{"id":20220,"date":"2020-06-29T08:00:00","date_gmt":"2020-06-29T00:00:00","guid":{"rendered":"https:\/\/swarma.org\/?p=20220"},"modified":"2020-06-29T08:00:00","modified_gmt":"2020-06-29T00:00:00","slug":"%e5%ad%a6%e4%b9%a0%e5%a4%8d%e6%9d%82%e5%a4%9a%e5%b0%ba%e5%ba%a6%e7%b3%bb%e7%bb%9f%e7%9a%84%e6%9c%89%e6%95%88%e5%8a%a8%e5%8a%9b%e5%ad%a6-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba%e6%96%87","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20220","title":{"rendered":"\u5b66\u4e60\u590d\u6742\u591a\u5c3a\u5ea6\u7cfb\u7edf\u7684\u6709\u6548\u52a8\u529b\u5b66 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901214\u7bc7"},"content":{"rendered":"
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    \u8ba1\u7b97\u590d\u5236\u6570\u5b57\u7684 matlab \u4ee3\u7801\u53ca\u5176\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75ab\u60c5\u7206\u53d1\u4e2d\u7684\u5e94\u7528\uff1b<\/span><\/section>\n<\/li>\n
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    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Learning the Effective Dynamics of Complex Multiscale Systems<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13431<\/span><\/h2>\n

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

    Pantelis R. Vlachas,Georgios Arampatzis,Caroline Uhler,Petros Koumoutsakos<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Simulations of complex multiscale systems are essential for science and technology ranging from weather forecasting to aircraft design. The predictive capabilities of simulations hinges on their capacity to capture the governing system dynamics. Large scale simulations, resolving all spatiotemporal scales, provide invaluable insight at a high computational cost. In turn, simulations using reduced order models are affordable but their veracity hinges often on linearisation and\/or heuristics. Here we present a novel systematic framework to extract and forecast accurately the effective dynamics (LED) of complex systems with multiple spatio-temporal scales. The framework fuses advanced machine learning algorithms with equation-free approaches. It deploys autoencoders to obtain a mapping between fine and coarse grained representations of the system and learns to forecast the latent space dynamics using recurrent neural networks. We compare the LED framework with existing approaches on a number of benchmark problems and demonstrate reduction in computational efforts by several orders of magnitude without sacrificing the accuracy of the system.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u590d\u6742\u591a\u5c3a\u5ea6\u7cfb\u7edf\u7684\u6a21\u62df\u5bf9\u4ece\u5929\u6c14\u9884\u62a5\u5230\u98de\u673a\u8bbe\u8ba1\u7684\u79d1\u5b66\u548c\u6280\u672f\u81f3\u5173\u91cd\u8981\u3002\u6a21\u62df\u7684\u9884\u6d4b\u80fd\u529b\u53d6\u51b3\u4e8e\u5b83\u4eec\u6355\u6349\u7ba1\u7406\u7cfb\u7edf\u52a8\u6001\u7684\u80fd\u529b\u3002\u5927\u89c4\u6a21\u6a21\u62df\uff0c\u89e3\u51b3\u6240\u6709\u7684\u65f6\u7a7a\u5c3a\u5ea6\uff0c\u63d0\u4f9b\u4e86\u5b9d\u8d35\u7684\u6d1e\u5bdf\u529b\u5728\u9ad8\u8ba1\u7b97\u6210\u672c\u3002\u53cd\u8fc7\u6765\uff0c\u4f7f\u7528\u964d\u9636\u6a21\u578b\u7684\u6a21\u62df\u662f\u53ef\u627f\u53d7\u7684\uff0c\u4f46\u5176\u51c6\u786e\u6027\u5f80\u5f80\u53d6\u51b3\u4e8e\u7ebf\u6027\u5316\u548c \/ \u6216\u542f\u53d1\u5f0f\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u7cfb\u7edf\u6846\u67b6\uff0c\u7528\u4e8e\u7cbe\u786e\u63d0\u53d6\u548c\u9884\u6d4b\u5177\u6709\u591a\u65f6\u7a7a\u5c3a\u5ea6\u7684\u590d\u6742\u7cfb\u7edf\u7684\u6709\u6548\u52a8\u529b\u5b66(LED)\u3002\u8be5\u6846\u67b6\u878d\u5408\u4e86\u5148\u8fdb\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e0e\u65b9\u7a0b\u81ea\u7531\u7684\u65b9\u6cd5\u3002\u5b83\u5229\u7528\u81ea\u52a8\u7f16\u7801\u5668\u83b7\u5f97\u7cfb\u7edf\u7684\u7ec6\u7c92\u5ea6\u8868\u793a\u548c\u7c97\u7c92\u5ea6\u8868\u793a\u4e4b\u95f4\u7684\u6620\u5c04\uff0c\u5e76\u5229\u7528\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60\u9884\u6d4b\u6f5c\u5728\u7684\u7a7a\u95f4\u52a8\u6001\u3002\u6211\u4eec\u6bd4\u8f83\u4e86 LED \u6846\u67b6\u548c\u73b0\u6709\u65b9\u6cd5\u5728\u4e00\u4e9b\u57fa\u51c6\u95ee\u9898\u4e0a\u7684\u4f18\u7f3a\u70b9\uff0c\u8bc1\u660e\u4e86\u5728\u4e0d\u727a\u7272\u7cfb\u7edf\u7cbe\u786e\u6027\u7684\u524d\u63d0\u4e0b\uff0c\u901a\u8fc7\u51e0\u4e2a\u6570\u91cf\u7ea7\u7684\u52aa\u529b\u51cf\u5c11\u4e86\u8ba1\u7b97\u91cf\u3002<\/span><\/p>\n


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

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

    Homoclinic puzzles and chaos in a nonlinear laser model<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13812<\/span><\/p>\n

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

    K. Pusuluri,H. G. E. Meijer,A. L. Shilnikov<\/span><\/p>\n


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    Abstract\uff1a<\/span><\/strong>We present a case study elaborating on the multiplicity and self-similarity of homoclinic and heteroclinic bifurcation structures in the 2D and 3D parameter spaces of a nonlinear laser model with a Lorenz-like chaotic attractor. In a symbiotic approach combining the traditional parameter continuation methods using MatCont and a newly developed technique called the Deterministic Chaos Prospector (DCP) utilizing symbolic dynamics on fast parallel computing hardware with graphics processing units (GPUs), we exhibit how specific codimension-two bifurcations originate and pattern regions of chaotic and simple dynamics in this classical model. We show detailed computational reconstructions of key bifurcation structures such as Bykov T-point spirals and inclination flips in 2D parameter space, as well as the spatial organization and 3D embedding of bifurcation surfaces, parametric saddles, and isolated closed curves (isolas).<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u7ed9\u51fa\u4e86\u4e00\u4e2a\u5177\u6709\u7c7b lorenz \u6df7\u6c8c\u5438\u5f15\u5b50\u7684\u975e\u7ebf\u6027\u6fc0\u5149\u6a21\u578b\u7684\u4e8c\u7ef4\u548c\u4e09\u7ef4\u53c2\u6570\u7a7a\u95f4\u4e2d\u540c\u5bbf\u7ed3\u6784\u548c\u5f02\u5bbf\u5206\u5c94\u7ed3\u6784\u7684\u591a\u91cd\u6027\u548c\u81ea\u76f8\u4f3c\u6027\u7684\u4e2a\u4f8b\u7814\u7a76\u3002\u5229\u7528 MatCont \u7684\u4f20\u7edf\u53c2\u6570\u5ef6\u62d3\u65b9\u6cd5\u548c\u4e00\u79cd\u65b0\u53d1\u5c55\u7684\u5229\u7528\u5e26\u56fe\u5f62\u5904\u7406\u5355\u5143\u7684\u5feb\u901f\u5e76\u884c\u8ba1\u7b97\u786c\u4ef6\u7684\u786e\u5b9a\u6027\u6df7\u6c8c\u9884\u6d4b\u6280\u672f(DCP)\u76f8\u7ed3\u5408\u7684\u5171\u751f\u65b9\u6cd5\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u8fd9\u4e2a\u7ecf\u5178\u6a21\u578b\u4e2d\u7279\u5b9a\u4f59\u7ef4\u6570-2\u5206\u53c9\u7684\u4ea7\u751f\u548c\u6df7\u6c8c\u53ca\u7b80\u5355\u52a8\u529b\u5b66\u7684\u6a21\u5f0f\u533a\u57df\u3002\u7ed9\u51fa\u4e86\u4e8c\u7ef4\u53c2\u6570\u7a7a\u95f4\u4e2d Bykov t \u70b9\u87ba\u65cb\u7ebf\u548c\u503e\u659c\u7ffb\u8f6c\u7ebf\u7b49\u5173\u952e\u5206\u5c94\u7ed3\u6784\u7684\u8be6\u7ec6\u8ba1\u7b97\u91cd\u6784\uff0c\u4ee5\u53ca\u5206\u5c94\u66f2\u9762\u3001\u53c2\u6570\u978d\u70b9\u548c\u5b64\u7acb\u95ed\u66f2\u7ebf\u7684\u7a7a\u95f4\u7ec4\u7ec7\u548c\u4e09\u7ef4\u5d4c\u5165\u3002<\/span><\/p>\n


