2020-07-02 01:35:01

在一次大规模 HST 测量(WISP)中通过监督式学习识别单光谱线: 欧几里德和 WFIRST 的试点研究;

原文标题: Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation 地址:

Abstract: Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given data. We explore in depth a strategy to disambiguate between possible underlying causal networks by perturbing the network, where the actuations are either targeted or applied at random. The resulting transient dynamics provide the critical information necessary to infer causality. Two methods are shown to provide accurate causal reconstructions: Granger causality (GC) with perturbations, and our proposed perturbation cascade inference (PCI). Perturbed GC is capable of inferring smaller networks under low coupling strength regimes. Our proposed PCI method demonstrated consistently strong performance in inferring causal relations for small (2-5 node) and large (10-20 node) networks, with both linear and nonlinear dynamics. Thus the ability to apply a large and diverse set of perturbations/actuations to the network is critical for successfully and accurately determining causal relations and disambiguating between various viable networks.

摘要: 从时间序列测量数据推断因果关系是一个不适定的数学问题,通常有无限多的潜在解可以重现给定的数据。我们深入探讨了一种策略,通过扰乱网络,消除可能的潜在因果网络之间的歧义,其中的执行要么是有针对性的,要么是随机应用。由此产生的瞬态动力学提供了推断因果关系所必需的关键信息。有两种方法可以提供精确的因果重构: 带有扰动的格兰杰因果关系重构(GC)和我们提出的扰动级联推理(PCI)。在低耦合强度的情况下,扰动 GC 能够推断出较小的网络。我们提出的 PCI 方法在推断线性和非线性动态的小型(2-5节点)和大型(10-20节点)网络的因果关系方面一贯表现出强大的性能。因此,对网络应用大量和多种多样的扰动 / 驱动的能力对于成功和准确地确定各种可行网络之间的因果关系和消除歧义至关重要。

原文标题: Portraying ride-hailing mobility using multi-day trip order data: A case study of Beijing, China 地址:

Abstract: As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over the world. Although it attracts much attention of scientific community, the understanding of ride-hailing is still very limited due to a lack of related data. For the first time, this paper introduces ride-hailing drivers' multi-day trip order data in Beijing, China and portrays ride-hailing.pdf mobility from the regional and driver perspectives. The analyses from the regional perspective help to understand the spatiotemporal flowing of the ride-hailing demand, and those from the driver perspective characterize the ride-hailing drivers' preference in providing ride-hailing services. A series of findings are obtained, such as the observations of the shrinking and expanding processes of the ride-hailing demand and the two categories of the ride-hailing drivers in term of the correlations between the activity region and working time. Those findings contribute to the understanding of the ride-hailing activities, the prediction of the ride-hailing demand, the modeling of the ride-hailing drivers' preferences, and the management of the ride-hailing services.

摘要: 作为移动互联网时代的一种新兴旅行模式,通过在线平台将乘客与私家车司机联系起来的乘车服务在世界范围内非常流行。本文首次介绍了中国北京叫车司机的多日旅行订单数据,并从区域和驾驶员的角度描绘了ride-hailing.pdf的出行方式。从区域角度进行的分析有助于理解乘车需求的时空流动,而从驾驶员角度进行的分析则体现了乘车驾驶员在提供乘车服务方面的偏好。获得了一系列发现,例如,就活动区域和工作时间之间的相关性而言,对乘车需求和乘车司机两类类别的收缩和扩展过程的观察。这些发现有助于人们了解乘车活动,预测乘车需求,模拟乘车驾驶员的偏好以及管理乘车服务。

原文标题: Order-chaos-order and invariant manifolds in the bounded planar Earth-Moon system 地址:

Abstract: I n this work, we investigate the Earth-Moon system, as modeled by the planar circular restricted three-body problem, and relate its dynamical properties to the underlying structure associated to specific invariant manifolds. We consider a range of Jacobi constant values for which the neck around the Lagrangian point L1 is always open but the orbits are bounded due to Hill stability. First, we show that the system displays three different dynamical scenarios in a neighborhood of the Moon: two mixed ones, with regular and chaotic orbits, and an almost entirely chaotic one in between. We then analyze the transitions between these scenarios using the Monodromy matrix theory and determine that they are given by two specific types of bifurcations. After that, we illustrate how the phase space configurations, particularly the shapes of stability regions and stickiness, are intrinsically related to the hyperbolic invariant manifolds of the Lyapunov orbits around L1 and also to the ones of some particular unstable periodic orbits. Lastly, we define transit time in a manner which is useful to depict dynamical trapping and show that the traced geometrical structures are also connected to the transport properties of the system.

