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u8f6f\u4ef6\u5957\u4ef6\u7528\u4e8e\u793e\u4f1a\u7f51\u7edc\u5efa\u6a21\u548c\u610f\u89c1\u4f20\u64ad\u8fc7\u7a0b\u3002\u793e\u4f1a\u7f51\u7edc\u79d1\u5b66\u7684\u8bb8\u591a\u7814\u7a76\u5047\u5b9a\u7f51\u7edc\u5177\u6709\u9759\u6001\u62d3\u6251\u7ed3\u6784\u3002\u6700\u8fd1\uff0c\u4eba\u4eec\u7684\u6ce8\u610f\u529b\u8f6c\u5411\u4e86\u8fdb\u5316\u4e2d\u7684\u7f51\u7edc\u3002\u867d\u7136\u7528\u4e8e\u5efa\u6a21\u7f51\u7edc\u62d3\u6251\u6f14\u5316\u548c\u6269\u6563\u8fc7\u7a0b\u7684\u8f6f\u4ef6\u6b63\u5728\u4e0d\u65ad\u6539\u8fdb\uff0c\u4f46\u662f\u5bf9\u4e8e agent \u5efa\u6a21\u7684\u7814\u7a76\u8fd8\u5f88\u5c11\u3002\u6211\u4eec\u7684\u8f6f\u4ef6\u88ab\u8bbe\u8ba1\u6210\u5065\u58ee\u7684\u3001\u6a21\u5757\u5316\u7684\u548c\u53ef\u6269\u5c55\u7684\uff0c\u63d0\u4f9b\u4e86\u5bf9\u52a8\u6001\u793e\u4f1a\u7f51\u7edc\u62d3\u6251\u548c\u591a\u7ef4\u6269\u6563\u8fc7\u7a0b\u5efa\u6a21\u7684\u80fd\u529b\uff0c\u4e0d\u540c\u7c7b\u578b\u7684\u4ee3\u7406\u5305\u62ec\u975e\u540c\u6e90\u8303\u4f8b\uff0c\u4ee5\u53ca\u4e00\u4e2a\u591a\u4ee3\u7406\u5f3a\u5316\u5b66\u4e60\u5b9e\u9a8c\u7684\u6d4b\u8bd5\u73af\u5883\u3002\u6211\u4eec\u8fd8\u8bf4\u660e\u4e86\u591a\u6837\u5316\u4ee3\u7406\u5efa\u6a21\u7684\u4ef7\u503c\uff0c\u4ee5\u53ca\u5141\u8bb8\u6218\u7565\u6027\u89e3\u9664\u597d\u53cb\u5173\u7cfb\u7684\u73af\u5883\u3002\u6211\u4eec\u7684\u5de5\u4f5c\u8868\u660e\uff0c\u6781\u5316\u548c\u5171\u8bc6\u52a8\u6001\uff0c\u4ee5\u53ca\u62d3\u6251\u805a\u7c7b\u6548\u5e94\uff0c\u53ef\u80fd\u4f9d\u8d56\u4e8e\u6bd4\u4ee5\u524d\u77e5\u9053\u7684\u66f4\u591a\u7684\u4e2a\u4eba\u7684\u76ee\u6807\u7ec4\u6210\u4ed6\u4eec\u7684\u90bb\u5c45\u7684\u610f\u89c1\u3002<\/span><\/section>\n

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    \u57fa\u4e8e\u8fd1\u4f3c\u7279\u5f81\u503c\u8f68\u8ff9\u8c31\u6f14\u5316\u7684\u94fe\u8def\u9884\u6d4b<\/strong><\/span><\/p>\n