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

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

    An approach to synaptic learning for autonomous motor control<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13471<\/span><\/p>\n

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

    Sergio Verduzco-Flores,William Dorrell,Erik DeSchutter<\/span><\/p>\n


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    Abstract\uff1a<\/span><\/strong>In the realm of motor control, artificial agents cannot match the performance of their biological counterparts. We thus explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. The architecture consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control reduces the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing to learn more complex actions. We discuss how this approach could be extended to hierarchical architectures.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd0\u52a8\u63a7\u5236\u9886\u57df\uff0c\u4eba\u5de5\u5236\u5242\u7684\u6027\u80fd\u65e0\u6cd5\u4e0e\u751f\u7269\u5236\u5242\u76f8\u6bd4\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u63a2\u7d22\u4e86\u4e00\u4e2a\u795e\u7ecf\u63a7\u5236\u67b6\u6784\uff0c\u65e2\u662f\u751f\u7269\u5b66\u4e0a\u53ef\u4fe1\u7684\uff0c\u5e76\u80fd\u591f\u5b8c\u5168\u81ea\u4e3b\u5b66\u4e60\u3002\u8be5\u4f53\u7cfb\u7ed3\u6784\u7531\u53cd\u9988\u63a7\u5236\u5668\u7ec4\u6210\uff0c\u8fd9\u4e9b\u53cd\u9988\u63a7\u5236\u5668\u901a\u8fc7\u9009\u62e9\u9a71\u52a8\u5b83\u4eec\u7684\u9519\u8bef\u6765\u5b66\u4e60\u8fbe\u5230\u6240\u9700\u7684\u72b6\u6001\u3002\u8fd9\u79cd\u9009\u62e9\u901a\u8fc7\u4e00\u7cfb\u5217\u5fae\u5206\u8d6b\u5e03\u5b66\u4e60\u89c4\u5219\u53d1\u751f\uff0c\u901a\u8fc7\u4e0e\u73af\u5883\u7684\u4e92\u52a8\uff0c\u53ef\u4ee5\u5b66\u4f1a\u63a7\u5236\u7cfb\u7edf\u7684\u9519\u8bef\u5355\u8c03\u54cd\u5e94\u7684\u63a7\u5236\u4fe1\u53f7\u3002\u6211\u4eec\u63a5\u4e0b\u6765\u5c55\u793a\u7684\u662f\uff0c\u5728\u66f4\u4e00\u822c\u7684\u60c5\u51b5\u4e0b\uff0c\u795e\u7ecf\u5143 \/ \u5f3a\u5316\u5b66\u4e60\u53ef\u4ee5\u4e0e\u53cd\u9988\u63a7\u5236\u5668\u8026\u5408\uff0c\u4ee5\u51cf\u5c11\u4e0e\u63a7\u5236\u4fe1\u53f7\u975e\u5355\u8c03\u4ea7\u751f\u7684\u8bef\u5dee\u3002\u53cd\u9988\u63a7\u5236\u7684\u4f7f\u7528\u964d\u4f4e\u4e86\u5f3a\u5316\u5b66\u4e60\u95ee\u9898\u7684\u590d\u6742\u6027\uff0c\u56e0\u4e3a\u53ea\u6709\u4e00\u4e2a\u671f\u671b\u503c\u5fc5\u987b\u5b66\u4e60\uff0c\u63a7\u5236\u5668\u5904\u7406\u5982\u4f55\u8fbe\u5230\u5b83\u7684\u7ec6\u8282\u3002\u8fd9\u4f7f\u5f97\u5b66\u4e60\u51fd\u6570\u53d8\u5f97\u66f4\u7b80\u5355\uff0c\u6f5c\u5728\u5730\u5141\u8bb8\u5b66\u4e60\u66f4\u590d\u6742\u7684\u52a8\u4f5c\u3002\u6211\u4eec\u5c06\u8ba8\u8bba\u5982\u4f55\u5c06\u8fd9\u79cd\u65b9\u6cd5\u6269\u5c55\u5230\u5c42\u6b21\u7ed3\u6784\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u53cc\u76f8\u4f3c\u53d8\u6362\u8026\u5408\u56e2\u7c07\u7406\u8bba<\/strong><\/span><\/p>\n

    \u7684\u5206\u5b50\u57fa\u6001\u79bb\u6563\u65f6\u95f4\u52a8\u529b\u5b66<\/strong><\/span><\/strong><\/p>\n

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


    <\/h2>\n

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

    Discrete-Time Dynamics of Molecular Ground State with a Double Similarity Transformed Coupled Cluster Theory<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13691<\/span><\/p>\n

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

    Valay Agarawal,Anish Chakraborty,Rahul Maitra<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The discrete-time propagation of a double similarity transformed Coupled Cluster theory with input perturbation is studied. The coupled iterative scheme to solve the ground state Schr{“o}dinger equation is cast as a multivariate logistic map, the solutions show the universal Feigenbaum dynamics. Using recurrence analysis, it is shown that the dynamics is dictated by a small subgroup of cluster operators, mostly those involving chemically active orbitals, whereas all other cluster operators with smaller amplitudes are enslaved.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7814\u7a76\u4e86\u5177\u6709\u8f93\u5165\u6270\u52a8\u7684\u53cc\u76f8\u4f3c\u53d8\u6362\u8026\u5408\u7c07\u7406\u8bba\u7684\u79bb\u6563\u4f20\u64ad\u95ee\u9898\u3002\u5c06\u6c42\u89e3\u57fa\u6001\u859b\u5b9a\u8c14\u65b9\u7a0b\u7684\u8026\u5408\u8fed\u4ee3\u683c\u5f0f\u8f6c\u5316\u4e3a\u591a\u5143 logistic \u6620\u5c04\uff0c\u5f97\u5230\u4e86\u901a\u7528\u7684 Feigenbaum \u52a8\u529b\u5b66\u89e3\u3002\u5229\u7528\u9012\u5f52\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u8fd9\u79cd\u52a8\u529b\u5b66\u662f\u7531\u4e00\u5c0f\u90e8\u5206\u96c6\u7fa4\u7b97\u5b50\u51b3\u5b9a\u7684\uff0c\u5927\u591a\u6570\u96c6\u7fa4\u7b97\u5b50\u6d89\u53ca\u5316\u5b66\u6d3b\u6027\u8f68\u9053\uff0c\u800c\u6240\u6709\u5176\u4ed6\u632f\u5e45\u8f83\u5c0f\u7684\u96c6\u7fa4\u7b97\u5b50\u90fd\u53d7\u5230\u675f\u7f1a\u3002<\/span><\/p>\n


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    \u6c34\u5e73\u968f\u673a\u529b\u9a71\u52a8\u7684\u53ef\u5012\u6446\u6cd5: <\/strong><\/span><\/p>\n

    \u6c38\u4e0d\u4e0b\u843d\u8f68\u8ff9\u548c\u8d85\u5bf9\u79f0\u6027\u7684\u7edf\u8ba1<\/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

    Inverted pendulum driven by a horizontal random force: statistics of the never-falling trajectory and supersymmetry<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13819<\/span><\/h2>\n