摘要: 在这项工作中,我们研究了由平面圆形限制的地月系三体问题模型,并将其动力学性质与特定不变流形的底层结构联系起来。我们考虑一系列的 Jacobi 常数值,这些常数是围绕着拉格朗日点的 L1 总是开放的,但轨道是有界的,由于希尔稳定性。首先,我们展示了该系统在月球附近呈现出三种不同的动力学场景: 两种混合的场景,有规则和混沌的轨道,以及一种几乎完全混沌的场景。然后,我们用单值矩阵理论分析了这些场景之间的转换,并确定它们是由两种特定类型的分岔给出的。然后,我们说明了相空间构型,特别是稳定区域的形状和粘性,与周围 Lyapunov 轨道的双曲不变流形有着内在的联系 L1 还有一些特殊的不稳定周期轨道。最后,我们以一种有用的方式定义渡越时间,以描述动力学陷阱,并表明跟踪的几何结构也连接到系统的输运性质。

原文标题: Compressing phase space detects state changes in nonlinear dynamical systems 地址 :

Abstract: Equations governing the nonlinear dynamics of complex systems are usually unknown and indirect methods are used to reconstruct their manifolds. In turn, they depend on embedding parameters requiring other methods and long temporal sequences to be accurate. In this paper, we show that an optimal reconstruction can be achieved by lossless compression of system's time course, providing a self-consistent analysis of its dynamics and a measure of its complexity, even for short sequences. Our measure of complexity detects system's state changes such as weak synchronization phenomena, characterizing many systems, in one step, integrating results from Lyapunov and fractal analysis.

摘要: 控制复杂系统非线性动力学的方程通常是未知的,用间接的方法重建它们的流形。反过来,它们依赖于需要其他方法和长时间序列才能准确的嵌入参数。在本文中,我们证明了一个最佳的重建可以通过无损数据压缩的系统的时间过程,提供了一个自洽的动态分析和其复杂性的措施,即使对短序列。我们的复杂性度量系统的状态变化,如弱同步现象,表征多个系统,在一个步骤中,结合李雅普诺夫和分形分析的结果。

原文标题: Rigorous computer-assisted proof for existence of period doubling renormalisation fixed points in maps with critical point of degree 4 地址:

Abstract: We gain tight rigorous bounds on the renormalisation fixed point for period doubling in families of unimodal maps with degree 4 4 critical point. We prove that the fixed point is hyperbolic and use a contraction mapping argument to bound essential eigenfunctions and eigenvalues for the linearisation and for the scaling of additive noise. We find analytic extensions of the fixed point function to larger domains. We use multi-precision arithmetic with rigorous directed rounding to bound operations in a space of analytic functions yielding tight bounds on power series and universal constants.

摘要: 我们得到了单峰地图族中周期倍的重整化不动点的严格界 4 临界点。我们证明了这个不动点是双曲型的,并且使用压缩映射变量来约束基本特征函数和特征值以实现线性化和加性噪声的缩放。我们找到了不动点函数在更大区域上的解析扩张。我们在解析函数空间中使用严格有向舍入的多精度算术,产生幂级数和普适常数的严格界。

Jonathan Colen,Ming Han,Rui Zhang,Steven A. Redford,Linnea M. Lemma,Link Morgan,Paul V. Ruijgrok,Raymond Adkins,Zev Bryant,Zvonimir Dogic,Margaret L. Gardel,Juan J. De Pablo,Vincenzo Vitelli

Abstract: Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood. Here, we use active nematics to demonstrate that neural networks can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin and actin-myosin experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.

