关闭和重新开放: 学校在欧洲新型冠状病毒肺炎传播中的作用 | 网络科学论文速递33篇

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核心速递

• 关闭和重新开放: 学校在欧洲新型冠状病毒肺炎传播中的作用；

• 基于品牌传播的在线社会网络影响节点识别；

• 图结构主题神经网络；

• 区域限制搜索上的随机漫步；

• 告密者伤痕累累: 关于告密的难度；

• 联合国天基信息平台: 选择性地划分相互关联的数据和实体关系；

• 分布位移下时态图上图形神经网络的增量式训练；

• 基于命中概率的有向图和马尔可夫链上的度量；

• 关于 COVID-19大流行的语义注释推文的知识库；

• 关于 RNNs 的 Lyapunov 指数: 用动态系统工具理解信息传播；

• 多层动态异网络中的爆炸同步；

• 用于极端神经形态智能的超低功耗 FDSOI 神经电路；

• 通过精确定时的脉冲控制振荡系综中的集体同步；

• 最大多尺度熵与神经网络正则化；

• 利用神经网络发现 SU (N)费米子隐藏特征的启发式机制；

• 脉冲星计时阵列各向异性引力波背景搜索的 Fisher 公式；

• 基于随机 SIR 模型的锁定 / 测试缓解策略研究及其与韩国、德国和纽约数据的比较；

• 不平衡状态下两党党派偏见的测量；

• 树的线性排列中边长之和的变化；

• 巴西巴伊亚州和圣卡塔琳娜的 SARS-CoV-2新型冠状病毒肺炎流行的最优控制问题；

• 飓风撤离过程中的道路网络可达性评估——以佛罗里达州的飓风 Irma 为例；

• 估计美国邮政编码之间的大驱动时间矩阵: 差分抽样方法；

• 城市尺度律中的空间相互作用；

• 20世纪90年代新型冠状病毒肺炎，美国大城市热点地区的移动和访问的不同模式；

• 有争议的信息在 Reddit 上传播得越来越快，越来越远；

• 加强企业网络中知识转移的干预情景；

• 基于志愿者困境博弈的紧急疏散救助行为模型研究；

• 新型冠状病毒肺炎流行病区室模型的结构可识别性和可观测性；

• 基于负采样高阶跳图的时变图表示学习；

• 相称社区结构的推论统计学；

• 稳健网络连通性的逾渗阈值；

• 预测印度新型冠状病毒肺炎大流行的每日和累积病例数；

• 拓扑相关的收益可以帮助人们摆脱囚徒困境；

关闭和重新开放: 

学校在欧洲新型冠状

病毒肺炎传播中的作用

原文标题：

Shut and re-open: the role of schools in the spread of COVID-19 in Europe

地址：

http://arxiv.org/abs/2006.14158

作者:

Helena B. Stage,Joseph Shingleton,Sanmitra Ghosh,Francesca Scarabel,Lorenzo Pellis,Thomas Finnie

Abstract：We investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden, and German states as case studies. By comparing the growth rates in daily hospitalisations or confirmed cases under different interventions, we provide evidence that the effect of school closure is visible as a reduction in the growth rate approximately 9 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. A large-scale reopening of schools while controlling or suppressing the epidemic appears feasible in countries such as Denmark or Norway, where community transmission is generally low. However, school reopening can contribute to significant increases in the growth rate in countries like Germany, where community transmission is relatively high. Our findings underscore the need for a cautious evaluation of reopening strategies that ensure low classroom occupancy and a solid infrastructure to quickly identify and isolate new infections.

摘要：我们将丹麦、挪威、瑞典和德国各州作为个案研究，调查学校关闭和随后的重新开放对新型冠状病毒肺炎传播的影响。通过比较不同干预措施下每日住院或确诊病例的增长率，我们提供的证据表明，关闭学校的影响是显而易见的，因为实施措施大约9天后增长率下降。有限的学校出勤，例如高年级学生参加考试或部分返回年轻群体，似乎没有显着影响社区传播。在社区传播率普遍较低的丹麦或挪威等国家，在控制或抑制这一流行病的同时大规模重新开放学校似乎是可行的。然而，在像德国这样的社区传播率相对较高的国家，学校重新开学可以促进增长率的显著提高。我们的研究结果强调，需要谨慎评估重新开放的策略，以确保低课堂占用率和坚实的基础设施，以快速识别和隔离新的感染。

基于品牌传播的

在线社会网络影响节点识别

原文标题：

Identify Influential Nodes in Online Social Network for Brand Communication

地址：

http://arxiv.org/abs/2006.14104

作者:

Yuxin Mao,Lujie Zhou,Naixue Xiong

Abstract：Online social networks have become incredibly popular in recent years, which prompts an increasing number of companies to promote their brands and products through social media. This paper presents an approach for identifying influential nodes in online social network for brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related contents. We quantitatively measure the individual value of the nodes in the community from both the network structure and brand engagement aspects. Then an algorithm for identifying the influential nodes from the virtual brand community is proposed. The algorithm evaluates the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. We extract and construct a virtual brand community for a specific brand from a real-life online social network as the dataset and empirically evaluate the proposed approach. The experimental results have shown that the proposed approach was able to identify influential nodes in online social network. We can get an identification result with higher ratio of verified users and user coverage by using the approach.

摘要：近年来，在线社交网络变得非常流行，促使越来越多的公司通过社交媒体推广自己的品牌和产品。本文提出了一种基于品牌传播的在线社会网络中有影响力节点的识别方法。我们首先从品牌相关内容中抽取用户及其关系，建立一个加权网络模型。我们从网络结构和品牌参与两个方面定量衡量社区中节点的个人价值。然后提出了一种从虚拟品牌社区中识别有影响力节点的算法。该算法通过节点的单个值以及周围节点的单个值来评估节点的重要性。我们从现实生活中的在线社会网络中抽取和构建一个特定品牌的虚拟品牌社区作为数据集，并对所提出的方法进行实证评价。实验结果表明，该方法能够识别在线社会网络中具有影响力的节点。该方法可以得到更高的用户识别率和用户覆盖率。

图结构主题神经网络

原文标题：

Graph Structural-topic Neural Network

地址：

http://arxiv.org/abs/2006.14278

作者:

Qingqing Long,Yilun Jin,Guojie Song,Yi Li,Wei Lin

Abstract：Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on graph structures within the neighborhood, especially higher-order structural patterns. However, such local structural patterns are shown to be indicative of node properties in numerous fields. In addition, it is not just single patterns, but the distribution over all these patterns matter, because networks are complex and the neighborhood of each node consists of a mixture of various nodes and structural patterns. Correspondingly, in this paper, we propose Graph Structural-topic Neural Network, abbreviated GraphSTONE, a GCN model that utilizes topic models of graphs, such that the structural topics capture indicative graph structures broadly from a probabilistic aspect rather than merely a few structures. Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently. In addition, we design multi-view GCNs to unify node features and structural topic features and utilize structural topics to guide the aggregation. We evaluate our model through both quantitative and qualitative experiments, where our model exhibits promising performance, high efficiency, and clear interpretability.

摘要：图卷积网络通过有效地收集节点的局部特征取得了巨大的成功。然而，通常 GCNs 更多地关注节点特征，而较少关注邻域内的图结构，特别是高阶结构模式。然而，这种局部结构模式在许多领域中表明节点属性。此外，它不仅仅是单一的模式，而是所有这些模式的分布都很重要，因为网络是复杂的，每个节点的邻居是由各种节点和结构模式的混合物组成的。相应地，本文提出了图结构主题神经网络，简称 GraphSTONE，一种利用图的主题模型的 GCN 模型，使结构主题从概率的角度广泛地捕捉指示性图形结构，而不仅仅是少数几种结构。具体来说，我们使用匿名漫步和图锚 LDA (Graph Anchor LDA) ，一种优先选择重要结构模式的 LDA 变量，在图上建立主题模型，以减少复杂性，有效地生成结构主题。此外，我们设计了多视图 GCNs 来统一节点特征和结构主题特征，并利用结构主题来指导聚合。我们通过定量和定性实验来评估我们的模型，在这里我们的模型表现出良好的性能，高效率和清晰的可解释性。

区域限制搜索上的随机漫步

原文标题：

A random walk on Area Restricted Search

地址：

http://arxiv.org/abs/2006.14318

作者:

Simone Santini

Abstract：These notes from a graduate class at the Unuversidad Autonoma de Madrid analyze a search behavior known as Area Resticted Search (ARS), widespread in the animal kingdom, and optimal when the resources that one is after are "patchy". In the first section we study the importance of the behavior in animal and its dependence on the dopamine as a indicator of reward. In the second section we put together a genetic algorithm to determine the optimality of ARS and its characteristics. Finally, we relate ARS to a type of random walks known as "Levy Walks", in which the probability of jumping at a distance d from the current location follows a power law distribution.