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

    Nikolai A. Stepanov,Mikhail A. Skvortsov<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We study stochastic dynamics of an inverted pendulum subject to a random force in the horizontal direction (Whitney’s problem). Considered on the entire time axis, the problem admits a unique solution that always remains in the upper half plane. We formulate the problem of statistical description of this never-falling trajectory and solve it by a field-theoretical technique assuming a white-noise driving. In our approach based on the supersymmetric formalism of Parisi and Sourlas, statistic properties of the never-falling trajectory are expressed in terms of the zero mode of the corresponding transfer-matrix Hamiltonian. The emerging mathematical structure is similar to that of the Fokker-Planck equation, which however is written for the “square root” of the probability distribution function. Our results for the statistics of the non-falling trajectory are in perfect agreement with direct numerical simulations of the stochastic pendulum equation. In the limit of strong driving (no gravitation), we obtain an exact analytical solution for the instantaneous joint probability distribution function of the pendulum’s angle and its velocity.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7814\u7a76\u4e86\u53ef\u5012\u6446\u6cd5\u5728\u6c34\u5e73\u65b9\u5411\u4e0a\u53d7\u5230\u968f\u673a\u529b\u7684\u968f\u673a\u52a8\u529b\u5b66(\u60e0\u7279\u5c3c\u95ee\u9898)\u3002\u4ece\u6574\u4e2a\u65f6\u95f4\u8f74\u6765\u770b\uff0c\u8fd9\u4e2a\u95ee\u9898\u6709\u4e00\u4e2a\u552f\u4e00\u7684\u89e3\uff0c\u5b83\u603b\u662f\u505c\u7559\u5728\u4e0a\u534a\u5e73\u9762\u4e0a\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u8fd9\u79cd\u6c38\u4e0d\u4e0b\u843d\u8f68\u8ff9\u7684\u7edf\u8ba1\u63cf\u8ff0\u95ee\u9898\uff0c\u5e76\u7528\u573a\u8bba\u6280\u672f\u89e3\u51b3\u4e86\u767d\u566a\u58f0\u9a71\u52a8\u4e0b\u7684\u6c38\u4e0d\u4e0b\u843d\u8f68\u8ff9\u3002\u672c\u6587\u57fa\u4e8e Parisi \u548c Sourlas \u7684\u8d85\u5bf9\u79f0\u5f62\u5f0f\uff0c\u7528\u76f8\u5e94\u7684\u4f20\u9012\u77e9\u9635\u54c8\u5bc6\u987f\u91cf\u7684\u96f6\u6a21\u8868\u793a\u6c38\u4e0d\u4e0b\u964d\u8f68\u8ff9\u7684\u7edf\u8ba1\u6027\u8d28\u3002\u65b0\u51fa\u73b0\u7684\u6570\u5b66\u7ed3\u6784\u7c7b\u4f3c\u4e8e\u798f\u514b-\u666e\u6717\u514b\u65b9\u7a0b\uff0c\u4f46\u662f\u5b83\u662f\u4e3a\u6982\u7387\u5206\u5e03\u51fd\u6570\u7684\u201c\u5e73\u65b9\u6839\u201d\u800c\u5199\u7684\u3002\u6211\u4eec\u7684\u975e\u4e0b\u843d\u8f68\u8ff9\u7edf\u8ba1\u7ed3\u679c\u4e0e\u968f\u673a\u6446\u65b9\u7a0b\u7684\u76f4\u63a5\u6570\u503c\u6a21\u62df\u7ed3\u679c\u5b8c\u5168\u4e00\u81f4\u3002\u5728\u5f3a\u529b\u9a71\u52a8(\u65e0\u5f15\u529b)\u7684\u6781\u9650\u4e0b\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u6446\u89d2\u548c\u6446\u901f\u7684\u77ac\u65f6\u8054\u5408\u5206\u5e03\u51fd\u6570\u7684\u7cbe\u786e\u89e3\u6790\u89e3\u3002<\/span><\/p>\n


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    \u5177\u6709\u53cd\u9988\u7535\u6d41\u7684\u81ea\u65cb\u7535\u5b50<\/strong><\/span><\/p>\n

    \u50a8\u5b58\u5668\u8bb0\u5fc6\u51fd\u6570\u7684\u5468\u671f\u7ed3\u6784<\/strong><\/p>\n

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

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

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

    Periodic structure of memory function in spintronics reservoir with feedback current<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13404<\/span><\/h2>\n

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

    Terufumi Yamaguchi,Nozomi Akashi,Sumito Tsunegi,Hitoshi Kubota,Kohei Nakajima,Tomohiro Taniguchi<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The role of the feedback effect on physical reservoir computing is studied theoretically by solving the vortex-core dynamics in a nanostructured ferromagnet. Although the spin-transfer torque due to the feedback current makes the vortex dynamics complex, it is clarified that the feedback effect does not always contribute to the enhancement of the memory function in a physical reservoir. The memory function, characterized by the correlation coefficient between the input data and the dynamical response of the vortex core, becomes large when the delay time of the feedback current is not an integral multiple of the pulse width. On the other hand, the memory function remains small when the delay time is an integral multiple of the pulse width. As a result, a periodic behavior for the short-term memory capacity is observed with respect to the delay time, the phenomenon of which can be attributed to correlations between the virtual neurons via the feedback current.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u901a\u8fc7\u6c42\u89e3\u7eb3\u7c73\u7ed3\u6784\u94c1\u78c1\u4f53\u4e2d\u7684\u6da1\u6838\u52a8\u529b\u5b66\uff0c\u4ece\u7406\u8bba\u4e0a\u7814\u7a76\u4e86\u53cd\u9988\u6548\u5e94\u5bf9\u7269\u7406\u6cb9\u85cf\u8ba1\u7b97\u7684\u5f71\u54cd\u3002\u867d\u7136\u53cd\u9988\u7535\u6d41\u5f15\u8d77\u7684\u81ea\u65cb\u8f6c\u79fb\u626d\u77e9\u4f7f\u6da1\u65cb\u52a8\u529b\u5b66\u590d\u6742\u5316\uff0c\u4f46\u662f\u53cd\u9988\u6548\u5e94\u5e76\u4e0d\u603b\u662f\u6709\u52a9\u4e8e\u63d0\u9ad8\u7269\u7406\u50a8\u5c42\u7684\u8bb0\u5fc6\u529f\u80fd\u3002\u5f53\u53cd\u9988\u7535\u6d41\u7684\u5ef6\u8fdf\u65f6\u95f4\u4e0d\u662f\u8109\u51b2\u5bbd\u5ea6\u7684\u6574\u6570\u500d\u65f6\uff0c\u8bb0\u5fc6\u51fd\u6570\u62e5\u6709\u5c5e\u6027\u8f93\u5165\u6570\u636e\u548c\u6da1\u6838\u52a8\u6001\u54cd\u5e94\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u53d8\u5927\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5f53\u5ef6\u8fdf\u65f6\u95f4\u4e3a\u8109\u51b2\u5bbd\u5ea6\u7684\u6574\u6570\u500d\u65f6\uff0c\u8bb0\u5fc6\u51fd\u6570\u4ecd\u7136\u5f88\u5c0f\u3002\u7ed3\u679c\u8868\u660e\uff0c\u77ed\u65f6\u8bb0\u5fc6\u5bb9\u91cf\u968f\u5ef6\u8fdf\u65f6\u95f4\u5448\u73b0\u5468\u671f\u6027\u53d8\u5316\uff0c\u8fd9\u79cd\u73b0\u8c61\u53ef\u4ee5\u5f52\u7ed3\u4e3a\u865a\u62df\u795e\u7ecf\u5143\u4e4b\u95f4\u901a\u8fc7\u53cd\u9988\u7535\u6d41\u7684\u76f8\u5173\u6027\u3002<\/span><\/p>\n


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    \u5e94\u7528\u4e8e\u4f20\u67d3\u75c5\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2<\/strong><\/span><\/p>\n

    \u80ba\u708e\u6d41\u884c\u75c5\u7684 PECAIQR \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

    PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13693<\/span><\/h2>\n

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

    Richard Bao,August Chen,Jethin Gowda,Shiva Mudide<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The Covid-19 pandemic has made clear the need to improve modern multivariate time-series forecasting models. Current state of the art predictions of future daily deaths and, especially, hospital resource usage have confidence intervals that are unacceptably wide. Policy makers and hospitals require accurate forecasts to make informed decisions on passing legislation and allocating resources. We used US county-level data on daily deaths and population statistics to forecast future deaths. We extended the SIR epidemiological model to a novel model we call the PECAIQR model. It adds several new variables and parameters to the naive SIR model by taking into account the ramifications of the partial quarantining implemented in the US. We fitted data to the model parameters with numerical integration. Because of the fit degeneracy in parameter space and non-constant nature of the parameters, we developed several methods to optimize our fit, such as training on the data tail and training on specific policy regimes. We use cross-validation to tune our hyper parameters at the county level and generate a CDF for future daily deaths. For predictions made from training data up to May 25th, we consistently obtained an averaged pinball loss score of 0.096 on a 14 day forecast. We finally present examples of possible avenues for utility from our model. We generate longer-time horizon predictions over various 1-month windows in the past, forecast how many medical resources such as ventilators and ICU beds will be needed in counties, and evaluate the efficacy of our model in other countries.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u6e05\u695a\u5730\u8868\u660e\u4e86\u6539\u8fdb\u73b0\u4ee3\u591a\u53d8\u91cf\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6a21\u578b\u7684\u5fc5\u8981\u6027\u3002\u76ee\u524d\u6700\u5148\u8fdb\u7684\u9884\u6d4b\u672a\u6765\u6bcf\u65e5\u6b7b\u4ea1\uff0c\u7279\u522b\u662f\u533b\u9662\u8d44\u6e90\u4f7f\u7528\u7684\u7f6e\u4fe1\u533a\u95f4\u662f\u4e0d\u53ef\u63a5\u53d7\u7684\u5bbd\u3002\u51b3\u7b56\u8005\u548c\u533b\u9662\u9700\u8981\u51c6\u786e\u7684\u9884\u6d4b\uff0c\u4ee5\u4fbf\u5c31\u901a\u8fc7\u7acb\u6cd5\u548c\u5206\u914d\u8d44\u6e90\u4f5c\u51fa\u77e5\u60c5\u7684\u51b3\u5b9a\u3002\u6211\u4eec\u4f7f\u7528\u7f8e\u56fd\u53bf\u7ea7\u7684\u6bcf\u65e5\u6b7b\u4ea1\u548c\u4eba\u53e3\u7edf\u8ba1\u6570\u636e\u6765\u9884\u6d4b\u672a\u6765\u7684\u6b7b\u4ea1\u4eba\u6570\u3002\u6211\u4eec\u5c06 SIR \u6d41\u884c\u75c5\u5b66\u6a21\u578b\u63a8\u5e7f\u5230\u4e00\u4e2a\u65b0\u7684\u6a21\u578b\u2014\u2014 PECAIQR \u6a21\u578b\u3002\u5b83\u901a\u8fc7\u8003\u8651\u7f8e\u56fd\u5b9e\u65bd\u7684\u90e8\u5206\u9694\u79bb\u7684\u540e\u679c\uff0c\u4e3a\u539f\u672c\u7684 SIR \u6a21\u578b\u589e\u52a0\u4e86\u51e0\u4e2a\u65b0\u7684\u53d8\u91cf\u548c\u53c2\u6570\u3002\u6211\u4eec\u4f7f\u7528\u6570\u503c\u79ef\u5206\u8f6f\u4ef6\u5bf9\u6a21\u578b\u53c2\u6570\u8fdb\u884c\u4e86\u62df\u5408\u3002\u7531\u4e8e\u53c2\u6570\u7a7a\u95f4\u7684\u62df\u5408\u7b80\u5e76\u6027\u548c\u53c2\u6570\u7684\u975e\u5e38\u6570\u6027\u8d28\uff0c\u6211\u4eec\u53d1\u5c55\u4e86\u51e0\u79cd\u65b9\u6cd5\u6765\u4f18\u5316\u6211\u4eec\u7684\u62df\u5408\uff0c\u5982\u6570\u636e\u5c3e\u7684\u8bad\u7ec3\u548c\u5177\u4f53\u7684\u653f\u7b56\u5236\u5ea6\u7684\u8bad\u7ec3\u3002\u6211\u4eec\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8c03\u6574\u6211\u4eec\u5728\u53bf\u4e00\u7ea7\u7684\u8d85\u53c2\u6570\uff0c\u5e76\u4e3a\u672a\u6765\u6bcf\u65e5\u6b7b\u4ea1\u751f\u6210\u4e00\u4e2a CDF\u3002\u5bf9\u4e8e5\u670825\u65e5\u4e4b\u524d\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u6211\u4eec\u4e00\u76f4\u572814\u5929\u7684\u9884\u6d4b\u4e2d\u83b7\u5f97\u4e860.096\u7684\u5e73\u5747\u5206\u3002\u6700\u540e\uff0c\u6211\u4eec\u7ed9\u51fa\u4e86\u4ece\u6211\u4eec\u7684\u6a21\u578b\u4e2d\u83b7\u5f97\u6548\u7528\u7684\u53ef\u80fd\u9014\u5f84\u7684\u793a\u4f8b\u3002\u6211\u4eec\u5bf9\u8fc7\u53bb\u7684\u5404\u79cd1\u4e2a\u6708\u7a97\u53e3\u8fdb\u884c\u4e86\u8f83\u957f\u65f6\u95f4\u7684\u9884\u6d4b\uff0c\u9884\u6d4b\u4e86\u5404\u53bf\u5c06\u9700\u8981\u591a\u5c11\u547c\u5438\u673a\u548c ICU \u5e8a\u4f4d\u7b49\u533b\u7597\u8d44\u6e90\uff0c\u5e76\u5bf9\u6211\u4eec\u7684\u6a21\u578b\u5728\u5176\u4ed6\u56fd\u5bb6\u7684\u6548\u679c\u8fdb\u884c\u4e86\u8bc4\u4f30\u3002<\/span><\/p>\n


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    \u8ba1\u7b97\u590d\u5236\u6570\u5b57\u7684 matlab \u4ee3\u7801<\/strong><\/span>\u53ca\u5176<\/strong><\/p>\n

    \u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75ab\u60c5\u7206\u53d1\u4e2d\u7684\u5e94\u7528<\/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

    A matlab code to compute reproduction numbers with applications to the Covid-19 outbreak<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13752<\/span><\/h2>\n

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

    Paulo R. Zingano,Janaina P. Zingano,Alessandra M. Silva,Carolina P. Zingano<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We discuss the generation of various reproduction ratios or numbers that are very useful to monitor an ongoing epidemic like Covid-19 and examine the effects of intervention measures. A detailed SEIR algorithm is described for their computation, with applications given to the current Covid-19 outbreaks in a number of countries (Argentina, Brazil, France, Italy, Mexico, Spain, UK and USA). The corresponding matlab script, complete and ready to use, is provided for free downloading.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8ba8\u8bba\u4e86\u5404\u79cd\u7e41\u6b96\u7387\u6216\u6570\u5b57\u7684\u4ea7\u751f\uff0c\u8fd9\u4e9b\u6570\u5b57\u5bf9\u4e8e\u76d1\u6d4b\u50cf\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u8fd9\u6837\u7684\u6301\u7eed\u6d41\u884c\u75c5\u975e\u5e38\u6709\u7528\uff0c\u5e76\u4e14\u68c0\u9a8c\u4e86\u5e72\u9884\u63aa\u65bd\u7684\u6548\u679c\u3002\u4e00\u4e2a\u8be6\u7ec6\u7684 SEIR \u7b97\u6cd5\u63cf\u8ff0\u4e86\u4ed6\u4eec\u7684\u8ba1\u7b97\uff0c\u5e76\u5e94\u7528\u4e8e\u5f53\u524d\u5728\u4e00\u4e9b\u56fd\u5bb6\u7206\u53d1\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75ab\u60c5(\u963f\u6839\u5ef7\uff0c\u5df4\u897f\uff0c\u6cd5\u56fd\uff0c\u610f\u5927\u5229\uff0c\u58a8\u897f\u54e5\uff0c\u897f\u73ed\u7259\uff0c\u82f1\u56fd\u548c\u7f8e\u56fd)\u3002\u76f8\u5e94\u7684 matlab \u811a\u672c\uff0c\u5b8c\u6574\u548c\u51c6\u5907\u4f7f\u7528\uff0c\u63d0\u4f9b\u514d\u8d39\u4e0b\u8f7d\u3002<\/span><\/p>\n


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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u884c\u4e3a\u60ef\u6027\u5bf9<\/strong><\/span><\/p>\n

    \u7ebd\u7ea6\u57ce\u5e02\u4ea4\u901a\u91cd\u65b0\u5f00\u653e\u7b56\u7565\u7684\u5f71\u54cd<\/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