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

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

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

    Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12657<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Miguel Romero,Jorge Finke,Camilo Rocha,Luis Tob\u00f3n <\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The spectral evolution model aims to characterize the growth of large networks (i.e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the adjacency matrices. It assumes that, while eigenvectors remain constant, eigenvalues evolve in a predictable manner over time. This paper extends the original formulation of the model twofold. First, it presents a method to compute an approximation of the spectral evolution of eigenvalues based on the Rayleigh quotient. Second, it proposes an algorithm to estimate the evolution of eigenvalues by extrapolating only a fraction of their approximated values. The proposed model is used to characterize mention networks of users who posted tweets that include the most popular political hashtags in Colombia from August 2017 to August 2018 (the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). To evaluate the extent to which the spectral evolution model resembles these networks, link prediction methods based on learning algorithms (i.e., extrapolation and regression) and graph kernels are implemented. Experimental results show that the learning algorithms deployed on the approximated trajectories outperform the usual kernel and extrapolation methods at predicting the formation of new edges.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8c31\u6f14\u5316\u6a21\u578b\u65e8\u5728\u901a\u8fc7\u90bb\u63a5\u77e9\u9635\u7684\u7279\u5f81\u503c\u5206\u89e3\u6765\u523b\u753b\u5927\u578b\u7f51\u7edc\u7684\u589e\u957f(\u5373\uff0c\u5b83\u4eec\u662f\u5982\u4f55\u6f14\u5316\u6210\u65b0\u7684\u8fb9\u7684)\u3002\u5b83\u5047\u5b9a\uff0c\u5f53\u7279\u5f81\u5411\u91cf\u4fdd\u6301\u4e0d\u53d8\u65f6\uff0c\u7279\u5f81\u503c\u968f\u65f6\u95f4\u4ee5\u4e00\u79cd\u53ef\u9884\u6d4b\u7684\u65b9\u5f0f\u6f14\u5316\u3002\u672c\u6587\u5bf9\u6a21\u578b\u7684\u539f\u6709\u516c\u5f0f\u8fdb\u884c\u4e86\u4e8c\u91cd\u63a8\u5e7f\u3002\u9996\u5148\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u745e\u5229\u5546\u7684\u7279\u5f81\u503c\u8c31\u6f14\u5316\u7684\u8fd1\u4f3c\u8ba1\u7b97\u65b9\u6cd5\u3002\u5176\u6b21\uff0c\u5b83\u63d0\u51fa\u4e86\u4e00\u79cd\u7b97\u6cd5\u6765\u4f30\u8ba1\u7279\u5f81\u503c\u7684\u6f14\u5316\uff0c\u53ea\u5916\u63a8\u5176\u8fd1\u4f3c\u503c\u7684\u4e00\u5c0f\u90e8\u5206\u3002\u63d0\u8bae\u7684\u6a21\u578b\u7528\u4e8e\u63cf\u8ff02017\u5e748\u6708\u81f32018\u5e748\u6708(\u5373\u54e5\u4f26\u6bd4\u4e9a\u9769\u547d\u6b66\u88c5\u90e8\u961f\u89e3\u9664\u6b66\u88c5\u7684\u65f6\u671f)\u53d1\u5e03\u5305\u62ec\u54e5\u4f26\u6bd4\u4e9a\u6700\u6d41\u884c\u7684\u653f\u6cbb\u6807\u7b7e\u7684\u63a8\u6587\u7684\u7528\u6237\u63d0\u53ca\u7f51\u7edc\u7684\u7279\u5f81\u3002\u4e3a\u4e86\u8bc4\u4f30\u8c31\u8fdb\u5316\u6a21\u578b\u4e0e\u8fd9\u4e9b\u7f51\u7edc\u7684\u76f8\u4f3c\u7a0b\u5ea6\uff0c\u5b9e\u73b0\u4e86\u57fa\u4e8e\u5b66\u4e60\u7b97\u6cd5(\u5373\u5916\u63a8\u548c\u56de\u5f52)\u548c\u56fe\u6838\u7684\u94fe\u8def\u9884\u6d4b\u65b9\u6cd5\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u57fa\u4e8e\u8fd1\u4f3c\u8f68\u8ff9\u7684\u5b66\u4e60\u7b97\u6cd5\u5728\u9884\u6d4b\u65b0\u8fb9\u7f18\u7684\u5f62\u6210\u65b9\u9762\u4f18\u4e8e\u4f20\u7edf\u7684\u6838\u548c\u5916\u63a8\u65b9\u6cd5\u3002 <\/span><\/section>\n

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

    \n
    \n
    <\/p>\n

    \u57fa\u4e8e\u5c5e\u6027\u7f51\u7edc\u7684<\/strong><\/span><\/p>\n

    \u5c11\u955c\u5934\u5b66\u4e60\u7684\u56fe\u539f\u578b\u7f51\u7edc<\/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

    Graph Prototypical Networks for Few-shot Learning on Attributed Networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.12739<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Kaize Ding,Jianling Wang,Jundong Li,Kai Shu,Chenghao Liu,Huan Liu<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes only contain limited labeled instances, rendering a long-tail node class distribution. Existing node classification algorithms are unequipped to handle the textit{few-shot} node classes. As a remedy, few-shot learning has attracted a surge of attention in the research community. Yet, few-shot node classification remains a challenging problem as we need to address the following questions: (i) How to extract meta-knowledge from an attributed network for few-shot node classification? (ii) How to identify the informativeness of each labeled instance for building a robust and effective model? To answer these questions, in this paper, we propose a graph meta-learning framework — Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task. Extensive experiments demonstrate the superior capability of GPN in few-shot node classification.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5982\u4eca\uff0c\u5c5e\u6027\u5316\u7f51\u7edc\u5728\u65e0\u6570\u9ad8\u5f71\u54cd\u529b\u7684\u5e94\u7528\u4e2d\u65e0\u5904\u4e0d\u5728\uff0c\u6bd4\u5982\u793e\u4ea4\u7f51\u7edc\u5206\u6790\u3001\u91d1\u878d\u6b3a\u8bc8\u68c0\u6d4b\u548c\u836f\u7269\u53d1\u73b0\u3002\u8282\u70b9\u5206\u7c7b\u4f5c\u4e3a\u5c5e\u6027\u7f51\u7edc\u7684\u6838\u5fc3\u5206\u6790\u4efb\u52a1\uff0c\u53d7\u5230\u4e86\u7814\u7a76\u754c\u7684\u5e7f\u6cdb\u5173\u6ce8\u3002\u5728\u73b0\u5b9e\u7684\u5c5e\u6027\u5316\u7f51\u7edc\u4e2d\uff0c\u5f88\u5927\u4e00\u90e8\u5206\u8282\u70b9\u7c7b\u53ea\u5305\u542b\u6709\u9650\u7684\u6807\u8bb0\u5b9e\u4f8b\uff0c\u5448\u73b0\u51fa\u957f\u5c3e\u8282\u70b9\u7c7b\u5206\u5e03\u3002\u73b0\u6709\u7684\u8282\u70b9\u5206\u7c7b\u7b97\u6cd5\u65e0\u6cd5\u5904\u7406 textit { few-shot }\u8282\u70b9\u7c7b\u3002\u4f5c\u4e3a\u4e00\u79cd\u8865\u6551\u63aa\u65bd\uff0c\u201c\u51e0\u6746\u5b66\u4e60\u201d\u5728\u7814\u7a76\u754c\u5f15\u8d77\u4e86\u6781\u5927\u7684\u5173\u6ce8\u3002\u7136\u800c\uff0c\u5c11\u955c\u5934\u8282\u70b9\u5206\u7c7b\u4ecd\u7136\u662f\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u95ee\u9898\uff0c\u56e0\u4e3a\u6211\u4eec\u9700\u8981\u89e3\u51b3\u4ee5\u4e0b\u95ee\u9898: (i)\u5982\u4f55\u4ece\u5c5e\u6027\u7f51\u7edc\u4e2d\u63d0\u53d6\u5143\u77e5\u8bc6\u8fdb\u884c\u5c11\u955c\u5934\u8282\u70b9\u5206\u7c7b\uff1f(ii)\u5982\u4f55\u786e\u5b9a\u6bcf\u4e2a\u88ab\u6807\u8bb0\u5b9e\u4f8b\u7684\u4fe1\u606f\u6027\uff0c\u4ee5\u5efa\u7acb\u4e00\u4e2a\u5065\u5168\u548c\u6709\u6548\u7684\u6a21\u578b\uff1f\u4e3a\u4e86\u56de\u7b54\u8fd9\u4e9b\u95ee\u9898\uff0c\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a\u56fe\u5143\u5b66\u4e60\u6846\u67b6\u2014\u2014\u56fe\u539f\u578b\u7f51\u7edc(GPN)\u3002\u901a\u8fc7\u6784\u9020\u4e00\u4e2a\u534a\u76d1\u7763\u7684\u8282\u70b9\u5206\u7c7b\u4efb\u52a1\u6c60\u6765\u6a21\u62df\u771f\u5b9e\u7684\u6d4b\u8bd5\u73af\u5883\uff0cGPN \u80fd\u591f\u5728\u4e00\u4e2a\u5c5e\u6027\u5316\u7f51\u7edc\u4e0a\u6267\u884c\u6587\u672c{\u5143\u5b66\u4e60} \uff0c\u5e76\u63a8\u5bfc\u51fa\u4e00\u4e2a\u9ad8\u5ea6\u901a\u7528\u7684\u76ee\u6807\u5206\u7c7b\u4efb\u52a1\u5904\u7406\u6a21\u578b\u3002\u5927\u91cf\u7684\u5b9e\u9a8c\u8bc1\u660e\u4e86 GPN \u5728\u5c11\u955c\u5934\u8282\u70b9\u5206\u7c7b\u65b9\u9762\u7684\u4f18\u8d8a\u6027\u80fd\u3002<\/span><\/section>\n