摘要：这些笔记来自 Unuversidad Autonoma de Madrid 的一个研究生班，它们分析了一种被称为“区域限制搜索”(ARS)的搜索行为，这种搜索在动物王国中很普遍，当人们所追求的资源“不完整”时，这种搜索行为是最理想的。在第一部分，我们研究了动物行为的重要性，以及动物对多巴胺作为奖赏指标的依赖性。在第二部分，我们提出了一个遗传算法，以确定的最优性农业研究系统及其特点。最后，我们将 ARS 与一种称为“ Levy Walks”的随机游动联系起来，在这种随机游动中，从当前位置的距离 d 处跳跃的概率服从幂律分布。

告密者伤痕累累: 关于告密的难度

原文标题：

Snitches Get Stitches: On The Difficulty of Whistleblowing

地址：

http://arxiv.org/abs/2006.14407

作者:

Mansoor Ahmed-Rengers,Ross Anderson,Darija Halatova,Ilia Shumailov

Abstract：One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you're caught. In this short paper, we report on efforts to enable whistleblowers to leak sensitive documents to journalists more safely. Following a survey of cases where whistleblowers were discovered due to operational or technological issues, we propose a game-theoretic model capturing the power dynamics involved in whistleblowing. We find that the whistleblower is often at the mercy of motivations and abilities of others. We identify specific areas where technology may be used to mitigate the whistleblower's risk. However we warn against technical solutionism: the main constraints are often institutional.

摘要：对于人类来说，最关键的安全协议问题之一就是当你背叛了一个信任，或许是为了某种更高的目的，如果你被抓住了，整个世界都会反过来对付你。在这篇简短的文章中，我们报道了如何让举报者更安全地将敏感文件泄露给记者。在调查了由于操作或技术问题而发现举报者的案例之后，我们提出了一个捕捉举报所涉及的权力动态的博弈论模型。我们发现告密者经常受到其他人的动机和能力的支配。我们确定了可以利用技术来减轻告密者风险的具体领域。然而，我们对技术解决方案主义提出了警告: 主要的制约因素往往是制度性的。

联合国天基信息平台: 

选择性地划分

相互关联的数据和实体关系

原文标题：

SPIDER: Selective Plotting of Interconnected Data and Entity Relations

地址：

http://arxiv.org/abs/2006.14416

作者:

Pranav Addepalli,Eric Wu,Douglas Bossart,Christina Lin,Allistar Smith

Abstract：Intelligence analysts have long struggled with an abundance of data that must be investigated on a daily basis. In the U.S. Army, this activity involves reconciling information from various sources, a process that has been automated to a certain extent, but which remains highly manual. To promote automation, a semantic analysis prototype was designed to aid in the intelligence analysis process. This tool, called Selective Plotting of Interconnected Data and Entity Relations (SPIDER), extracts entities and their relationships from text in order to streamline investigations. SPIDER is a web application that can be remotely-accessed via a web browser, and has three major components: (1) a Java API that reads documents, extracts entities and relationships using Stanford CoreNLP, (2) a Neo4j graph database that stores entities, relationships, and properties; (3) a JavaScript-based SigmaJS visualization tool for displaying the graph on the browser. SPIDER can scale document analysis to thousands of files for quick visualization, making the intelligence analysis process more efficient, and allowing military leadership quicker insights into a vast array of potentially-hidden knowledge.

摘要：长期以来，情报分析人员一直在与大量必须每天调查的数据作斗争。在美国陆军，这种活动包括调节来自不同来源的信息，这个过程在一定程度上已经自动化，但仍然是高度手动的。为了促进自动化，设计了一个语义分析原型来辅助智能分析过程。这个工具，称为选择性绘制相互关联的数据和实体关系(SPIDER) ，从文本中提取实体及其关系，以简化调查。Spider 是一个可以通过 web 浏览器远程访问的 web 应用程序，它有三个主要组成部分: (1)一个 Java API，它可以使用 Stanford CoreNLP 读取文档、提取实体和关系; (2)一个 Neo4j 图形数据库，它存储实体、关系和属性; (3)一个基于 javascript 的 sigs 可视化工具，用于在浏览器上显示图形。天基信息平台可以将文件分析扩大到数千个文件，以便快速可视化，使情报分析过程更加有效，并使军事领导人能够更快地洞察大量潜在隐藏的知识。

分布位移下时态图上

图形神经网络的增量式训练

原文标题：

Incremental Training of Graph Neural Networks on Temporal Graphs under Distribution Shift

地址：

http://arxiv.org/abs/2006.14422

作者:

Lukas Galke,Iacopo Vagliano,Ansgar Scherp

Abstract：Current graph neural networks (GNNs) are promising, especially when the entire graph is known for training. However, it is not yet clear how to efficiently train GNNs on temporal graphs, where new vertices, edges, and even classes appear over time. We face two challenges: First, shifts in the label distribution (including the appearance of new labels), which require adapting the model. Second, the growth of the graph, which makes it, at some point, infeasible to train over all vertices and edges. We address these issues by applying a sliding window technique, i.e., we incrementally train GNNs on limited window sizes and analyze their performance. For our experiments, we have compiled three new temporal graph datasets based on scientific publications and evaluate isotropic and anisotropic GNN architectures. Our results show that both GNN types provide good results even for a window size of just 1 time step. With window sizes of 3 to 4 time steps, GNNs achieve at least 95% accuracy compared to using the entire timeline of the graph. With window sizes of 6 or 8, at least 99% accuracy could be retained. These discoveries have direct consequences for training GNNs over temporal graphs. We provide the code (https://github.com/Incremental-GNNs) and the newly compiled datasets (https://zenodo.org/record/3764770) for reproducibility and reuse.

摘要：当前的图形神经网络(gnn)是很有前途的，特别是当整个图形已知的训练。然而，目前还不清楚如何有效地在时态图上训练 gnn，在时态图上，新的顶点、边甚至类都会随着时间的推移出现。我们面临两个挑战: 第一，标签分布的变化(包括新标签的出现) ，这需要调整模型。第二，图的增长，这使得在某一点上，对所有顶点和边进行训练是不可行的。我们通过应用滑动窗口技术来解决这些问题，也就是说，我们在有限的窗口大小上逐步训练 gnn 并分析它们的性能。在实验中，我们基于科学文献编制了三个新的时间图数据集，并对各向同性和各向异性的 GNN 结构进行了评估。我们的结果显示，这两种 GNN 类型提供了良好的结果，即使窗口大小只有1个时间步长。窗口大小为3至4个时间步长，与使用图的整个时间线相比，gnn 的准确率至少达到95% 。窗口大小为6或8时，至少可保持99% 的准确度。这些发现对在时态图上训练 gnn 有直接的影响。我们提供代码。

基于命中概率的有向图

和马尔可夫链上的度量

原文标题：

A metric on directed graphs and Markov chains based on hitting probabilities

地址：

http://arxiv.org/abs/2006.14482

作者:

Zachary M. Boyd,Nicolas Fraiman,Jeremy L. Marzuola,Peter J. Mucha,Braxton Osting,Jonathan Weare

Abstract：The shortest-path, commute time, and diffusion distances on undirected graphs have been widely employed in applications such as dimensionality reduction, link prediction, and trip planning. Increasingly, there is interest in using asymmetric structure of data derived from Markov chains and directed graphs, but few metrics are specifically adapted to this task. We introduce a metric on the state space of any ergodic, finite-state, time-homogeneous Markov chain and, in particular, on any Markov chain derived from a directed graph. Our construction is based on hitting probabilities, with nearness in the metric space related to the transfer of random walkers from one node to another at stationarity. Notably, our metric is insensitive to shortest and average path distances, thus giving new information compared to existing metrics. We use possible degeneracies in the metric to develop an interesting structural theory of directed graphs and explore a related quotienting procedure. Our metric can be computed in O(n3) time, where n is the number of states, and in examples we scale up to n=10,000 nodes and ≈38M edges on a desktop computer. In several examples, we explore the nature of the metric, compare it to alternative methods, and demonstrate its utility for weak recovery of community structure in dense graphs, visualization, structure recovering, dynamics exploration, and multiscale cluster detection.