    Impact of COVID-19 Behavioral Inertia on Reopening Strategies for New York City Transit<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13368<\/span><\/h2>\n

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

    Ding Wang,Brian Yueshuai He,Jingqin Gao,Joseph Y. J. Chow,Kaan Ozbay,Shri Iyer<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u5df2\u7ecf\u5f71\u54cd\u4e86\u65c5\u6e38\u884c\u4e3a\u548c\u4ea4\u901a\u7cfb\u7edf\u7684\u8fd0\u4f5c\uff0c\u5404\u4e2a\u57ce\u5e02\u6b63\u5728\u52aa\u529b\u89e3\u51b3\u4ec0\u4e48\u6837\u7684\u653f\u7b56\u624d\u80fd\u6709\u6548\u5730\u5b9e\u73b0\u7531\u793e\u4f1a\u8ddd\u79bb\u5851\u9020\u7684\u5206\u9636\u6bb5\u91cd\u65b0\u5f00\u653e\u3002\u5148\u524d\u5df2\u7ecf\u4e3a\u524d covid \u6761\u4ef6\u5f00\u53d1\u548c\u6821\u51c6\u4e86\u4e00\u4e2a\u57fa\u7ebf\u6a21\u578b\uff0c\u5373 MATSim-NYC\u3002\u901a\u8fc7\u91cd\u65b0\u6821\u51c6\u4eba\u53e3\u8bae\u7a0b\u4ee5\u5305\u62ec\u5728\u5bb6\u5de5\u4f5c\u548c\u91cd\u65b0\u4f30\u8ba1 MATSim-NYC \u7684\u6a21\u5f0f\u9009\u62e9\u6a21\u578b\u4ee5\u9002\u5e94\u89c2\u6d4b\u5230\u7684\u4ea4\u901a\u548c\u8fd0\u8f93\u5ba2\u6d41\u6570\u636e\uff0c\u6821\u51c6\u4e86\u4e00\u4e2a\u65b0\u7684 COVID \u6a21\u578b\uff0c\u8be5\u6a21\u578b\u4ee3\u8868\u4e86\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4\u7684\u65c5\u884c\u884c\u4e3a\u3002\u5047\u8bbe\u5728\u91cd\u65b0\u5f00\u653e\u8fc7\u7a0b\u4e2d\u884c\u4e3a\u7684\u6539\u53d8\u8868\u73b0\u51fa\u60ef\u6027\uff0c\u6211\u4eec\u5206\u6790\u4e86\u7531\u4e8e\u7ebd\u7ea6\u5dde\u653f\u5e9c\u6307\u5bfc\u7684\u5206\u9636\u6bb5\u91cd\u65b0\u5f00\u653e\u8ba1\u5212\u5bfc\u81f4\u7684\u6c7d\u8f66\u4ea4\u901a\u91cf\u7684\u589e\u52a0\u3002\u5206\u6790\u4e86\u56db\u4e2a\u91cd\u65b0\u5f00\u653e\u9636\u6bb5\u548c\u4e24\u4e2a\u91cd\u65b0\u5f00\u653e\u60c5\u666f(\u6709\u548c\u6ca1\u6709\u8fc7\u5883\u80fd\u529b\u9650\u5236)\u3002100% \u8fd0\u8f93\u80fd\u529b\u7684\u7b2c4\u9636\u6bb5\u91cd\u65b0\u5f00\u653e\u53ef\u80fd\u53ea\u80fd\u770b\u5230 covid \u524d\u4e58\u5ba2\u4eba\u6570\u591a\u8fbe73% \uff0c\u6c7d\u8f66\u51fa\u884c\u6b21\u6570\u589e\u52a0\u591a\u8fbe\u5927\u6d41\u884c\u524d\u6c34\u5e73\u7684142% \u3002\u5c06\u8fd0\u8f93\u80fd\u529b\u9650\u5236\u572850% \u5c06\u8fdb\u4e00\u6b65\u964d\u4f4e\u8fd0\u8f93\u4e58\u5ba2\u6570\u91cf\uff0c\u4ece73% \u964d\u81f364% \uff0c\u540c\u65f6\u5c06\u6c7d\u8f66\u65c5\u884c\u589e\u81f3\u5927\u6d41\u884c\u524d\u6c34\u5e73\u7684143% \u3002\u867d\u7136\u589e\u957f\u770b\u8d77\u6765\u5f88\u5c0f\uff0c\u4f46\u662f\u7531\u4e8e\u7b2c\u4e00\u4ea4\u901a\u5835\u585e\u7684\u589e\u957f\uff0c\u5bf9\u6d88\u8d39\u8005\u5269\u4f59\u7684\u5f71\u54cd\u4e0d\u6210\u6bd4\u4f8b\u7684\u5927\u3002\u8bb8\u591a\u65c5\u884c\u4e5f\u88ab\u8f6c\u79fb\u5230\u5176\u4ed6\u6a21\u5f0f\uff0c\u6bd4\u5982\u5fae\u578b\u673a\u52a8\u8f66\u3002\u7814\u7a76\u7ed3\u679c\u610f\u5473\u7740\uff0c\u5728\u91cd\u65b0\u5f00\u653e\u671f\u95f4\u5b9e\u884c\u8fd0\u8f93\u80fd\u529b\u9650\u5236\u653f\u7b56\u7684\u540c\u65f6\uff0c\u9700\u8981(1)\u652f\u6301\u5fae\u578b\u673a\u52a8\u6a21\u5f0f\uff0c\u7279\u522b\u662f\u5728\u975e\u66fc\u54c8\u987f\u884c\u653f\u533a\uff0c(2)\u4ee5\u51cf\u5c11\u66fc\u54c8\u987f\u4ea4\u901a\u4e3a\u91cd\u70b9\u7684\u7f13\u89e3\u4ea4\u901a\u62e5\u5835\u653f\u7b56\uff0c\u4f8b\u5982\u57fa\u4e8e\u8b66\u6212\u7ebf\u7684\u5b9a\u4ef7\u3002<\/span><\/p>\n


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    \u6c11\u65cf\u5bbd\u5bb9\u80fd\u6291\u5236<\/strong><\/span><\/p>\n

    \u81ea\u6211\u5f3a\u5316\u7684\u5b66\u6821\u9694\u79bb\u5417? <\/strong><\/span><\/p>\n

    \u4e00\u4e2a\u57fa\u4e8e Agent \u7684\u7406\u8bba\u6a21\u578b<\/strong><\/span><\/p>\n

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

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

    Can ethnic tolerance curb self-reinforcing school segregation? A theoretical Agent Based Model<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13531<\/span><\/h2>\n