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    Lumos: <\/strong><\/span><\/p>\n

    \u4e00\u4e2a\u7528\u4e8e\u8bca\u65ad web \u89c4\u6a21<\/strong><\/span><\/p>\n

    \u5e94\u7528\u7a0b\u5e8f\u4e2d\u7684\u5ea6\u91cf\u56de\u5f52\u7684\u5e93<\/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

    Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.12793<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Jamie Pool,Ebrahim Beyrami,Vishak Gopal,Ashkan Aazami,Jayant Gupchup,Jeff Rowland,Binlong Li,Pritesh Kanani,Ross Cutler,Johannes Gehrke<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population, and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We present Lumos, a Python library built using the principles of AB testing to systematically diagnose metric regressions to automate such analysis. Lumos has been deployed across the component teams in Microsoft’s Real-Time Communication applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application of Lumos has resulted in freeing up as much as 95% of the time allocated to metric-based investigations. In this work, we open source Lumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>Web \u89c4\u6a21\u7684\u5e94\u7528\u7a0b\u5e8f\u53ef\u4ee5\u6309\u7167\u6bcf\u5929\u5230\u6bcf\u5468\u7684\u8282\u594f\u53d1\u5e03\u4ee3\u7801\u3002\u8fd9\u4e9b\u5e94\u7528\u7a0b\u5e8f\u4f9d\u8d56\u4e8e\u5728\u7ebf\u6307\u6807\u6765\u76d1\u89c6\u65b0\u7248\u672c\u7684\u5065\u5eb7\u72b6\u51b5\u3002\u516c\u5236\u503c\u7684\u56de\u5f52\u9700\u8981\u5c3d\u65e9\u68c0\u6d4b\u548c\u8bca\u65ad\uff0c\u4ee5\u51cf\u5c11\u5bf9\u7528\u6237\u548c\u4ea7\u54c1\u6240\u6709\u8005\u7684\u5e72\u6270\u3002\u6307\u6807\u56de\u5f52\u53ef\u80fd\u51fa\u73b0\u7684\u539f\u56e0\u6709\u5f88\u591a: \u771f\u6b63\u7684\u4ea7\u54c1\u56de\u5f52\u3001\u7528\u6237\u7fa4\u4f53\u7684\u53d8\u5316\u3001\u9065\u6d4b\u6570\u636e\u4e22\u5931(\u6216\u5904\u7406)\u5bfc\u81f4\u7684\u504f\u89c1\u90fd\u662f\u5e38\u89c1\u7684\u539f\u56e0\u3002\u5bf9\u4e8e\u5de5\u7a0b\u56e2\u961f\u6765\u8bf4\uff0c\u8bca\u65ad\u8fd9\u4e9b\u5ea6\u91cf\u56de\u5f52\u7684\u539f\u56e0\u6210\u672c\u5f88\u9ad8\uff0c\u56e0\u4e3a\u4ed6\u4eec\u9700\u8981\u82b1\u8d39\u65f6\u95f4\u5c3d\u5feb\u627e\u5230\u95ee\u9898\u7684\u6839\u672c\u539f\u56e0\u3002\u6211\u4eec\u4ecb\u7ecd\u4e86 Lumos\uff0c\u8fd9\u662f\u4e00\u4e2a\u4f7f\u7528 AB \u6d4b\u8bd5\u539f\u7406\u6784\u5efa\u7684 Python \u5e93\uff0c\u7528\u4e8e\u7cfb\u7edf\u5730\u8bca\u65ad\u5ea6\u91cf\u56de\u5f52\u4ee5\u81ea\u52a8\u8fdb\u884c\u8fd9\u79cd\u5206\u6790\u3002Lumos \u5df2\u7ecf\u90e8\u7f72\u5728\u5fae\u8f6f\u5b9e\u65f6\u901a\u4fe1\u5e94\u7528\u7a0b\u5e8f Skype \u548c\u5fae\u8f6f\u56e2\u961f\u7684\u7ec4\u4ef6\u56e2\u961f\u4e2d\u3002\u5b83\u4f7f\u5de5\u7a0b\u56e2\u961f\u80fd\u591f\u68c0\u6d4b\u5230100\u4e2a\u771f\u5b9e\u7684\u6307\u6807\u53d8\u5316\uff0c\u5e76\u62d2\u7edd\u7531\u5f02\u5e38\u68c0\u6d4b\u5668\u68c0\u6d4b\u5230\u76841000\u4e2a\u5047\u8b66\u62a5\u3002Lumos \u7684\u7533\u8bf7\u4f7f\u5f97\u591a\u8fbe95% \u7684\u65f6\u95f4\u7528\u4e8e\u57fa\u4e8e\u516c\u5236\u7684\u8c03\u67e5\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u5f00\u653e\u6e90\u7801 Lumos\uff0c\u5e76\u5728\u6570\u767e\u4e07\u4e2a\u4f1a\u8bdd\u4e2d\u5c06\u5176\u5e94\u7528\u4e8e RTC \u7ec4\u4e2d\u7684\u4e24\u4e2a\u4e0d\u540c\u7ec4\u4ef6\uff0c\u4ece\u800c\u663e\u793a\u6211\u4eec\u7684\u7ed3\u679c\u3002\u8fd9\u4e2a\u901a\u7528\u5e93\u53ef\u4ee5\u4e0e\u4efb\u4f55\u751f\u4ea7\u7cfb\u7edf\u76f8\u7ed3\u5408\uff0c\u4ee5\u6709\u6548\u5730\u7ba1\u7406\u62a5\u8b66\u7684\u6570\u91cf\u3002<\/span><\/section>\n