摘要：无向图上的最短路径、通勤时间和扩散距离被广泛应用于降维、链路预测和行程规划等应用中。从马尔可夫链和有向图导出的数据的非对称结构越来越引起人们的兴趣，但很少有度量标准专门适用于这项任务。我们在任意遍历、有限状态、时齐马氏链的状态空间上，特别是在由有向图导出的任意马氏链上，引入了一个度量。我们的结构是基于命中概率，在度量空间中的贴近度与随机行走者以平稳的方式从一个节点转移到另一个节点有关。值得注意的是，我们的度量对最短路径距离和平均路径距离不敏感，因此与现有度量相比提供了新的信息。我们利用度量中可能的退化来发展一个有趣的有向图的结构理论，并探索相关的商程序。我们的度量可以用O(n3) 是状态的数量，在例子中我们放大到n=10,000  节点和≈38M 桌面电脑的边缘。在几个实例中，我们探讨了度量的本质，并将其与其他方法进行了比较，证明了它在稠密图形中的社区结构弱恢复、可视化、结构恢复、动态探索和多尺度聚类检测方面的实用性。

关于COVID-19大流行的

语义注释推文的知识库

原文标题：

TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

地址：

http://arxiv.org/abs/2006.14492

作者:

Dimitar Dimitrov,Erdal Baran,Pavlos Fafalios,Ran Yu,Xiaofei Zhu,Matthäus Zloch,Stefan Dietze

Abstract：Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning, NLP and information retrieval methods. With respect to the recent outbreak of COVID-19, online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection or entity recognition. However, obtaining, archiving and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning the period Oct'19-Apr'20. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis, use cases and usage of the corpus.

摘要：公开的社会媒体档案促进了社会科学的研究，并为培训和测试广泛的机器学习、 NLP 和信息检索方法提供了语料库。关于最近爆发的新型冠状病毒肺炎疫情，Twitter 上的在线讨论反映了公众对大流行病本身的意见和看法，以及缓解措施和它们的社会影响。理解这样的话语，它的演变和与现实世界事件或(错误的)信息的相互依赖可以培养有价值的洞察力。另一方面，这样的语料库对于处理情感分析、事件检测或实体识别等任务的计算方法具有重要的促进作用。然而，获取、归档和对大量 tweets 进行语义注释的代价是昂贵的。在本文中，我们描述了 TweetsCOV19，这是一个公开的知识库，目前有超过800万条推讯，时间跨度从10月19日到4月20日。关于 tweets 的元数据以及提取的实体、 hashtags、用户提及、 sentiments 和 url 都使用已建立的 rdf / s 词汇表公开，为一系列知识发现任务提供了前所未有的知识库。在描述数据集及其提取和注释过程的基础上，我们给出了一个初步的分析、用例和语料库的使用。

关于 RNNs 的 Lyapunov 指数: 

用动态系统工具理解信息传播

原文标题：

On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools

地址：

http://arxiv.org/abs/2006.14123

作者:

Ryan Vogt,Maximilian Puelma Touzel,Eli Shlizerman,Guillaume Lajoie

Abstract：Recurrent neural networks (RNNs) have been successfully applied to a variety of problems involving sequential data, but their optimization is sensitive to parameter initialization, architecture, and optimizer hyperparameters. Considering RNNs as dynamical systems, a natural way to capture stability, i.e., the growth and decay over long iterates, are the Lyapunov Exponents (LEs), which form the Lyapunov spectrum. The LEs have a bearing on stability of RNN training dynamics because forward propagation of information is related to the backward propagation of error gradients. LEs measure the asymptotic rates of expansion and contraction of nonlinear system trajectories, and generalize stability analysis to the time-varying attractors structuring the non-autonomous dynamics of data-driven RNNs. As a tool to understand and exploit stability of training dynamics, the Lyapunov spectrum fills an existing gap between prescriptive mathematical approaches of limited scope and computationally-expensive empirical approaches. To leverage this tool, we implement an efficient way to compute LEs for RNNs during training, discuss the aspects specific to standard RNN architectures driven by typical sequential datasets, and show that the Lyapunov spectrum can serve as a robust readout of training stability across hyperparameters. With this exposition-oriented contribution, we hope to draw attention to this understudied, but theoretically grounded tool for understanding training stability in RNNs.

摘要：递归神经网络(rnn)已经成功地应用于各种涉及序列数据的问题，但其优化对参数初始化、结构和优化器超参数都很敏感。将 RNNs 看作动态系统，一种捕获稳定性的自然方法，即长迭代过程中的增长和衰减，是构成 Lyapunov 谱的 Lyapunov 指数(LEs)。由于信息的正向传播与误差梯度的反向传播有关，故训练样本的稳定性直接关系到训练样本的稳定性。Les 测量非线性轨迹扩展和收缩的渐近速率，并将稳定性分析推广到构造数据驱动 RNNs 的非自治动态的时变吸引子。作为理解和利用训练动力学稳定性的工具，李雅普诺夫谱填补了有限范围的规定性数学方法和计算代价昂贵的经验方法之间现有的空白。为了充分利用这一工具，我们实现了一种有效的方法来计算在训练过程中 RNNs 的 LEs，讨论了标准的 RNN 结构在典型的序列数据集驱动下的特定方面，并表明 Lyapunov 谱可以作为一个跨超参数的训练稳定性的鲁棒读出。有了这个以论文为导向的贡献，我们希望引起人们对这个未充分研究，但是理论上扎实的工具的注意，以理解 RNNs 的训练稳定性。

多层动态异网络中的爆炸同步

原文标题：

Explosive Synchronization in Multilayer Dynamically Dissimilar Networks

地址：

http://arxiv.org/abs/2006.14161

作者:

Sarika Jalan,Ajay Deep Kachhvah,Hawoong Jeong

Abstract：The phenomenon of explosive synchronization, which originates from hypersensitivity to small perturbation caused by some form of frustration prevailed in various physical and biological systems, has been shown to lead events of cascading failure of the power grid to chronic pain or epileptic seizure in the brain. Furthermore, networks provide a powerful model to understand and predict the properties of a diverse range of real-world complex systems. Recently, a multilayer network has been realized as a better suited framework for the representation of complex systems having multiple types of interactions among the same set of constituents. This article shows that by tuning the properties of one layer (network) of a multilayer network, one can regulate the dynamical behavior of another layer (network). By taking an example of a multiplex network comprising two different types of networked Kuramoto oscillators representing two different layers, this article attempts to provide a glimpse of opportunities and emerging phenomena multiplexing can induce which is otherwise not possible for a network in isolation. Here we consider explosive synchronization to demonstrate the potential of multilayer networks framework. To the end, we discuss several possible extensions of the model considered here by incorporating real-world properties.

摘要：爆发性同步现象，这种现象起源于过敏，由于某种形式的挫折感在各种物理和生物系统中普遍存在而引起的微小扰动，已经被证明导致电网连锁故障事件，导致慢性疼痛或大脑中的癫痫发作。此外，网络提供了一个强大的模型来理解和预测各种各样的现实世界复杂系统的性质。近年来，多层网络作为一种更适合的框架被实现，用于表示同一组成部分之间具有多种类型相互作用的复杂系统。本文表明，通过调整多层网络的一层(网络)的性质，可以调节另一层(网络)的动态行为。本文以两种不同类型的 Kuramoto 振荡器组成的多路复用网络为例，试图提供一个机会和新出现的现象多路复用可以诱导，否则不可能在一个网络中孤立。这里我们考虑爆炸性同步来证明多层网络框架的潜力。最后，我们讨论了这里考虑的模型的几个可能的扩展，它们结合了真实世界的属性。

用于极端神经形态智能

的超低功耗 FDSOI 神经电路

原文标题：

Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence

地址：

http://arxiv.org/abs/2006.14270

作者:

Arianna Rubino,Can Livanelioglu,Ning Qiao,Melika Payvand,Giacomo Indiveri

Abstract：Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit simulation results and demonstrate the circuit's ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.