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

    Lucas Sage,Andreas Flache<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Thirdly, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers mixed to segregated schools. However, we also identify a sweet spot in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when the preference for nearby schools weighs heavily in parents’ utility function and the residential map is only moderately segregated. Further experiments are presented that unravel the underlying mechanisms.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8c22\u6797\u548c\u8428\u79d1\u8fbe\u7a81\u51fa\u5730\u63d0\u51fa\u4e86\u8ba1\u7b97\u6a21\u578b\uff0c\u8868\u660e\u5f3a\u70c8\u7684\u79cd\u65cf\u5c45\u4f4f\u9694\u79bb\u53ef\u80fd\u662f\u4e00\u79cd\u81ea\u6211\u5f3a\u5316\u7684\u52a8\u6001\u7684\u610f\u5916\u7ed3\u679c\uff0c\u8fd9\u79cd\u52a8\u6001\u7531\u5177\u6709\u76f8\u5f53\u5bbd\u5bb9\u7684\u79cd\u65cf\u504f\u597d\u7684\u4e2a\u4eba\u7684\u9009\u62e9\u9a71\u52a8\u3002\u5f88\u5c11\u6709\u4eba\u8bd5\u56fe\u628a\u8fd9\u79cd\u89c2\u70b9\u5e94\u7528\u5230\u5b66\u6821\u9009\u62e9\u4e0a\uff0c\u8fd9\u662f\u79cd\u65cf\u9694\u79bb\u53d1\u751f\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u9886\u57df\u3002\u672c\u6587\u91c7\u7528\u7c7b\u4f3c\u4e8e\u5c45\u4f4f\u9694\u79bb\u7406\u8bba\u7684\u57fa\u4e8e\u4e3b\u4f53\u7684\u7406\u8bba\u6a21\u578b\uff0c\u63a2\u8ba8\u4e86\u5bb6\u957f\u95f4\u7684\u65cf\u88d4\u5bbd\u5bb9\u5bf9\u5b66\u6821\u9694\u79bb\u6c34\u5e73\u7684\u5f71\u54cd\u3002\u66f4\u5177\u4f53\u5730\u8bf4\uff0c\u6211\u4eec\u60f3\u77e5\u9053\uff0c\u5982\u679c\u66f4\u591a\u7684\u5bb6\u957f\u6301\u6709\u5bbd\u5bb9\u7684\u79cd\u65cf\u504f\u597d\uff0c\u5b66\u6821\u9694\u79bb\u662f\u5426\u53ef\u4ee5\u51cf\u5c11\uff0c\u4ee5\u53ca\u5728\u4f55\u79cd\u6761\u4ef6\u4e0b\u53ef\u4ee5\u51cf\u5c11\u3002\u6211\u4eec\u5728\u4e09\u4e2a\u65b9\u9762\u8d85\u8d8a\u4e86\u65e9\u671f\u7684\u5b66\u6821\u9694\u79bb\u6a21\u5f0f\u3002\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528\u968f\u673a\u6548\u7528\u79bb\u6563\u9009\u62e9\u65b9\u6cd5\u5bf9\u4e2a\u4f53\u7684\u5b66\u6821\u9009\u62e9\u8fdb\u884c\u5efa\u6a21\u3002\u5176\u6b21\uff0c\u6211\u4eec\u7cfb\u7edf\u5730\u6539\u53d8\u4e86\u5b66\u6821\u9009\u62e9\u5c45\u4f4f\u73af\u5883\u4e2d\u7684\u79cd\u65cf\u9694\u79bb\u6a21\u5f0f\uff0c\u6bd4\u8f83\u4e86\u4e0e\u7236\u6bcd\u5bb9\u5fcd\u7a0b\u5ea6\u65e0\u5173\u7684\u5c45\u4f4f\u5730\u56fe\u548c\u53cd\u6620\u4ed6\u4eec\u79cd\u65cf\u504f\u597d\u7684\u5c45\u4f4f\u5730\u56fe\u3002\u7b2c\u4e09\uff0c\u5f15\u5165\u540c\u4e00\u7fa4\u4f53\u4eb2\u672c\u95f4\u8010\u53d7\u6c34\u5e73\u7684\u5f02\u8d28\u6027\u3002\u6211\u4eec\u7684\u6a21\u62df\u5b9e\u9a8c\u8868\u660e\uff0c\u79cd\u65cf\u5b66\u6821\u9694\u79bb\u53ef\u80fd\u662f\u4e00\u79cd\u975e\u5e38\u5f3a\u70c8\u7684\u73b0\u8c61\uff0c\u751a\u81f3\u5728\u5927\u7ea6\u4e00\u534a\u7684\u4eba\u53e3\u559c\u6b22\u6df7\u5408\u5b66\u6821\u800c\u4e0d\u559c\u6b22\u9694\u79bb\u5b66\u6821\u7684\u65f6\u5019\u4e5f\u4f1a\u53d1\u751f\u3002\u7136\u800c\uff0c\u6211\u4eec\u4e5f\u5728\u53c2\u6570\u7a7a\u95f4\u4e2d\u786e\u5b9a\u4e86\u4e00\u4e2a\u6700\u4f73\u70b9\uff0c\u5728\u8fd9\u4e2a\u70b9\u4e0a\uff0c\u66f4\u5927\u6bd4\u4f8b\u7684\u5bbd\u5bb9\u7236\u6bcd\u4ea7\u751f\u4e86\u6700\u5927\u7684\u5dee\u5f02\u3002\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5bf9\u9644\u8fd1\u5b66\u6821\u7684\u504f\u597d\u5728\u5bb6\u957f\u7684\u6548\u7528\u51fd\u6570\u4e2d\u5360\u5f88\u5927\u6bd4\u91cd\uff0c\u800c\u4f4f\u5b85\u5730\u56fe\u53ea\u662f\u9002\u5ea6\u9694\u79bb\u3002\u8fdb\u4e00\u6b65\u7684\u5b9e\u9a8c\u63ed\u793a\u4e86\u6f5c\u5728\u7684\u673a\u5236\u3002<\/span><\/p>\n


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    \u4ece\u5f62\u5f0f\u5230\u4fe1\u606f: <\/strong><\/span><\/p>\n

    \u5206\u6790\u4e0d\u540c\u7a7a\u95f4\u6587\u5316\u4e2d\u7684\u5efa\u7b51\u73af\u5883<\/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

    From form to information: Analysing built environments in different spatial cultures<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13897<\/span><\/h2>\n

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

    Vinicius M. Netto,Edgardo Brigatti,Caio Cacholas<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Cities are different around the world, but does this fact bare any relation to culture? The idea that urban form embodies idiosyncrasies related to cultural identities captures the imagination of many in urban studies, but it is an assumption yet to be carefully examined. Approaching spatial configurations in the built environment as a proxy of urban culture, this paper searches for differences potentially consistent with specific regional cultures or cultures of planning in urban development. It does so focusing on the elementary components shaping cities: buildings and how they are aggregated in cellular complexes of built form. Exploring Shannon’s work, we introduce an entropy measure to analyse the probability distribution of cellular arrangements in built form systems. We apply it to downtown areas of 45 cities from different regions of the world as a similarity measure to compare and cluster cities potentially consistent with specific spatial cultures. Findings suggest a classification scheme that sheds further light on what we call the “cultural hypothesis”: the possibility that different cultures and regions find different ways of ordering space.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e16\u754c\u5404\u5730\u7684\u57ce\u5e02\u662f\u4e0d\u540c\u7684\uff0c\u4f46\u662f\u8fd9\u4e2a\u4e8b\u5b9e\u4e0e\u6587\u5316\u6709\u4ec0\u4e48\u8054\u7cfb\u5417\uff1f\u57ce\u5e02\u5f62\u6001\u4f53\u73b0\u4e86\u4e0e\u6587\u5316\u8ba4\u540c\u76f8\u5173\u7684\u7279\u8d28\u8fd9\u4e00\u89c2\u70b9\u5728\u57ce\u5e02\u7814\u7a76\u4e2d\u6fc0\u53d1\u4e86\u8bb8\u591a\u4eba\u7684\u60f3\u8c61\u529b\uff0c\u4f46\u8fd9\u53ea\u662f\u4e00\u4e2a\u6709\u5f85\u4ed4\u7ec6\u7814\u7a76\u7684\u5047\u8bbe\u3002\u4ee5\u57ce\u5e02\u6587\u5316\u4e3a\u4ee3\u8868\uff0c\u63a2\u8ba8\u57ce\u5e02\u5efa\u6210\u73af\u5883\u4e2d\u7684\u7a7a\u95f4\u7ed3\u6784\uff0c\u5bfb\u627e\u4e0e\u57ce\u5e02\u53d1\u5c55\u4e2d\u7279\u5b9a\u5730\u57df\u6587\u5316\u6216\u89c4\u5212\u6587\u5316\u6f5c\u5728\u7684\u4e00\u81f4\u6027\u5dee\u5f02\u3002\u5b83\u4e13\u6ce8\u4e8e\u5851\u9020\u57ce\u5e02\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206: \u5efa\u7b51\u4ee5\u53ca\u5b83\u4eec\u662f\u5982\u4f55\u805a\u96c6\u5728\u5efa\u7b51\u5f62\u5f0f\u7684\u7ec6\u80de\u590d\u5408\u4f53\u4e2d\u7684\u3002\u63a2\u7d22 Shannon \u7684\u5de5\u4f5c\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u71b5\u6d4b\u5ea6\u6765\u5206\u6790\u6784\u5efa\u5f62\u5f0f\u7cfb\u7edf\u4e2d\u7ec6\u80de\u6392\u5217\u7684\u6982\u7387\u5206\u5e03\u3002\u6211\u4eec\u5c06\u5176\u5e94\u7528\u4e8e\u4e16\u754c\u4e0d\u540c\u5730\u533a\u768445\u4e2a\u57ce\u5e02\u7684\u5e02\u4e2d\u5fc3\u533a\u57df\uff0c\u4f5c\u4e3a\u4e00\u79cd\u76f8\u4f3c\u6027\u5ea6\u91cf\uff0c\u6765\u6bd4\u8f83\u548c\u805a\u96c6\u4e0e\u7279\u5b9a\u7a7a\u95f4\u6587\u5316\u6f5c\u5728\u4e00\u81f4\u7684\u57ce\u5e02\u3002\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u4e00\u4e2a\u5206\u7c7b\u65b9\u6848\u8fdb\u4e00\u6b65\u9610\u660e\u4e86\u6211\u4eec\u6240\u8bf4\u7684\u201c\u6587\u5316\u5047\u8bf4\u201d : \u4e0d\u540c\u7684\u6587\u5316\u548c\u5730\u533a\u627e\u5230\u4e0d\u540c\u7684\u7a7a\u95f4\u6392\u5e8f\u65b9\u5f0f\u7684\u53ef\u80fd\u6027\u3002<\/span><\/p>\n