摘要: 水动力学理论用一些宏观参数有效地描述了多体系统失去平衡的情况。然而,这样的流体动力学参数很难从显微镜得到。这种挑战很少比在活动物质中更明显,因为对于能量级联机制负责自主的大规模动态的了解很少。在这里,我们使用主动向列相学来证明神经网络可以直接从实验中提取水动力参数的时空变化。我们的算法分析微管激动素和肌动蛋白肌球蛋白实验的计算机视觉问题。与现有的方法不同,神经网络可以确定多个参数,如活性和弹性常数如何变化的 ATP 和运动浓度。此外,我们可以预测这些混沌多体系统的发展演变完全从图像序列的过去结合自动编码器和循环网络与剩余结构。我们的研究为人工智能角色塑造和在不同的物理和生物系统中控制耦合的混沌领域铺平了道路,即使没有潜在的动力学知识存在。

原文标题: Alveolar mimics with periodic strain and its effect on the cell layer formation 地址:

Milad Radiom,Yong He,Juan Peng,Armelle Baeza-Squiban,Jean-Franccois Berret,Yong Chen

Abstract: We report on the development of a new model of alveolar air-tissue interface on a chip. The model consists of an array of suspended hexagonal monolayers of gelatin nanofibers supported by microframes and a microfluidic device for the patch integration. The suspended monolayers are deformed to a central displacement of 40-80 um at the air-liquid interface by application of air pressure in the range of 200-1000 Pa. With respect to the diameter of the monolayers that is 500 um, this displacement corresponds to a linear strain of 2-10% in agreement with the physiological strain range in the lung alveoli. The culture of A549 cells on the monolayers for an incubation time 1-3 days showed viability in the model. We exerted a periodic strain of 5% at a frequency of 0.2 Hz during 1 hour to the cells. We found that the cells were strongly coupled to the nanofibers, but the strain reduced the coupling and induced remodeling of the actin cytoskeleton, which led to a better tissue formation. Our model can serve as a versatile tool in lung investigations such as in inhalation toxicology and therapy.

摘要: 我们报告了一种新的肺泡气-组织界面模型芯片的开发。该模型由一系列悬浮的六角形明胶纳米纤维单分子膜和一个微流控器件组成。在200ー1000 pa 的气压作用下,悬浮单分子膜在气液界面上发生了40ー80微米的中心位移。对于单层膜的直径为500微米,这个位移相当于2-10% 的线性应变,与肺泡内的生理应变范围相一致。在单层培养1-3天的 A549细胞在模型中表现出活性。我们在1小时内以0.2赫兹的频率向细胞施加5% 的周期性应变。我们发现,细胞与纳米纤维强烈耦合,但应变降低了耦合和肌动蛋白细胞骨架的重塑,从而导致更好的组织形成。我们的模型可以作为一个多功能的工具在肺部调查,如在吸入毒理学和治疗。

原文标题: An energy landscape approach to locomotor transitions in complex 3D terrain 地址:

Abstract: Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near-steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex 3-D terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3-D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely towards lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free energy landscapes for multi-pathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multi-pathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multi-scale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.

摘要: 在自然界中,有效的运动是通过跨越多种模式(例如,走路、跑步、爬山)来实现的。尽管如此,关于陆地运动的更多的机械理解是关于如何在单一模式下产生和稳定周围的近稳态运动。对于复杂地形下的物理作用是如何产生运动过渡的,我们知之甚少。因此,机器人在很大程度上依赖于几何地图来避开障碍物,而不是穿越它们。最近的研究表明,在复杂的三维地形中,运动的转变是以概率的方式通过多种途径发生的。在这里,我们展示了能源景观方法阐明了基本的物理原理。我们发现,动物和机器人自我推进穿越复杂的三维地形时的运动转变与潜在能量景观中跨越障碍的转变相对应。运动模式被势能障碍分隔的景观盆地所吸引。由振荡自推进产生的动能波动帮助系统随机地脱离一个水池到达另一个水池进行过渡。逃逸更有可能向较低的障碍方向。这些原理与那些接近平衡的微观系统惊人地相似。类似于蛋白质多途径折叠转换的自由能景观,我们从第一性原理出发的能量景观方法是复杂地形中多途径运动转换的统计物理学理论的开端。这不仅有助于理解动物行为的组织是如何从它们的神经系统、机械系统和物理环境之间的多尺度相互作用中产生的,而且还可以指导机器人设计、控制和规划大型的、难以处理的运动-地形参数空间,从而在现实世界中产生强大的运动过渡。

原文标题: Interpreting Holographic Molecular Binding Assays with Effective Medium Theory 地址:

Abstract: Holographic molecular binding assays use holographic video microscopy to directly detect molecules binding to the surfaces of micrometer-scale colloidal beads by monitoring associated changes in the beads' light-scattering properties. Holograms of individual spheres are analyzed by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Each fit yields an estimate of a probe bead's diameter and refractive index with sufficient precision to watch the beads grow as molecules bind. Rather than modeling the molecular-scale coating, however, these fits use effective medium theory, treating the coated sphere as if it were homogeneous. This effective-sphere analysis is rapid and numerically robust and so is useful for practical implementations of label-free immunoassays. Here, we assess how effective-sphere properties reflect the properties of molecular-scale coatings by modeling coated spheres with the discrete-dipole approximation and analyzing their holograms with the effective-sphere model.