摘要：近年来，人们越来越关注用于边缘计算应用的人工智能电路和系统的发展。内存计算混合信号神经形态结构由于具有实时模拟脉冲神经网络的能力，为边缘计算感觉处理应用提供了有前途的超低功耗解决方案。这种方法所提供的细粒度并行性使得这些神经回路能够通过将其动力学适应于感知信号而有效地处理感觉数据，而不必诉诸于冯 · 诺依曼结构的时间复用计算模式。为了进一步降低功耗，我们提出了一套混合信号模拟 / 数字电路，它利用了先进的全耗尽 SOI 集成过程的特点。具体来说，我们探索先进的 FDSOI 技术的选项，以解决模拟设计问题和优化设计的突触积分器和自适应神经元电路相应。我们给出了电路仿真结果，并证明了该电路能够以紧凑的设计产生生物学上似是而非的神经动力学，为在神经形态处理器中实现大规模脉冲神经网络进行了优化。

通过精确定时的脉冲

控制振荡系综中的集体同步

原文标题：

Controlling collective synchrony in oscillatory ensembles by precisely timed pulses

地址：

http://arxiv.org/abs/2006.14355

作者:

Michael Rosenblum

Abstract：We present an efficient technique for control of synchrony in a globally coupled ensemble by pulsatile action. We assume that we can observe the collective oscillation and can stimulate all elements of the ensemble simultaneously. We pay special attention to the minimization of intervention into the system. The key idea is to stimulate only at the most sensitive phase. To find this phase we implement an adaptive feedback control. Estimating the instantaneous phase of the collective mode on the fly, we achieve efficient suppression using a few pulses per oscillatory cycle. We discuss the possible relevance of the results for neuroscience, namely for the development of advanced algorithms for deep brain stimulation, a medical technique used to treat Parkinson's disease.

摘要：我们提出了一个有效的技术控制同步在一个全球耦合系综脉动行动。我们假设我们可以观察到集体振荡，并且可以同时激发系综的所有元素。我们特别注意尽量减少对系统的干预。关键是只在最敏感的阶段刺激。为了找到这个阶段，我们实现了一个自适应反馈控制。通过动态估算集体模式的瞬时频率，我们可以在每个振荡周期中使用几个脉冲来实现有效的抑制。我们讨论的可能相关的结果，神经科学，即先进的算法的发展脑深部刺激，一种医疗技术用于治疗帕金森氏症。

最大多尺度熵与神经网络正则化

原文标题：

Maximum Multiscale Entropy and Neural Network Regularization

地址：

http://arxiv.org/abs/2006.14614

作者:

Amir R. Asadi,Emmanuel Abbe

Abstract：A well-known result across information theory, machine learning, and statistical physics shows that the maximum entropy distribution under a mean constraint has an exponential form called the Gibbs-Boltzmann distribution. This is used for instance in density estimation or to achieve excess risk bounds derived from single-scale entropy regularizers (Xu-Raginsky '17). This paper investigates a generalization of these results to a multiscale setting. We present different ways of generalizing the maximum entropy result by incorporating the notion of scale. For different entropies and arbitrary scale transformations, it is shown that the distribution maximizing a multiscale entropy is characterized by a procedure which has an analogy to the renormalization group procedure in statistical physics. For the case of decimation transformation, it is further shown that this distribution is Gaussian whenever the optimal single-scale distribution is Gaussian. This is then applied to neural networks, and it is shown that in a teacher-student scenario, the multiscale Gibbs posterior can achieve a smaller excess risk than the single-scale Gibbs posterior.

摘要：信息论、机器学习和统计物理学的一个著名结果表明，在均值约束下的最大熵分布呈指数形式，称为吉布斯-波尔兹曼分布。例如，这用于密度估计或实现从单尺度熵正则化者(Xu-Raginsky’17)推导出的超额风险界限。本文研究了这些结果在多尺度环境下的推广。我们提出了不同的方法通过纳入规模的概念来推广最大熵的结果。对于不同的熵和任意尺度的变换，我们证明了最大化多尺度熵的分布拥有属性是一个与统计物理学中的重整化群过程类似的过程。在抽取变换的情况下，进一步证明了当最优单尺度分布是高斯分布时，抽取变换的抽样分布是高斯分布。然后将其应用于神经网络，结果表明，在师生情景下，多尺度吉布斯后验相对于单尺度吉布斯后验可以实现更小的过度风险。

利用神经网络发现 

SU (N)费米子隐藏特征的启发式机制

原文标题：

Heuristic machinery to uncover hidden features of SU(N) Fermions with neural networks

地址：

http://arxiv.org/abs/2006.14142

作者:

Entong Zhao,Jeongwon Lee,Chengdong He,Zejian Ren,Elnur Hajiyev,Junwei Liu,Gyu-Boong Jo

Abstract：The power of machine learning (ML) provides the possibility of analyzing experimental measurements with an unprecedented sensitivity. However, it still remains challenging to uncover hidden features directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by combining the power of ML and the "trial and error" in a supervised way. We use our machinery to reveal hidden thermodynamic features in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator, and discover their connection to spin multiplicity. Although such spin symmetry should manifest itself in a many-body wavefuction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of ∼94% for detection of the spin multiplicity, we investigate the dependency of accuracy on various hidden features with filtered measurements. Guided by our machinery, we verify how the NN extracts a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(N) Fermi liquids, and to identify hidden features even for highly complex quantum matters with minimal prior understanding.

摘要：机器学习(ML)的力量提供了以前所未有的灵敏度分析实验测量的可能性。然而，要发现与物理观测直接相关的隐藏特征，并利用机器学习从普通实验数据中理解物理学，仍然是一个挑战。在这里，我们介绍了一种启发式机制结合的权力的机器学习和“试错”在监督的方式。我们利用我们的机制揭示了超冷费米子在 SU (中相互作用的密度分布中隐藏的热力学特征N自旋对称性，并发现它们与自旋多重性的联系。虽然这种自旋对称性本身应该表现在多体波前中，但是费米子的动量分布，这种最普通的测量方法，如何揭示自旋对称性的影响，却是令人费解的。使用一个完全训练有素的卷积神经网络~94%为了检测旋量多重性，我们调查了各种隐藏特征与过滤测量的精确度依赖性。在我们的机制指导下，我们验证了神经网络如何从单个图像的密度起伏中提取热力学压缩性。我们的机器学习框架显示了验证 SU (理论描述的潜力N）费米液体，并确定隐藏的特点，即使是高度复杂的量子问题的最低事先了解。

脉冲星计时阵列各向

异性引力波背景搜索的 Fisher 公式

原文标题：

Fisher formalism for anisotropic gravitational-wave background searches with pulsar timing arrays

地址：

http://arxiv.org/abs/2006.14570

作者:

Yacine Ali-Haïmoud,Tristan L. Smith,Chiara M. F. Mingarelli

Abstract：Pulsar timing arrays (PTAs) are currently the only experiments directly sensitive to gravitational waves with decade-long periods. Within the next five to ten years, PTAs are expected to detect the stochastic gravitational-wave background (SGWB) collectively sourced by inspiralling supermassive black hole binaries. It is expected that this background is mostly isotropic, and current searches focus on the monopole part of the SGWB. Looking ahead, anisotropies in the SGWB may provide a trove of additional information both on known and unknown astrophysical and cosmological sources. In this paper, we build a simple yet realistic Fisher formalism for anisotropic SGWB searches with PTAs. Our formalism is able to accommodate realistic properties of PTAs, and allows simple and accurate forecasts. We illustrate our approach with an idealized PTA consisting of identical, isotropically distributed pulsars. In a companion paper, we apply our formalism to current PTAs and show that it can be a powerful tool to guide and optimize real data analysis.