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

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

    \n
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    <\/p>\n

    \u75c5\u6bd2\u5728\u5efa\u6210\u73af\u5883\u4e2d\u4f20\u64ad\u7684\u8be6\u7ec6\u6a21\u62df<\/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

    Detailed Simulation of Viral Propagation In The Built Environment<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13792<\/span><\/h2>\n

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

    Rainald L\u00f6hner,Harbir Antil,Sergio Idelsohn,Eugenio O\u00f1ate<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>A summary is given of the mechanical characteristics of virus contaminants and the transmission via droplets and aerosols. The ordinary and partial differential equations describing the physics of these processes with high fidelity are presented, as well as appropriate numerical schemes to solve them. Several examples taken from recent evaluations of the built environment are shown, as well as the optimal placement of sensors.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7efc\u8ff0\u4e86\u75c5\u6bd2\u6c61\u67d3\u7269\u7684\u529b\u5b66\u7279\u6027\u53ca\u5176\u901a\u8fc7\u6db2\u6ef4\u548c\u6c14\u6eb6\u80f6\u4f20\u64ad\u7684\u673a\u7406\u3002\u7ed9\u51fa\u4e86\u63cf\u8ff0\u8fd9\u4e9b\u8fc7\u7a0b\u7269\u7406\u8fc7\u7a0b\u7684\u9ad8\u4fdd\u771f\u5e38\u5fae\u5206\u65b9\u7a0b\u548c\u504f\u5fae\u5206\u65b9\u7a0b\uff0c\u5e76\u7ed9\u51fa\u4e86\u76f8\u5e94\u7684\u6570\u503c\u89e3\u6cd5\u3002\u4ece\u6700\u8fd1\u7684\u5efa\u7b51\u73af\u5883\u8bc4\u4ef7\u7684\u51e0\u4e2a\u4f8b\u5b50\u663e\u793a\uff0c\u4ee5\u53ca\u4f20\u611f\u5668\u7684\u6700\u4f73\u4f4d\u7f6e\u3002<\/span><\/p>\n


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    \u5229\u7528\u91cf\u5b50\u5149\u63a2\u6d4b\u548c\u8ddf\u8e2a\u7ec6\u83cc<\/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

    Detecting and tracking bacteria with quantum light<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13250<\/span><\/h2>\n

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

    Gaetana Spedalieri,Lolita Piersimoni,Omar Laurino,Samuel L. Braunstein,Stefano Pirandola<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The field of quantum sensing aims at improving the detection and estimation of classical parameters that are encoded in physical systems by resorting to quantum sources of light and quantum detection strategies. The same approach can be used to improve the current classical measurements that are performed on biological systems. Here we consider the scenario of two bacteria (E. coli and Salmonella) growing in a Luria Bertani broth and monitored by classical spectrophotometers. Their concentration can be related to the optical transmissivity via the Beer-Lambert-Bouguer’s law and their growth curves can be described by means of Gompertz functions. Starting from experimental data points, we extrapolate the growth curves of the two bacteria and we study the theoretical performance that would be achieved with a quantum setup. In particular, we discuss how the bacterial growth can in principle be tracked by irradiating the samples with orders of magnitude fewer photons, identifying the clear superiority of quantum light in the early stages of growth. We then show the superiority and the limits of quantum resources in two basic tasks: (i) the early detection of bacterial growth and (ii) the early discrimination between two bacteria species.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u91cf\u5b50\u4f20\u611f\u9886\u57df\u7684\u76ee\u6807\u662f\u901a\u8fc7\u91cf\u5b50\u5149\u6e90\u548c\u91cf\u5b50\u68c0\u6d4b\u7b56\u7565\u6765\u6539\u8fdb\u5bf9\u7269\u7406\u7cfb\u7edf\u4e2d\u7f16\u7801\u7684\u7ecf\u5178\u53c2\u6570\u7684\u68c0\u6d4b\u548c\u4f30\u8ba1\u3002\u540c\u6837\u7684\u65b9\u6cd5\u4e5f\u53ef\u4ee5\u7528\u6765\u6539\u8fdb\u76ee\u524d\u5728\u751f\u7269\u7cfb\u7edf\u4e0a\u8fdb\u884c\u7684\u7ecf\u5178\u6d4b\u91cf\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8003\u8651\u8fd9\u6837\u4e00\u79cd\u60c5\u51b5: \u4e24\u79cd\u7ec6\u83cc(\u5927\u80a0\u6746\u83cc\u548c\u6c99\u95e8\u6c0f\u83cc)\u751f\u957f\u5728 Luria Bertani \u8089\u6c64\u4e2d\uff0c\u5e76\u901a\u8fc7\u7ecf\u5178\u7684\u5206\u5149\u5149\u5ea6\u8ba1\u8fdb\u884c\u76d1\u6d4b\u3002\u5b83\u4eec\u7684\u6d53\u5ea6\u53ef\u4ee5\u901a\u8fc7 Beer-Lambert-Bouguer \u5b9a\u5f8b\u4e0e\u5149\u900f\u8fc7\u7387\u6709\u5173\uff0c\u5b83\u4eec\u7684\u751f\u957f\u66f2\u7ebf\u53ef\u4ee5\u7528 Gompertz \u51fd\u6570\u6765\u63cf\u8ff0\u3002\u4ece\u5b9e\u9a8c\u6570\u636e\u70b9\u51fa\u53d1\uff0c\u6211\u4eec\u5916\u63a8\u4e86\u8fd9\u4e24\u79cd\u7ec6\u83cc\u7684\u751f\u957f\u66f2\u7ebf\uff0c\u5e76\u7814\u7a76\u4e86\u91cf\u5b50\u88c5\u7f6e\u6240\u80fd\u8fbe\u5230\u7684\u7406\u8bba\u6027\u80fd\u3002\u7279\u522b\u662f\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u539f\u5219\u4e0a\u5982\u4f55\u901a\u8fc7\u7528\u66f4\u5c11\u7684\u5149\u5b50\u7167\u5c04\u6837\u54c1\u6765\u8ddf\u8e2a\u7ec6\u83cc\u7684\u751f\u957f\uff0c\u786e\u5b9a\u4e86\u5728\u751f\u957f\u7684\u65e9\u671f\u9636\u6bb5\u91cf\u5b50\u5149\u7684\u660e\u663e\u4f18\u52bf\u3002\u6570\u91cf\u7ea7\u3002\u7136\u540e\u6211\u4eec\u5728\u4e24\u4e2a\u57fa\u672c\u4efb\u52a1\u4e2d\u5c55\u793a\u4e86\u91cf\u5b50\u8d44\u6e90\u7684\u4f18\u8d8a\u6027\u548c\u5c40\u9650\u6027: (i)\u7ec6\u83cc\u751f\u957f\u7684\u65e9\u671f\u68c0\u6d4b\u548c(ii)\u4e24\u79cd\u7ec6\u83cc\u7684\u65e9\u671f\u9274\u522b\u3002<\/span><\/p>\n


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    \u6c34\u5728\u4e3b\u5ba2\u4e92\u52a8\u4e2d\u7684\u4f5c\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

    The role of water in host-guest interaction<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.13274<\/span><\/h2>\n