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    \n
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    \u8fde\u7eed\u65f6\u95f4\u4e8b\u4ef6\u65f6\u6001\u7f51\u7edc\u7684<\/strong><\/span><\/p>\n

    Hawkes \u8fb9\u754c\u5212\u5206\u6a21\u578b<\/strong><\/span><\/p>\n

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

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

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

    The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.12952<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Sikun Yang,Heinz Koeppl<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We propose a novel probabilistic framework to model continuous-time interaction events data. Our goal is to infer the emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the community structure influences the interaction dynamics among these nodes. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities. Moreover, our model can flexibly incorporate the auxiliary individuals’ attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via P’olya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance for temporal link prediction compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u6982\u7387\u6846\u67b6\u6765\u6a21\u62df\u8fde\u7eed\u65f6\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u4e8b\u4ef6\u6570\u636e\u3002\u6211\u4eec\u7684\u76ee\u6807\u662f\u63a8\u65ad\u5b9e\u4f53\u4e4b\u95f4\u65f6\u95f4\u76f8\u4e92\u4f5c\u7528\u80cc\u540e\u7684 emph { implicit }\u793e\u533a\u7ed3\u6784\uff0c\u5e76\u5229\u7528\u793e\u533a\u7ed3\u6784\u5982\u4f55\u5f71\u54cd\u8fd9\u4e9b\u8282\u70b9\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u52a8\u529b\u5b66\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u4f7f\u7528\u76f8\u4e92\u6fc0\u52b1\u7684\u970d\u514b\u65af\u8fc7\u7a0b\u6765\u6a21\u62df\u4e2a\u4f53\u4e4b\u95f4\u7684\u5f80\u590d\u4ea4\u4e92\u4f5c\u7528\u3002\u57fa\u4e8e\u5c42\u6b21\u4f3d\u739b\u8fc7\u7a0b\u8fb9\u754c\u5212\u5206\u6a21\u578b(HGaP-EPM)\u63a8\u5bfc\u51fa\u6bcf\u5bf9\u4e2a\u4f53\u7684 Hawkes \u8fc7\u7a0b\u7684\u57fa\u672c\u6982\u7387\u3002\u7279\u522b\u662f\uff0c\u6211\u4eec\u7684\u6a21\u578b\u5141\u8bb8\u6bcf\u5bf9\u4e2a\u4f53\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u52a8\u6001\u88ab\u4ed6\u4eec\u5404\u81ea\u7684\u9644\u5c5e\u793e\u533a\u6240\u8c03\u6574\u3002\u6b64\u5916\uff0c\u6211\u4eec\u7684\u6a21\u578b\u53ef\u4ee5\u7075\u6d3b\u5730\u7ed3\u5408\u8f85\u52a9\u4e2a\u4f53\u7684\u5c5e\u6027\uff0c\u6216\u8005\u4e0e\u4ea4\u4e92\u4e8b\u4ef6\u76f8\u5173\u7684\u534f\u53d8\u91cf\u3002\u5229\u7528 p\u2018 olya-Gamma \u6570\u636e\u589e\u5f3a\u7b56\u7565\uff0c\u5f00\u53d1\u4e86\u9ad8\u6548\u7684 Gibbs \u91c7\u6837\u548c\u671f\u671b\u6700\u5927\u5316\u7b97\u6cd5\u6765\u8fdb\u884c\u63a8\u7406\u3002\u5728\u73b0\u5b9e\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u8be5\u6a21\u578b\u4e0d\u4ec5\u5728\u65f6\u95f4\u94fe\u8def\u9884\u6d4b\u65b9\u9762\u6bd4\u73b0\u6709\u65b9\u6cd5\u5177\u6709\u66f4\u5f3a\u7684\u7ade\u4e89\u6027\uff0c\u800c\u4e14\u8fd8\u53d1\u73b0\u4e86\u9690\u85cf\u5728\u89c2\u5bdf\u5230\u7684\u65f6\u95f4\u4ea4\u4e92\u80cc\u540e\u7684\u53ef\u89e3\u91ca\u7684\u6f5c\u5728\u7ed3\u6784\u3002<\/span><\/section>\n