摘要：脉冲星定时阵列(PTAs)是目前唯一对引力波直接敏感的十年周期实验。在接下来的5到10年内，预计 PTAs 将探测到由吸入的超重黑洞双星共同获得的随机引力波背景(SGWB)。预计这种背景大多是各向同性的，目前的搜索主要集中在 SGWB 的单极子部分。展望未来，SGWB 的各向异性可能会提供关于已知和未知天体物理学和宇宙学来源的额外信息。在本文中，我们建立了一个用于各向异性 SGWB 搜索的简单而实际的 Fisher 形式。我们的形式主义能够适应 pta 的现实特性，并允许简单和准确的预测。我们用一个理想化的 PTA 来说明我们的方法，PTA 由相同的、等热点分布的脉冲星组成。在一篇配套文章中，我们将我们的形式主义应用于当前的 pta，并表明它可以成为指导和优化实际数据分析的有力工具。

基于随机 SIR 模型的锁定 / 测试

缓解策略研究及其

与韩国、德国和纽约数据的比较

原文标题：

Study of lockdown/testing mitigation strategies on stochastic SIR model and its comparison with South Korea, Germany and New York data

地址：

http://arxiv.org/abs/2006.14373

作者:

 Priyanka,Vicky Verma

Abstract：We are currently facing a highly critical case of a world-wide pandemic. The novel coronavirus (SARS-CoV-2, a.k.a. COVID-19) has proved to be extremely contagious and the original outbreak from Asia has now spread to all continents. This situation will fruitfully profit from the study in regards of the spread of the virus, assessing effective countermeasures to weight the impact of the adopted strategies. The standard Susceptible-Infectious-Recovered (SIR) model is a very successful and widely used mathematical model for predicting the spread of an epidemic. We adopt the SIR model on a random network and extend the model to include control strategies {\em lockdown} and {\em testing} -- two often employed mitigation strategies. The ability of these strategies in controlling the pandemic spread is investigated by varying the effectiveness with which they are implemented. The possibility of a second outbreak is evaluated in detail after the mitigation strategies are withdrawn. We notice that, in any case, a sudden interruption of such mitigation strategies will likely induce a resurgence of a second outbreak, whose peak will be correlated to the number of susceptible individuals. In fact, we find that a population will remain vulnerable to the infection until the herd immunity is achieved. We also test our model with real statistics and information on the epidemic spread in South Korea, Germany, and New York and find a remarkable agreement with the simulation data.

摘要：我们目前正面临全世界大流行的一个非常严重的病例。新型冠状病毒(SARS-CoV-2，又名新型冠状病毒肺炎)已被证明具有极强的传染性，最初从亚洲爆发的疫情现已蔓延到所有大陆。这种情况将有效地受益于关于病毒传播的研究，评估有效的对策，以衡量所采取的战略的影响。标准的易感-传染-恢复(SIR)模型是一个非常成功和广泛使用的数学模型来预测传染病的传播。我们在随机网络上采用 SIR 模型，并将模型扩展到包括控制策略{ em lockdown }和{ em testing }---- 两种常用的缓解策略。通过改变这些战略的实施效果来调查这些战略控制大流行传播的能力。在撤销减缓战略之后，将详细评估第二次爆发的可能性。我们注意到，在任何情况下，这种缓解策略的突然中断都可能导致第二次疫情的再次爆发，其高峰将与易感人群的数量相关。事实上，我们发现在群体免疫力达到之前，群体仍然容易受到感染。我们还用韩国、德国和纽约疫情传播的真实统计数据和信息对我们的模型进行了检验，发现与模拟数据有显著的一致性。

不平衡状态下两党党派偏见的测量

原文标题：

On measuring two-party partisan bias in unbalanced states

地址：

http://arxiv.org/abs/2006.14067

作者:

John F. Nagle,Alec Ramsay

Abstract：Assuming that partisan fairness and responsiveness are important aspects of redistricting, it is important to measure them. Many measures of partisan bias are satisfactory for states that are balanced with roughly equal proportions of voters for the two major parties. It has been less clear which metrics measure fairness robustly when the proportion of the vote is unbalanced by as little as 60% to 40%. We have addressed this by analyzing past election results for four states with Democratic preferences (CA, IL, MA, and MD), three states with Republican preferences (SC, TN, and TX) and comparing those to results for four nearly balanced states (CO, NC, OH, and PA). We used many past statewide elections in each state to build statistically precise seats for votes and rank for votes graphs to which many measures of partisan bias were applied. In addition to providing values of responsiveness, we find that five of the measures of bias provide mutually consistent values in all states, thereby providing a core of usable measures for unbalanced states. Although all five measures focus on different aspects of partisan bias, normalization of the values across the eleven states provides a suitable way to compare them, and we propose that their average provides a superior measure which we call composite bias. Regarding other measures, we find that the most seemingly plausible symmetry measure fails for unbalanced states. We also consider deviations from the proportionality ideal, but using it is difficult because the political geography of a state can entangle responsiveness with total partisan bias. We do not attempt to separate intentional partisan bias from the implicit bias that results from the interaction of the map drawing rules of a state and its political geography, on the grounds that redistricting should attempt to minimize total partisan bias whatever its provenance.

摘要：假设党派公平和反应能力是重新划分选区的重要方面，重要的是要衡量它们。许多衡量党派偏见的标准对于两个主要政党的选民比例大致相等的州来说是令人满意的。如果选票的比例不平衡，只有60% 到40% ，那么哪种衡量标准能够有力地衡量公平性就不那么明确了。我们通过分析民主党偏好的四个州(CA，IL，MA 和 MD)、共和党偏好的三个州(SC，TN 和 TX)的过去选举结果来解决这个问题，并将这些结果与四个接近平衡的州(CO，NC，OH 和 PA)的结果进行比较。我们利用过去在每个州举行的许多州级选举，为选票建立了统计学上精确的席位，并对选票图表进行排名，许多衡量党派偏见的指标都适用于这些图表。除了提供反应能力的价值，我们发现五种偏差测量方法在所有状态下提供了相互一致的价值，从而为不平衡状态提供了可用的核心测量方法。尽管所有的5项指标都集中在党派偏见的不同方面，但是十一个州价值观的正常化提供了一个合适的方法来比较它们，我们建议他们的平均值提供了一个更好的指标，我们称之为综合偏见。至于其他的测量方法，我们发现最看似合理的对称测量方法对不平衡状态是不适用的。我们也考虑偏离相称性的理想，但使用它是困难的，因为一个国家的政治地理可以纠缠与完全党派偏见的反应。我们并不试图将蓄意的党派偏见与一个国家的地图绘制规则及其政治地理的相互作用所产生的隐含偏见区分开来，理由是重新划分选区应试图尽量减少完全的党派偏见，无论其来源如何。

树的线性排列中边长之和的变化

原文标题：

The variation of the sum of edge lengths in linear arrangements of trees

地址：

http://arxiv.org/abs/2006.14069

作者:

Ramon Ferrer-i-Cancho,Carlos Gómez-Rodríguez,Juan Luis Esteban

Abstract：A fundamental problem in network science is the normalization of the topological or physical distance between vertices, that requires understanding the range of variation of the unnormalized distances. Here we investigate the limits of the variation of the physical distance in linear arrangements of the vertices of trees. In particular, we investigate various problems on the sum of edge lengths in trees of a fixed size: the minimum and the maximum value of the sum for specific trees, the minimum and the maximum in classes of trees (bistar trees and caterpillar trees) and finally the minimum and the maximum for any tree. We establish some foundations for research on optimality scores for spatial networks in one dimension.

摘要：网络科学中的一个基本问题是顶点之间的拓扑或物理距离的规范化，这需要理解非规范化距离的变化范围。这里我们研究树的顶点线性排列的物理距离变化的极限。特别地，我们研究了固定大小树的边长之和的各种问题: 特定树的边长之和的最小值和最大值，各类树(双星树和毛虫树)的最小值和最大值，最后是任何树的最小值和最大值。我们为研究一维空间网络的最优性得分奠定了一些基础。

巴西巴伊亚州和圣卡塔琳娜的 

SARS-CoV-2新型冠状病毒肺炎

流行的最优控制问题

原文标题：

Optimal Control Concerns Regarding the COVID-19 (SARS-CoV-2) Pandemic in Bahia and Santa Catarina, Brazil

地址：

http://arxiv.org/abs/2006.14108

作者:

Marcelo M. Morato,Igor M. L. Pataro,Marcus V. Americano da Costa,Julio E. Normey-Rico

Abstract：The COVID-19 pandemic is the profoundest health crisis of the 21rst century. The SARS-CoV-2 virus arrived in Brazil around March, 2020 and its social and economical backlashes are catastrophic. In this paper, it is investigated how Model Predictive Control (MPC) could be used to plan appropriate social distancing policies to mitigate the pandemic effects in Bahia and Santa Catarina, two states of different regions, culture, and population demography in Brazil. In addition, the parameters of Susceptible-Infected-Recovered-Deceased (SIRD) models for these two states are identified using an optimization procedure. The control input to the process is a social isolation guideline passed to the population. Two MPC strategies are designed: a) a centralized MPC, which coordinates a single control policy for both states; and b) a decentralized strategy, for which one optimization is solved for each state. Simulation results are shown to illustrate and compare both control strategies. The framework serves as guidelines to deals with such pandemic phenomena.