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

    Valerio Rizzi,Luigi Bonati,Narjes Ansari,Michele Parrinello<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>One of the main applications of atomistic computer simulations is the calculation of ligand binding energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system’s degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u539f\u5b50\u8ba1\u7b97\u673a\u6a21\u62df\u7684\u4e3b\u8981\u5e94\u7528\u4e4b\u4e00\u662f\u914d\u4f53\u7ed3\u5408\u80fd\u7684\u8ba1\u7b97\u3002\u8fd9\u4e9b\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u53d6\u51b3\u4e8e\u529b\u573a\u7684\u8d28\u91cf\u548c\u6784\u578b\u91c7\u6837\u7684\u5f7b\u5e95\u6027\u3002\u7531\u4e8e\u52a8\u80fd\u74f6\u9888\u7684\u9891\u7e41\u51fa\u73b0\uff0c\u91c7\u6837\u662f\u73b0\u4ee3\u6a21\u62df\u4e2d\u7684\u4e00\u4e2a\u969c\u788d\u3002\u901a\u5e38\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u56f0\u96be\u662f\u901a\u8fc7\u589e\u5f3a\u62bd\u6837\u6280\u672f\u6765\u907f\u514d\u7684\u3002\u901a\u5e38\uff0c\u8fd9\u4e9b\u6280\u672f\u4f9d\u8d56\u4e8e\u5f15\u5165\u9002\u5f53\u7684\u96c6\u5408\u53d8\u91cf\uff0c\u8fd9\u4e9b\u96c6\u5408\u53d8\u91cf\u65e8\u5728\u6355\u83b7\u7cfb\u7edf\u7684\u81ea\u7531\u5ea6\u3002\u5728\u914d\u4f53\u7ed3\u5408\u4e2d\uff0c\u6c34\u4e00\u76f4\u88ab\u8ba4\u4e3a\u8d77\u7740\u5173\u952e\u4f5c\u7528\uff0c\u4f46\u662f\u5b83\u7684\u590d\u6742\u884c\u4e3a\u5df2\u7ecf\u88ab\u8bc1\u660e\u5f88\u96be\u88ab\u5b8c\u5168\u6355\u83b7\u3002\u672c\u6587\u5c06\u673a\u5668\u5b66\u4e60\u548c\u7269\u7406\u76f4\u89c9\u76f8\u7ed3\u5408\uff0c\u6784\u9020\u4e86\u4e00\u4e2a\u975e\u5c40\u90e8\u7684\u3001\u9ad8\u6548\u7684\u6c34\u63cf\u8ff0\u96c6\u5408\u53d8\u91cf\u3002\u6211\u4eec\u4f7f\u7528\u5b83\u6765\u7814\u7a76\u4e00\u7ec4\u6765\u81ea\u4e8e SAMPL5\u6311\u6218\u7684\u4e3b\u5ba2\u7cfb\u7edf\u3002\u83b7\u5f97\u4e86\u9ad8\u7cbe\u5ea6\u7684\u7ed3\u5408\u80fd\uff0c\u4e0e\u5b9e\u9a8c\u7b26\u5408\u826f\u597d\u3002\u7136\u540e\u8be6\u7ec6\u5206\u6790\u4e86\u6c34\u5728\u80f6\u5408\u8fc7\u7a0b\u4e2d\u7684\u4f5c\u7528\u3002<\/span><\/p>\n


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    \u7eb3\u7c73\u5b54\u6355\u83b7\u68d2\u72b6\u5206\u5b50: <\/strong><\/span><\/p>\n

    \u5b9a\u4e49\u201c\u53d6\u5411\u6355\u83b7\u534a\u5f84<\/strong><\/span><\/p>\n

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


    <\/p>\n

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

    Capture of rod-like molecules by a nanopore: defining an “orientational capture radius”<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13338<\/span><\/h2>\n

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

    Le Qiao,Gary W. Slater<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Both the translational diffusion coefficient<\/span>D and the electrophoretic mobility <\/span>\u03bc of a short rod-like molecule (such as dsDNA) that is being pulled towards a nanopore by an electric field should depend on its orientation. Since a charged rod-like molecule tends to orient in the presence of an inhomogeneous electric field, <\/span>D and <\/span>\u03bc will change as the molecule approaches the nanopore, and this will impact the capture process. We present a simplified study of this problem using theoretical arguments and Langevin Dynamics simulations. In particular, we introduce a new textit{orientational capture radius} which we compare to the capture radius for the equivalent point-like particle, and we discuss the different physical regimes of orientation during capture and the impact of initial orientations on the capture time.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e24\u8005\u7684\u5e73\u52a8\u6269\u6563\u7cfb\u6570<\/span>D \u8fd8\u6709\u7535\u6cf3\u6d82\u5c42<\/span>\u03bc \u4e00\u4e2a\u77ed\u68d2\u72b6\u5206\u5b50(\u6bd4\u5982 dsDNA)\u88ab\u7535\u573a\u62c9\u5411\u7eb3\u7c73\u5b54\uff0c\u8fd9\u5e94\u8be5\u53d6\u51b3\u4e8e\u5b83\u7684\u65b9\u5411\u3002\u7531\u4e8e\u5e26\u7535\u7684\u68d2\u72b6\u5206\u5b50\u5728\u975e\u5747\u5300\u7535\u573a\u4e2d\u503e\u5411\u4e8e\u5b9a\u5411,<\/span>D \u53ca<\/span>\u03bc \u4f1a\u968f\u7740\u5206\u5b50\u63a5\u8fd1\u7eb3\u7c73\u5b54\u800c\u6539\u53d8\uff0c\u8fd9\u4f1a\u5f71\u54cd\u6355\u83b7\u8fc7\u7a0b\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u7b80\u5316\u7684\u7814\u7a76\uff0c\u8fd9\u4e2a\u95ee\u9898\u4f7f\u7528\u7406\u8bba\u8bba\u8bc1\u548c\u6717\u4e4b\u4e07\u52a8\u529b\u5b66\u6a21\u62df\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u4e00\u79cd\u65b0\u7684\u65b9\u5411\u6355\u83b7\u534a\u5f84\uff0c\u5e76\u4e0e\u7b49\u6548\u70b9\u72b6\u7c92\u5b50\u7684\u6355\u83b7\u534a\u5f84\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u8ba8\u8bba\u4e86\u6355\u83b7\u8fc7\u7a0b\u4e2d\u53d6\u5411\u7684\u4e0d\u540c\u7269\u7406\u72b6\u6001\u4ee5\u53ca\u521d\u59cb\u53d6\u5411\u5bf9\u6355\u83b7\u65f6\u95f4\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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    \u6d3b\u6027\u805a\u5408\u7269\u548c\u7ec6\u4e1d\u7684\u7269\u7406\u5b66<\/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 physics of active polymers and filaments<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.13509<\/span><\/h2>\n

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

    Roland G. Winkler,Gerhard Gompper<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Active matter agents consume internal energy or extract energy from the environment for locomotion and force generation. Already rather generic models, such as ensembles of active Brownian particles, exhibit phenomena, which are absent at equilibrium, in particular motility-induced phase separation and collective motion. Further intriguing nonequilibrium effects emerge in assemblies of bound active agents as in linear polymers or filaments. The interplay of activity and conformational degrees of freedom gives rise to novel structural and dynamical features of individual polymers as well as in interacting ensembles. Such out-of-equilibrium polymers are an integral part of living matter, ranging from biological cells with filaments propelled by motor proteins in the cytoskeleton, and RNA\/DNA in the transcription process, to long swarming bacteria and worms such as Proteus mirabilis and Caenorhabditis elegans, respectively. Even artificial active polymers have been synthesized. The emergent properties of active polymers or filaments depend on the coupling of the active process to their conformational degrees of freedom, aspects which are addressed in this article. The theoretical models for tangentially and isotropically self-propelled or active-bath driven polymers are presented, both in presence and absence of hydrodynamic interactions. The consequences for their conformational and dynamical properties are examined, emphasizing the strong influence of the coupling between activity and hydrodynamic interactions. Particular features of emerging phenomena, induced by steric and hydrodynamic interactions, are highlighted. Various important, yet theoretically unexplored, aspects are featured and future challenges are discussed.<\/span><\/p>\n

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    \u82f1\u56fd\u65b0\u51a0\u80ba\u708e\u7981\u95ed: \u5bf9\u7a7a\u6c14\u6c61\u67d3\u6709\u4ec0\u4e48\u5f71\u54cd | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7<\/strong><\/a>
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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901230\u7bc7<\/strong><\/a>
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    \u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c\u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc |\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901225\u7bc7<\/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

    \n

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

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


    <\/p>\n

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

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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":"

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