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    \u5728\u4e00\u6b21\u5927\u89c4\u6a21 HST \u6d4b\u91cf(WISP)\u4e2d<\/strong><\/span><\/p>\n

    \u901a\u8fc7\u76d1\u7763\u5f0f\u5b66\u4e60\u8bc6\u522b\u5355\u5149\u8c31\u7ebf:<\/strong><\/span><\/p>\n

    \u6b27\u51e0\u91cc\u5fb7\u548c WFIRST \u7684\u8bd5\u70b9\u7814\u7a76<\/strong><\/span><\/p>\n

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

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

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

    Identification of single spectral lines through supervised machine learning in a large HST survey (WISP): a pilot study for Euclid and WFIRST<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12613<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    I. Baronchelli,C. M. Scarlata,G. Rodighiero,L. Rodr\u00edguez-Mu\u00f1oz,M. Bonato,M. Bagley,A. Henry,M. Rafelski,M. Malkan,J. Colbert,Y. S. Dai,H. Dickinson,C. Mancini,V. Mehta,L. Morselli,H. I. Teplitz<\/span><\/section>\n

    <\/section>\n
    A<\/span><\/strong>bstract\uff1a<\/strong>Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a sample of single emission lines extracted from the WFC3 IR Spectroscopic Parallel survey (WISP), one of the closest existing analogs to future slitless surveys. Our automatic software integrates a SED fitting strategy with additional independent sources of information. We calibrated it and tested it on a “gold” sample of securely identified objects with multiple lines detected. The algorithm correctly classifies real emission lines with an accuracy of 82.6%, whereas the accuracy of the SED fitting technique alone is low (~50%) due to the limited amount of photometric data available (<=6 bands). While not specifically designed for the Euclid and WFIRST surveys, the algorithm represents an important precursor of similar algorithms to be used in these future missions.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u672a\u6765\u7684\u8c03\u67e5\u5c06\u7740\u773c\u4e8e\u4e86\u89e3\u6697\u80fd\u91cf\u7684\u672c\u8d28(\u4f8b\u5982\uff0c\u6b27\u51e0\u91cc\u5f97\u548c WFIRST) \uff0c\u5c06\u5728\u8fd1\u7ea2\u5916\u65e0\u7f1d\u5149\u8c31\u5b66\u4e2d\u8986\u76d6\u94f6\u6cb3\u7cfb\u5916\u5929\u7a7a\u7684\u5927\u90e8\u5206\u3002\u8fd9\u4e9b\u52d8\u6d4b\u5c06\u63a2\u6d4b\u5230\u5927\u91cf\u7684\u661f\u7cfb\uff0c\u8fd9\u4e9b\u661f\u7cfb\u5728\u88ab\u8986\u76d6\u7684\u5149\u8c31\u8303\u56f4\u5185\u53ea\u6709\u4e00\u6761\u53d1\u5c04\u7ebf\u3002\u4e3a\u4e86\u6700\u5927\u9650\u5ea6\u5730\u63d0\u9ad8\u8fd9\u4e9b\u98de\u884c\u4efb\u52a1\u7684\u79d1\u5b66\u56de\u62a5\uff0c\u5fc5\u987b\u6b63\u786e\u5730\u786e\u5b9a\u5355\u4e00\u53d1\u5c04\u7ebf\u3002\u5229\u7528\u6709\u76d1\u7763\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u6211\u4eec\u5bf9\u4ece WFC3 IR \u5149\u8c31\u5e73\u884c\u6d4b\u91cf(WISP)\u4e2d\u63d0\u53d6\u7684\u5355\u53d1\u5c04\u7ebf\u6837\u54c1\u8fdb\u884c\u4e86\u5206\u7c7b\u3002WFC3 IR \u5149\u8c31\u5e73\u884c\u6d4b\u91cf\u662f\u76ee\u524d\u6700\u63a5\u8fd1\u672a\u6765\u65e0\u7f1d\u6d4b\u91cf\u7684\u7c7b\u4f3c\u7269\u4e4b\u4e00\u3002\u6211\u4eec\u7684\u81ea\u52a8\u5316\u8f6f\u4ef6\u96c6\u6210\u4e86 SED \u62df\u5408\u7b56\u7565\u4e0e\u5176\u4ed6\u72ec\u7acb\u7684\u4fe1\u606f\u6765\u6e90\u3002\u6211\u4eec\u5bf9\u5b83\u8fdb\u884c\u6821\u51c6\uff0c\u5e76\u5728\u4e00\u4e2a\u201c\u9ec4\u91d1\u201d\u6837\u672c\u4e0a\u8fdb\u884c\u6d4b\u8bd5\uff0c\u8be5\u6837\u672c\u7531\u591a\u6761\u7ebf\u63a2\u6d4b\u5230\u7684\u5b89\u5168\u8bc6\u522b\u7269\u4f53\u7ec4\u6210\u3002\u8be5\u7b97\u6cd5\u5bf9\u5b9e\u9645\u53d1\u5c04\u7ebf\u7684\u6b63\u786e\u5206\u7c7b\u51c6\u786e\u7387\u4e3a82.6% \uff0c\u800c\u5355\u72ec\u4f7f\u7528 SED \u62df\u5408\u6280\u672f\u7684\u51c6\u786e\u7387\u8f83\u4f4e(\u7ea650%) \uff0c\u539f\u56e0\u662f\u53ef\u7528\u7684\u5149\u5ea6\u6570\u636e\u91cf\u6709\u9650(< = 6\u6ce2\u6bb5)\u3002\u867d\u7136\u8be5\u7b97\u6cd5\u4e0d\u662f\u4e13\u95e8\u4e3a\u6b27\u51e0\u91cc\u5f97\u548c WFIRST \u8c03\u67e5\u8bbe\u8ba1\u7684\uff0c\u4f46\u5b83\u4ee3\u8868\u4e86\u7c7b\u4f3c\u7b97\u6cd5\u7684\u91cd\u8981\u5148\u9a71\uff0c\u5c06\u7528\u4e8e\u672a\u6765\u7684\u4efb\u52a1\u3002<\/span><\/section>\n