摘要：新型冠状病毒肺炎是21世纪最严重的健康危机。Sars-cov-2病毒于2020年3月左右抵达巴西，其社会和经济反弹是灾难性的。在这篇论文中，我们研究了如何利用模型预估计控制卫生组织来制定适当的社会疏远政策，以减轻巴伊亚和圣卡塔琳娜这两个不同地区、不同文化和不同人口组成的州的流行病影响。此外，还利用最优化方法对这两种状态下的易感-感染-康复-死亡(SIRD)模型的参数进行了辨识。该过程的控制输入是传递给总体的社会隔离指导方针。设计了两种 MPC 策略: 一种是集中式 MPC，协调两种状态的单一控制策略; 另一种是分散式 MPC，为每种状态解决一个优化问题。仿真结果验证了两种控制策略的有效性。该框架是处理这种大流行现象的指导方针。

飓风撤离过程中的道路网络可达性评估

——以佛罗里达州的飓风 Irma 为例

原文标题：

Estimating Road Network Accessibility during a Hurricane Evacuation: A Case Study of Hurricane Irma in Florida

地址：

http://arxiv.org/abs/2006.14137

作者:

Yi-Jie Zhu,Yujie Hu,Jennifer M. Collins

Abstract：Understanding the spatiotemporal road network accessibility during a hurricane evacuation, the level of ease of residents in an area in reaching evacuation destination sites through the road network, is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond), few have considered both dynamic evacuation demand and characteristics of a hurricane. This study proposes a methodological framework to achieve this goal. In an interval of every six hours, the method first estimates the evacuation demand in terms of number of vehicles per household in each county subdivision by considering the hurricane's wind radius and track. The closest facility analysis is then employed to model evacuees' route choices towards the predefined evacuation destinations. The potential crowdedness index (PCI), a metric capturing the level of crowdedness of each road segment, is then computed by coupling the estimated evacuation demand and route choices. Finally, the road accessibility of each sub-county is measured by calculating the reciprocal of the sum of PCI values of corresponding roads connecting evacuees from the sub-county to the designated destinations. The method is applied to the entire state of Florida during Hurricane Irma in September 2017. Results show that I-75 and I-95 northbound have a high level of congestion, and sub-counties along the northbound I-95 suffer from the worst road accessibility. In addition, this research performs a sensitivity analysis for examining the impacts of different choices of behavioral response curves on accessibility results.

摘要：了解飓风疏散期间的时空道路网络可达性，即通过道路网络到达疏散目的地地区的居民的容易程度，是应急管理的一个重要组成部分。虽然许多研究试图衡量道路的可达性(无论是在疏散范围内还是以外) ，但很少有研究考虑到动态疏散需求和飓风的特点。本研究提出了实现这一目标的方法框架。在每六个小时的时间间隔内，该方法首先考虑飓风的风向半径和路径，以每个县分区每户的车辆数量来估计疏散需求。最近设施分析，然后采用模型疏散人员的路线选择到预定义的疏散目的地。潜在拥挤度指数(PCI)是一个度量每个路段拥挤程度的指标，然后通过耦合估计的疏散需求和路线选择计算出来。最后，通过计算连接从该次级县到指定目的地的疏散人员的相应道路 PCI 值之和的倒数来衡量每个次级县的道路可达性。该方法在2017年9月飓风“厄玛”期间适用于整个佛罗里达州。结果显示，I-75和 I-95北行路段拥堵程度很高，I-95北行路段沿线各县的道路交通状况最差。此外，这项研究还采用了一个敏感度分析的方法来检验不同选择的行为反应曲线对可达性结果的影响。

估计美国邮政编码之间

的大驱动时间矩阵:

差分抽样方法

原文标题：

Estimating a Large Drive Time Matrix between Zip Codes in the United States: A Differential Sampling Approach

地址：

http://arxiv.org/abs/2006.14138

作者:

Yujie Hu,Changzhen Wang,Ruiyang Li,Fahui Wang

Abstract：Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.--a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.

摘要：估算不同位置之间的大规模驱动时间矩阵是一项实际但具有挑战性的任务。挑战包括可靠的道路网络(包括交通)数据的可用性、编程专业知识以及对高性能计算资源的访问。这项研究提出了一种估算美国邮政编码地区之间的全国驱动时间矩阵的方法---- 这是一个地理单元，许多国家数据集，如健康信息是在这里汇编和分发的。该方法(1)不依赖于在数据准备或访问高级计算资源方面的密集努力，(2)使用不同复杂度和计算时间的算法来估计不同行程长度的驱动时间，(3)兼顾了区间和区内驱动时间。核心设计根据行程长度采样不同强度的邮政编码对，并通过谷歌地图 API 计算驱动时间，然后使用谷歌时间调整和改进一些低计算成本的原始驱动时间估计。研究结果为研究人员提供了宝贵的资源。

城市尺度律中的空间相互作用

原文标题：

Spatial interactions in urban scaling laws

地址：

http://arxiv.org/abs/2006.14140

作者:

Eduardo G. Altmann

Abstract：Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and the spatial interactions are inferred from data simultaneously, allowing for rigorous (Bayesian) model comparison and overcoming the problem of defining the boundaries of urban regions. Results in five different datasets show that including spatial interactions typically leads to better models and a change in the exponent of the scaling law. Data and codes are provided in Ref. [1].

摘要：对城市尺度律的分析假定不同城市的观测值与邻近城市的存在无关。在这里，我们介绍生成模型和数据分析方法，克服这一局限性，明确建模个人之间的相互作用在不同地点的影响。描述尺度律和空间相互作用的参数同时从数据中推断出来，允许进行严格的(贝叶斯)模型比较，克服了界定城市区域边界的问题。五个不同数据集的结果表明，包括空间相互作用通常会导致更好的模型和标度律指数的变化。数据和代码提供参考文献。[1]。

20世纪90年代新型冠状病毒肺炎，

美国大城市热点地区的

移动和访问的不同模式

原文标题：

Disparate Patterns of Movements and Visits to Points of Interests Located in Urban Hotspots across U.S. Metropolitan Cities during COVID-19

地址：

http://arxiv.org/abs/2006.14157

作者:

Qingchun Li,Liam Bessell,Xin Xiao,Chao Fan,Xinyu Gao,Ali Mostafavi

Abstract：We examined the effect of social distancing on changes in visits to urban hotspot points of interest. Urban hotspots, such as central business districts, are gravity activity centers orchestrating movement and mobility patterns in cities. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped origin-destination networks from census block groups to points of interest (POIs) in sixteen cities in the United States. We adopted a coarse-grain approach to study movement patterns of visits to POIs among the hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in POI visits to hotspot areas. The sixteen cities are divided into two categories based on visits to POIs in hotspot areas. In one category, which includes the cities of, San Francisco, Seattle, and Chicago, we observe a considerable decrease in visits to POIs in hotspot areas, while in another category, including the cites of, Austin, Houston, and San Diego, the visits to hotspot areas did not greatly decrease during the social distancing period. In addition, while all the cities exhibited overall decreasing visits to POIs, one category maintained the proportion of visits to POIs in the hotspots. The proportion of visits to some POIs (e.g., Restaurant and Other Eating Places) remained stable during the social distancing period, while some POIs had an increased proportion of visits (e.g., Grocery Stores). The findings highlight that social distancing orders do yield disparate patterns of reduction in movements to hotspots POIs.

摘要：我们研究了社会距离对城市热点地区游客数量变化的影响。城市热点地区，如中央商务区，是重力活动中心，协调城市的运动和流动模式。在大流行的情况下，城市热点可能是潜在的超级传播地区，因为访问城市热点可以增加人口之间接触和传播疾病的风险。我们绘制了美国十六个城市从人口普查区组到感兴趣点(POIs)的起点-目的地网络。我们采用粗粒度方法研究2020年1月至5月在热点和非热点地区访问 POIs 的流动模式。此外，我们还进行了卡方检验，以确定在分析期间有显著通量变化的 poi。调查结果显示，各个城市的 POI 热点地区访问量下降的情况各不相同。这十六个城市根据热点地区的 POIs 访问量被分为两类。在一个类别中，包括旧金山、西雅图和芝加哥，我们观察到在热点地区访问 POIs 的大量减少，而在另一个类别中，包括奥斯汀、休斯顿和圣地亚哥，访问热点地区在社会疏远期间并没有大量减少。此外，虽然所有城市对 POIs 的访问总体呈下降趋势，但有一类访问热点地区的 POIs 的比例保持不变。在社交距离拉大期间，部分参观点(例如食肆及其他食肆)的访问比例保持稳定，而部分参观点(例如食品杂货店)的访问比例则有所增加。研究结果突出表明，社会疏远秩序确实会导致不同模式的 POIs 热点移动减少。

有争议的信息在 Reddit 上

传播得越来越快，越来越远

原文标题：

Controversial information spreads faster and further in Reddit

地址：

http://arxiv.org/abs/2006.13991

作者:

Jasser Jasser,Ivan Garibay,Steve Scheinert,Alexander V. Mantzaris

Abstract：Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, controversiality within social cascades is of high interest to us. It is known that controversiality is bound to happen within online discussions. The online social network platform Reddit has the feature to tag comments as controversial if the users have mixed opinions about that comment. The difference between this study and previous attempts at understanding controversiality on social networks is that we do not investigate topics that are known to be controversial. On the contrary, we examine typical cascades with comments that the readers deemed to be controversial concerning the matter discussed. This work asks whether controversially initiated information cascades have distinctive characteristics than those not controversial in Reddit. We used data collected from Reddit consisting of around 17 million posts and their corresponding comments related to cybersecurity issues to answer these emerging questions. From the comparative analyses conducted, controversial content travels faster and further from its origin. Understanding this phenomenon would shed light on how users or organization might use it to their help in controlling and spreading a specific beneficiary message.