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    \u91cf\u5316\u7ebf\u6027\u3001\u79e9\u4e8f\u3001\u8d1d\u53f6\u65af\u786c\u573a\u5c42\u6790\u6210\u50cf<\/strong><\/span><\/p>\n

    \u4e2d<\/strong><\/span>\u6700\u5927\u540e\u9a8c\u4f30\u8ba1\u7684\u7a7a\u95f4\u5206\u8fa8\u7387<\/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

    Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12846<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Johannes Emmert,Steven Wagner,Kyle J. Daun<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate) and show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u57fa\u4e8e\u56fe\u50cf\u7684\u8bca\u65ad\u662f\u89e3\u91ca\u4e0a\u4e0b\u6587\u7684\u7a7a\u95f4\u5206\u8fa8\u7387\u3002\u65ad\u5c42\u56fe\u50cf\u91cd\u5efa\u4e5f\u662f\u5982\u6b64\u3002\u76ee\u524d\uff0c\u57fa\u4e8e\u7ecf\u9a8c\u9a71\u52a8\u7684\u7a7a\u95f4\u5206\u8fa8\u7387\u91cf\u5316\u65b9\u6cd5\u4f9d\u8d56\u4e8e\u57fa\u4e8e\u70b9\u6269\u6563\u51fd\u6570\u7684\u786e\u5b9a\u6027\u516c\u5f0f\uff0c\u5ffd\u7565\u4e86\u7edf\u8ba1\u5148\u9a8c\u4fe1\u606f\uff0c\u8fd9\u662f\u79e9\u4e8f\u5c42\u6790\u6210\u50cf\u4e0d\u53ef\u6216\u7f3a\u7684\u4e00\u90e8\u5206\u3002\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u91cd\u5efa\u534f\u65b9\u5dee(\u70b9\u4f30\u8ba1)\u7684\u7edf\u8ba1\u7a7a\u95f4\u5206\u8fa8\u7387\u6d4b\u5ea6\u65b9\u6cd5\uff0c\u8bc1\u660e\u4e86\u5148\u9a8c\u4fe1\u606f\u662f\u7a7a\u95f4\u5206\u8fa8\u7387\u7684\u4e00\u4e2a\u4e0b\u9650\u3002\u6b64\u5916\uff0c\u8003\u8651\u5230\u7a7a\u95f4\u5206\u8fa8\u7387\u7684\u4e0d\u5747\u5300\u6027\uff0c\u7a7a\u95f4\u5206\u8fa8\u7387\u6d4b\u5ea6\u53ef\u7528\u4e8e\u5c42\u6790\u6210\u50cf\u7cfb\u7edf\u7684\u8bbe\u8ba1\u3002<\/span><\/section>\n

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    \u6392\u653e\u6838\u7d20\u7ec4\u5206\u66ff\u4ee3\u6a21\u62df\u7684\u9ad8\u65af\u8fc7\u7a0b<\/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