摘要：在线用户在社交网络上讨论和交谈各种各样的话题。在 Facebook，Twitter，Reddit 等其他网站上，用户可以自由地分享信息。通过这些网络共享的丰富信息使它们成为研究人类信息传播行为各个方面的有吸引力的领域。在许多有趣的行为中，社会级联中的争议是我们非常感兴趣的。众所周知，在网络讨论中肯定会有争议。在线社交网络平台 Reddit 有一个功能，如果用户对评论有不同的意见，可以将评论标记为有争议的。这项研究与之前尝试理解社交网络上的争议的不同之处在于，我们不调查那些已知具有争议性的话题。相反，我们检查典型的瀑布与评论，读者认为是有争议的事项讨论。这项研究提出了这样一个问题: 与 Reddit 上那些没有争议的信息级联相比，这些有争议的信息级联是否具有独特的特征。我们使用从 Reddit 上收集的大约1700万篇帖子的数据，以及他们相应的与网络安全问题相关的评论来回答这些新出现的问题。从所进行的比较分析来看，有争议的内容传播得越来越快，而且离它的起源越来越远。了解这一现象将有助于了解用户或组织如何利用它来帮助控制和传播特定的受益人信息。

加强企业网络中知识转移的干预情景

原文标题：

Intervention scenarios to enhance knowledge transfer in a network of firm

地址：

https://arxiv.org/abs/2006.14249

作者:

Frank Schweitzer,Yan Zhang,Giona Casiraghi

Abstract：We investigate a multi-agent model of firms in an R\&D network. Each firm is characterized by its knowledge stock xi(t), which follows a non-linear dynamics. It can grow with the input from other firms, i.e., by knowledge transfer, and decays otherwise. Maintaining interactions is costly. Firms can leave the network if their expected knowledge growth is not realized, which may cause other firms to also leave the network. The paper discusses two bottom-up intervention scenarios to prevent, reduce, or delay cascades of firms leaving. The first one is based on the formalism of network controllability, in which driver nodes are identified and subsequently incentivized, by reducing their costs. The second one combines node interventions and network interventions. It proposes the controlled removal of a single firm and the random replacement of firms leaving. This allows to generate small cascades, which prevents the occurrence of large cascades. We find that both approaches successfully mitigate cascades and thus improve the resilience of the R\&D network.

摘要：研究了 r & d 网络中企业的多智能体模型，每个企业都有其知识存量xi(t),本文提出了一种基于非线性动力学的非线性动力学方法。它可以随着其他公司的投入而增长，例如，通过知识转移，否则就会衰退。维护交互是昂贵的。如果企业预期的知识增长不能实现，企业可以离开网络，这可能导致其他企业也离开网络。本文讨论了两种自下而上的干预情景，以防止、减少或延迟企业离开的级联效应。第一种是基于网络可控性的形式主义，即通过降低成本来识别驱动节点并随后激励它们。第二种是结合节点干预和网络干预。它建议有控制地取消一家公司，随机取代离开的公司。这允许生成小级联，从而防止出现大级联。我们发现这两种方法都成功地减少了级联，从而提高了研发网络的弹性。

基于志愿者困境博弈的

紧急疏散救助行为模型研究

原文标题：

Modeling Helping Behavior in Emergency Evacuations Using Volunteer's Dilemma Game

地址：

http://arxiv.org/abs/2006.14207

作者:

Jaeyoung Kwak,Michael H Lees,Wentong Cai,Marcus EH Ong

Abstract：People often help others who are in trouble, especially in emergency evacuation situations. For instance, during the 2005 London bombings, it was reported that evacuees helped injured persons to escape the place of danger. In terms of game theory, it can be understood that such helping behavior provides a collective good while it is a costly behavior because the volunteers spend extra time to assist the injured persons in case of emergency evacuations. In order to study the collective effects of helping behavior in emergency evacuations, we have performed numerical simulations of helping behavior among evacuees in a room evacuation scenario. Our simulation model is based on the volunteer's dilemma game reflecting volunteering cost. The game theoretic model is coupled with a social force model to understand the relationship between the spatial and social dynamics of evacuation scenarios. By systematically changing the cost parameter of helping behavior, we observed different patterns of collective helping behaviors and these collective patterns are summarized with a phase diagram.

摘要：人们经常帮助有困难的人，特别是在紧急撤离的情况下。例如，在2005年伦敦爆炸案期间，据报道，撤离人员帮助受伤者逃离危险地点。从博弈论的角度来看，可以理解的是，这种帮助行为提供了一种集体利益，而这是一种代价高昂的行为，因为在紧急撤离时，志愿者会花费额外的时间来帮助受伤的人。为了研究紧急疏散中帮助行为的集体效应，我们对一个房间疏散场景中被疏散者的帮助行为进行了数值模拟。我们的模拟模型是基于反映志愿者成本的志愿者困境博弈。该博弈论模型与社会力量模型相结合，以理解疏散场景的空间和社会动态之间的关系。通过系统地改变帮助行为的成本参数，我们观察到了不同的集体帮助行为模式，并用相图对这些集体模式进行了总结。

新型冠状病毒肺炎流行病

区室模型的结构可识别性和可观测性

原文标题：

Structural Identifiability and Observability of Compartmental Models of the COVID-19 Pandemic

地址：

http://arxiv.org/abs/2006.14295

作者:

Gemma Massonis,Julio R. Banga,Alejandro F. Villaverde

Abstract：The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights -- as well as the possibility of controlling the system -- may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.

摘要：最近的冠状病毒病(新型冠状病毒肺炎)的爆发极大地提高了公众对动态模型的效用的认识和欣赏。与此同时，矛盾模型预测的传播突出了它们的局限性。如果一个模型的某些参数和 / 或状态变量不能从输出测量中确定，那么它产生正确洞察力的能力——以及控制系统的可能性——可能会受到损害。流行病动力学通常使用分隔模型进行分析，这些模型的许多变体已被用于分析和预测新型冠状病毒肺炎流行病的演变。本文综述了文献中提出的各种模型，收集了36种模型结构，并评估了它们提供可靠信息的能力。我们用结构可识别性和可观测性的控制理论概念来解决这个问题。由于某些参数在传染病过程中会发生变化，我们考虑了常数和时变参数的假设。我们分析了所有模型的结构可识别性和可观测性，考虑了所有可能的输出选择和时变参数，这使我们分析了255个不同的模型版本。根据模型在不同假设条件下的结构可识别性和可观测性对模型进行了分类，并讨论了分类结果的意义。我们还用一个例子来说明几种可供选择的方法来弥补模型可观测性的不足。我们的分析为每个目的选择最有价值的模型提供了指导方针，同时考虑到现有的知识和测量。

基于负采样高阶跳图的时变图表示学习

原文标题：

Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling

地址：

http://arxiv.org/abs/2006.14330

作者:

Simone Piaggesi,André Panisson

Abstract：Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both for static and for time-varying graphs. Here, we build upon the fact that the skip-gram embedding approach implicitly performs a matrix factorization, and we extend it to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned time-varying graph representations outperform state-of-the-art methods when used to solve downstream tasks such as network reconstruction, and to predict the outcome of dynamical processes such as disease spreading. The source code and data are publicly available at https://github.com/simonepiaggesi/hosgns.

摘要：图表示学习模型是一系列成功的技术，它们将节点投射到特征空间，这些特征空间可以被其他机器学习算法利用。由于许多现实世界的网络本质上是动态的，节点之间的交互随时间变化，这些技术可以定义为静态图和时变图。在这里，我们建立在跳过格拉姆嵌入方法隐式地执行矩阵分解分解的事实之上，我们将其扩展到对时变图的不同张量表示执行隐式张量因式分解。我们证明了高阶负采样跳图(HOSGNS)能够区分节点和时间的作用，只需要其他方法所需参数的一小部分。我们使用时间分辨的面对面接近数据对我们的方法进行了实证评估，表明学习的时变图表示法在解决网络重建等下游任务和预测疾病传播等动态过程的结果时优于最先进的方法。源代码和数据可在https://github.com/simonepiaggesi/hosgns

相称社区结构的推论统计学

原文标题：

Statistical inference of assortative community structures

地址：

http://arxiv.org/abs/2006.14493

作者:

Lizhi Zhang,Tiago P. Peixoto

Abstract：We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples. In addition, we show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities, as long as there is statistical evidence for them. Our formulation is amenable to model selection procedures, which allow us to compare it to more general approaches based on the stochastic block model, and in this way reveal whether assortativity is in fact the dominating large-scale mixing pattern. We perform this comparison with several empirical networks, and identify numerous cases where the network's assortativity is exaggerated by traditional community detection methods, and we show how a more faithful degree of assortativity can be identified.

摘要：基于种植划分模型的非参数贝叶斯公式，我们提出了一种原则性的方法来推断网络中的分类群落。我们证明，这种方法成功地在网络中发现了统计意义重大的相称模块，而不像模块化最大化这样的替代方案，这种替代方案系统地超越了人工和经验实例。此外，我们证明了我们的方法不受分辨率限制，并且可以揭示任意大量的社区，只要有它们的统计证据。我们的公式是顺从模型选择程序，这使我们能够比较它与更一般的方法基于随机块模型，在这种方式下揭示是否协调性实际上是主要的大规模混合模式。我们对几个经验网络进行了比较，发现了大量的网络的协调性被传统的社区检测方法夸大的案例，并说明了如何能够识别出更加忠实的协调性程度。

稳健网络连通性的逾渗阈值

原文标题：

Percolation Thresholds for Robust Network Connectivity

地址：

http://arxiv.org/abs/2006.14496

作者:

Arman Mohseni-Kabir,Mihir Pant,Don Towsley,Saikat Guha,Ananthram Swami

Abstract：Communication networks, power grids, and transportation networks are all examples of networks whose performance depends on reliable connectivity of their underlying network components even in the presence of usual network dynamics due to mobility, node or edge failures, and varying traffic loads. Percolation theory quantifies the threshold value of a local control parameter such as a node occupation (resp., deletion) probability or an edge activation (resp., removal) probability above (resp., below) which there exists a giant connected component (GCC), a connected component comprising of a number of occupied nodes and active edges whose size is proportional to the size of the network itself. Any pair of occupied nodes in the GCC is connected via at least one path comprised of active edges and occupied nodes. The mere existence of the GCC itself does not guarantee that the long-range connectivity would be robust, e.g., to random link or node failures due to network dynamics. In this paper, we explore new percolation thresholds that guarantee not only spanning network connectivity, but also robustness. We define and analyze four measures of robust network connectivity, explore their interrelationships, and numerically evaluate the respective robust percolation thresholds for the 2D square lattice.

摘要：通信网络、电力网络和交通网络都是网络的例子，它们的性能取决于其底层网络组件的可靠连接，即使存在通常的网络动态，由于移动性、节点或边缘故障和不同的交通负荷。逾渗理论量化了一个局部控制参数的阈值，如节点占用(呼吸，删除)概率或边缘激活(呼吸，删除)概率大于(呼吸，删除)存在一个巨大的连接元件(图论)(GCC) ，一个由若干被占用的节点和活跃的边缘组成的连接元件(图论) ，其大小与网络本身的大小成正比。所述 GCC 中的任意一对被占用的节点通过至少一条由活动边和被占用节点组成的路径连接。仅仅海湾合作委员会本身的存在并不能保证远程连接是健壮的，例如，对于由网络动态引起的随机链接或节点故障。在本文中，我们探索新的逾渗阈值，不仅保证跨越网络连通性，而且健壮性。我们定义并分析了四种稳健网络连通性度量，探讨了它们之间的相互关系，并对二维正方形网格的稳健渗流阈值进行了数值评估。

预测印度新型冠状病毒肺炎

大流行的每日和累积病例数

原文标题：

Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India

地址：

http://arxiv.org/abs/2006.14575

作者:

Subhas Khajanchi,Kankan Sarkar

Abstract：The ongoing novel coronavirus epidemic has been announced a pandemic by the World Health Organization on March 11, 2020, and the Govt. of India has declared a nationwide lockdown from March 25, 2020, to prevent community transmission of COVID-19. Due to absence of specific antivirals or vaccine, mathematical modeling play an important role to better understand the disease dynamics and designing strategies to control rapidly spreading infectious diseases. In our study, we developed a new compartmental model that explains the transmission dynamics of COVID-19. We calibrated our proposed model with daily COVID-19 data for the four Indian provinces, namely Jharkhand, Gujarat, Andhra Pradesh, and Chandigarh. We study the qualitative properties of the model including feasible equilibria and their stability with respect to the basic reproduction number R0. The disease-free equilibrium becomes stable and the endemic equilibrium becomes unstable when the recovery rate of infected individuals increased but if the disease transmission rate remains higher then the endemic equilibrium always remain stable. For the estimated model parameters, R0>1 for all the four provinces, which suggests the significant outbreak of COVID-19. Short-time prediction shows the increasing trend of daily and cumulative cases of COVID-19 for the four provinces of India.

摘要：2020年3月11日，世界卫生组织和政府宣布正在进行的新型冠状病毒疫情大流行。印度政府宣布从2020年3月25日起实行全国封锁，以防止新型冠状病毒肺炎在社区传播。由于缺乏特定的抗病毒药物或疫苗，数学模型在更好地理解疾病动态和设计控制传染病快速传播的策略方面发挥着重要作用。在我们的研究中，我们开发了一个新的分室模型来解释新型冠状病毒肺炎的传播动力学。我们用印度4个省份---- 贾坎德、古吉拉特、 Andhra Pradesh 和 Chandigarh---- 的每日新型冠状病毒肺炎数据校准了我们的模型。我们研究了模型的定性性质，包括可行平衡点及其相对于基本传染数的稳定性R0.当感染者的恢复率增加时，无病平衡变得稳定，地方病平衡变得不稳定，但如果传染率保持较高，则地方病平衡始终保持稳定。对于估计的模型参数,R0>1 所有四个省份，这意味着新型冠状病毒肺炎的大规模爆发。短期预测显示，印度4个省每日和累积的新型冠状病毒肺炎病例呈上升趋势。

拓扑相关的收益

可以帮助人们摆脱囚徒困境

原文标题：

Topology dependent payoffs can lead to escape from prisoner's dilemma

地址：

http://arxiv.org/abs/2006.14593

作者:

Saptarshi Sinha,Deep Nath,Soumen Roy

Abstract：Evolutionary game theory attempts to understand the stability of cooperation in spatially restricted populations. Maintenance of cooperation is difficult, especially in the absence of spatial restrictions. There have been numerous studies of games played on graphs. It is well recognised that the underlying graph topology significantly influences the outcome of such games. A natural yet unexplored question is whether the topology of the underlying structures on which the games are played possess no role whatsoever in the determination of payoffs. Herein, we introduce a form of game payoff, which is weakly dependent on the underlying topology. Our approach is inspired by the well-known microbial phenomenon of quorum sensing. We demonstrate that even with such a weak dependence, the basic game dynamics and indeed the very nature of the game may be altered.

摘要：进化博弈论试图理解空间受限种群中合作的稳定性。维持合作是困难的，特别是在没有空间限制的情况下。已经有很多关于图形游戏的研究。众所周知，底层图形的拓扑结构显著地影响着这类游戏的结果。一个自然而未被探索的问题是，在游戏进行的基础结构的拓扑结构是否在确定收益方面没有任何作用。在此，我们引入了对策支付的一种形式，它弱依赖于底层拓扑。我们的方法是启发众所周知的微生物群体感应现象。我们证明，即使有这样一个弱的依赖性，基本的游戏动态和实际上的性质的游戏可能被改变。

来源：集智斑图

编辑：王建萍

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