    Gaussian Processes for Surrogate Modeling of Discharged Fuel Nuclide Compositions<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.12921<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Antonio Figueroa,Malte Goettsche<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Several applications such as nuclear forensics, nuclear fuel cycle simulations and sensitivity analysis require methods to quickly compute spent fuel nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have been pre-computed from nuclear reactor simulations for a grid of input parameters, using fits such as Cubic Spline. We propose the use of Gaussian Processes (GP) to create surrogate models, which not only provide nuclide compositions, but also the gradient and estimates of their prediction uncertainty. The former is useful for applications such as forward and inverse optimization problems, the latter for uncertainty quantification applications. For this purpose, we compare GP-based surrogate model performance with Cubic- Spline-based interpolators based on infinite lattice simulations of a CANDU 6 nuclear reactor using the SERPENT 2 code, considering burnup and temperature as input parameters. Additionally, we compare the performance of various grid sampling schemes to quasirandom sampling based on the Sobol sequence. We find that GP-based models perform significantly better in predicting spent fuel compositions than Cubic-Spline-based models, though requiring longer computational runtime. Furthermore, we show that the predicted nuclide uncertainties are reasonably accurate. While in the studied two-dimensional case, grid- and quasirandom sampling provide similar results, quasirandom sampling will be a more effective strategy in higher dimensional cases.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4e00\u4e9b\u5e94\u7528\uff0c\u5982\u6838\u53d6\u8bc1\uff0c\u6838\u71c3\u6599\u5faa\u73af\u6a21\u62df\u548c\u654f\u611f\u5ea6\u5206\u6790\uff0c\u9700\u8981\u5feb\u901f\u8ba1\u7b97\u5404\u79cd\u8f90\u7167\u5386\u53f2\u7684\u4e4f\u71c3\u6599\u6838\u7d20\u7ec4\u6210\u7684\u65b9\u6cd5\u3002\u4f20\u7edf\u4e0a\uff0c\u8fd9\u662f\u901a\u8fc7\u63d2\u503c\u4e4b\u95f4\u7684\u5355\u7ec4\u622a\u9762\u5df2\u9884\u5148\u8ba1\u7b97\u4ece\u6838\u53cd\u5e94\u5806\u6a21\u62df\u7684\u8f93\u5165\u53c2\u6570\u7f51\u683c\uff0c\u4f7f\u7528\u62df\u5408\uff0c\u5982\u4e09\u6b21\u6837\u6761\u3002\u6211\u4eec\u5efa\u8bae\u4f7f\u7528\u9ad8\u65af\u8fc7\u7a0b(GP)\u6765\u5efa\u7acb\u66ff\u4ee3\u6a21\u578b\uff0c\u5b83\u4e0d\u4ec5\u63d0\u4f9b\u6838\u7d20\u7ec4\u6210\uff0c\u800c\u4e14\u63d0\u4f9b\u5b83\u4eec\u7684\u9884\u6d4b\u4e0d\u786e\u5b9a\u6027\u7684\u68af\u5ea6\u548c\u4f30\u8ba1\u3002\u524d\u8005\u9002\u7528\u4e8e\u6b63\u5411\u548c\u53cd\u5411\u4f18\u5316\u95ee\u9898\uff0c\u540e\u8005\u9002\u7528\u4e8e\u4e0d\u786e\u5b9a\u6027\u91cf\u5316\u5e94\u7528\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u91c7\u7528 serp2\u7a0b\u5e8f\u5bf9 candu6\u53cd\u5e94\u5806\u8fdb\u884c\u4e86\u65e0\u9650\u683c\u70b9\u6a21\u62df\uff0c\u5c06\u71c3\u8017\u548c\u6e29\u5ea6\u4f5c\u4e3a\u8f93\u5165\u53c2\u6570\uff0c\u6bd4\u8f83\u4e86\u57fa\u4e8e gp \u7684\u4ee3\u7406\u6a21\u578b\u548c\u57fa\u4e8e\u4e09\u6b21\u6837\u6761\u63d2\u503c\u7684\u4ee3\u7406\u6a21\u578b\u7684\u6027\u80fd\u3002\u6b64\u5916\uff0c\u6211\u4eec\u6bd4\u8f83\u4e86\u5404\u79cd\u7f51\u683c\u91c7\u6837\u65b9\u6848\u548c\u57fa\u4e8e Sobol \u5e8f\u5217\u7684\u51c6\u968f\u673a\u91c7\u6837\u65b9\u6848\u7684\u6027\u80fd\u3002\u6211\u4eec\u53d1\u73b0\u57fa\u4e8e gp \u7684\u6a21\u578b\u5728\u9884\u6d4b\u4e4f\u71c3\u6599\u6210\u5206\u65b9\u9762\u6bd4\u57fa\u4e8e\u4e09\u6b21\u6837\u6761\u7684\u6a21\u578b\u8868\u73b0\u5f97\u66f4\u597d\uff0c\u5c3d\u7ba1\u9700\u8981\u66f4\u957f\u7684\u8ba1\u7b97\u65f6\u95f4\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86\u9884\u6d4b\u7684\u6838\u7d20\u4e0d\u786e\u5b9a\u5ea6\u662f\u5408\u7406\u7684\u3002\u5728\u4e8c\u7ef4\u60c5\u51b5\u4e0b\uff0c\u7f51\u683c\u548c\u51c6\u968f\u673a\u62bd\u6837\u5f97\u5230\u4e86\u76f8\u4f3c\u7684\u7ed3\u679c\uff0c\u800c\u5728\u9ad8\u7ef4\u60c5\u51b5\u4e0b\uff0c\u51c6\u968f\u673a\u62bd\u6837\u5c06\u662f\u4e00\u79cd\u66f4\u6709\u6548\u7684\u7b56\u7565\u3002<\/span><\/section>\n

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    \u591a\u4e2a\u8f6f\u4ef6\u4f20\u611f\u5668\u7684\u65e0\u76d1\u7763\u96c6\u6210: <\/strong><\/span><\/p>\n

    \u4e00\u79cd\u5355\u901a\u9053\u6216\u53cc\u901a\u9053<\/strong><\/span><\/p>\n

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

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

    Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13054<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    John Malik,Yu-Ting Lin,Ronen Talmon,Hau-Tieng Wu<\/span><\/section>\n

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    Abstract\uff1a<\/span><\/strong>While several electrocardiogram-derived respiratory (EDR) algorithms have been proposed to extract breathing activity from a single-channel ECG signal, conclusively identifying a superior technique is challenging. We propose viewing each EDR algorithm as a {em software sensor} that records the breathing activity from the ECG signal, and ensembling those software sensors to achieve a higher quality EDR signal. We refer to the output of the proposed ensembling algorithm as the {em ensembled EDR}. We test the algorithm on a large scale database of 116 whole-night polysomnograms and compare the ensembled EDR signal with four respiratory signals recorded from four different hardware sensors. The proposed algorithm consistently improves upon other algorithms, and we envision its clinical value and its application in future healthcare.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u867d\u7136\u4eba\u4eec\u5df2\u7ecf\u63d0\u51fa\u4e86\u591a\u79cd\u5fc3\u7535\u56fe\u884d\u751f\u7684\u547c\u5438\u7b97\u6cd5(EDR)\u6765\u4ece\u5355\u901a\u9053 ECG \u4fe1\u53f7\u4e2d\u63d0\u53d6\u547c\u5438\u6d3b\u52a8\uff0c\u4f46\u662f\u6700\u7ec8\u786e\u5b9a\u4e00\u79cd\u4f18\u8d8a\u7684\u6280\u672f\u4ecd\u7136\u662f\u4e00\u4e2a\u6311\u6218\u3002\u6211\u4eec\u5efa\u8bae\u5c06\u6bcf\u4e2a EDR \u7b97\u6cd5\u89c6\u4e3a\u4e00\u4e2a{ em \u8f6f\u4ef6\u4f20\u611f\u5668} \uff0c\u4ece ECG \u4fe1\u53f7\u4e2d\u8bb0\u5f55\u547c\u5438\u6d3b\u52a8\uff0c\u5e76\u5c06\u8fd9\u4e9b\u8f6f\u4ef6\u4f20\u611f\u5668\u96c6\u6210\u4ee5\u83b7\u5f97\u66f4\u9ad8\u8d28\u91cf\u7684 EDR \u4fe1\u53f7\u3002\u6211\u4eec\u5c06\u6240\u63d0\u51fa\u7684\u96c6\u6210\u7b97\u6cd5\u7684\u8f93\u51fa\u79f0\u4e3a{ em \u96c6\u6210 EDR }\u3002\u6211\u4eec\u5728116\u4e2a\u5168\u591c\u591a\u5bfc\u7761\u7720\u56fe\u7684\u5927\u89c4\u6a21\u6570\u636e\u5e93\u4e0a\u6d4b\u8bd5\u4e86\u8be5\u7b97\u6cd5\uff0c\u5e76\u5c06 EDR \u96c6\u6210\u4fe1\u53f7\u4e0e4\u4e2a\u4e0d\u540c\u786c\u4ef6\u4f20\u611f\u5668\u8bb0\u5f55\u76844\u4e2a\u547c\u5438\u4fe1\u53f7\u8fdb\u884c\u4e86\u6bd4\u8f83\u3002\u672c\u6587\u63d0\u51fa\u7684\u7b97\u6cd5\u4e0d\u65ad\u6539\u8fdb\u5176\u4ed6\u7b97\u6cd5\uff0c\u6211\u4eec\u5c55\u671b\u5176\u4e34\u5e8a\u4ef7\u503c\u53ca\u5176\u5728\u672a\u6765\u533b\u7597\u4fdd\u5065\u4e2d\u7684\u5e94\u7528\u3002<\/span><\/section>\n

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    \u7f16\u8f91\uff1a\u738b\u5efa\u840d<\/section>\n<\/section>\n<\/blockquote>\n
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    <\/strong>\u8fd1\u8ddd\u79bb\u611f\u67d3\u4f20\u64ad\u7684\u8499\u7279\u5361\u7f57\u6a21\u62df\u7814\u7a76 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901239\u7bc7<\/span><\/a>
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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    \u82f1\u56fd\u65b0\u51a0\u80ba\u708e\u7981\u95ed: \u5bf9\u7a7a\u6c14\u6c61\u67d3\u6709\u4ec0\u4e48\u5f71\u54cd | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7<\/strong><\/span><\/a>
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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901230\u7bc7<\/span><\/strong><\/a>
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    \u96c6\u667a\u4ff1\u4e50\u90e8QQ\u7fa4\uff5c877391004<\/span><\/strong><\/p>\n

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

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

    \u672c\u6587\u7531\u673a\u5668\u7ffb\u8bd1\uff0c\u4ec5\u4f9b\u53c2\u8003\uff0c\u611f\u5174\u8da3\u8bf7\u67e5\u9605\u8bba\u6587\u539f\u6587 \u6838\u5fc3\u901f\u9012 \u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c\u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc\uff1b \u5229\u7528\u591a\u65e5\u51fa\u884c\u8ba2\u5355\u6570\u636e\u63cf\u8ff0\u53eb\u8f66\u6d41\u52a8\u6027: \u4e2d\u56fd\u5317\u4eac\u7684\u6848\u4f8b\u7814\u7a76\uff1b \u6709\u754c\u5e73\u9762\u5730\u6708\u7cfb\u4e2d\u7684\u5e8f-\u6df7\u6c8c\u5e8f\u548c\u4e0d\u53d8\u6d41\u5f62\uff1b \u538b\u7f29\u76f8\u7a7a\u95f4\u68c0\u6d4b\u975e\u7ebf\u6027\u52a8\u6001\u7cfb\u7edf\u7684\u72b6\u6001\u53d8\u5316\uff1b \u57284\u5ea6\u4e34\u754c\u70b9\u5730\u56fe\u4e2d\u5b58\u5728\u500d\u5468\u671f\u91cd\u6574\u5316\u4e0d\u52a8\u70b9\u7684\u4e25\u683c\u7535\u8111\u534f…<\/p>\n","protected":false},"author":1,"featured_media":20177,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20179"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20179"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20179\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/20177"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20179"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20179"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20179"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=20179"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}