摘要: 全息分子结合分析利用全息视频显微镜,通过监测胶珠光散射特性的相关变化,直接探测与微米级胶珠表面结合的分子。基于洛伦兹-米氏理论,通过拟合生成模型分析了单个球面的全息图,得到了球面全息图的光散射。每次试验都会得到一个探针珠子的直径和折射率的估计值,这个估计值足够精确,可以看到探针珠子随着分子的结合而生长。与其模拟分子尺度的涂层,不过,这些适合使用有效介质理论,对待涂层球体好像它是均匀的。这种有效范围的分析是快速和数值稳定的,因此对于实际实施无标记免疫分析是有用的。本文通过用离散偶极子近似模拟涂层球体,并用有效球模型分析涂层球体的全息图,评价了有效球体的性质如何反映分子尺度涂层的性质。

Abstract: This paper presents a unified mathematical theory of swarms where the dynamics of social behaviors interacts with the mechanical dynamics of self-propelled particles. The term behavioral swarms is introduced to characterize the specific object of the theory which is subsequently followed by applications. As concrete examples for our unified approach, we show that several Cucker-Smale type models with internal variables fall down to our framework. Subsequently the modeling goes beyond the Cucker-Smale approach and looks ahead to research perspectives.

摘要:本文提出了一个关于群的统一数学理论,其中社会行为的动力学与自驱动粒子的力学动力学相互作用。行为蜂群这个术语被引入来描述理论的特定对象,随后被应用。作为我们统一方法的具体例子,我们展示了一些内部变量的 Cucker-Smale 类型模型落到我们的框架中。随后的建模超越了 Cucker-Smale 方法,并展望了研究前景。

Abstract: Following up on a previous work we examine a model of transportation network in some source-sink flow paradigm subjected to growth and resource allocation. The model is inspired from plants, and we add rules and factors that are analogous to what plants are subjected to. We study how different resource allocation schemes affect the tree and how the schemes interact with additional factors such as embedding the network into a 3D space and applying gravity or shading. The different outcomes are discussed. PACS. 05.45.-a Nonlinear dynamics and chaos-05.65.+b Self-organized systems

摘要: 在以前工作的基础上,我们研究了一个在源汇流范式下的交通网络模型,该模型受到增长和资源分配的影响。这个模型的灵感来自于植物,我们添加了一些规则和因素,这些规则和因素类似于植物所受到的影响。我们研究了不同的资源分配方案如何影响树,以及这些方案如何与其他因素相互作用,如嵌入网络到一个三维空间和应用重力或阴影。讨论了不同的结果。医学影像存储系统。05.45.-a 非线性动力学与混沌 -05.65。+ b 自组织系统。

原文标题: Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement 地址:

Stewart W Doe,Tyler Russell Seekins,David Fitzpatrick,Dawsin Blanchard,Salimeh Yasaei Sekeh

Abstract: Accurately forecasting county level COVID-19 confirmed cases is crucial to optimizing medical resources. Forecasting emerging outbreaks pose a particular challenge because many existing forecasting techniques learn from historical seasons trends. Recurrent neural networks (RNNs) with LSTM-based cells are a logical choice of model due to their ability to learn temporal dynamics. In this paper, we adapt the state and county level influenza model, TDEFSI-LONLY, proposed in Wang et a. [l2020] to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We analyze the two week ahead forecasting capabilities of the TDEFSI-LONLY model with combinations of regularization techniques. Effective training of the TDEFSI-LONLY model requires data augmentation, to overcome this challenge we utilize an SEIR model and present an inter-county mixing extension to this model to simulate sufficient training data. Further, we propose an alternate forecast model, {it County Level Epidemiological Inference Recurrent Network} (alg{}) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern and utilizes a time distributed dense layer to learn individual county confirmed case changes each day for a two weeks forecast. We show that the best, worst, and median state forecasts made using CLEIR-Net model are respectively New York, South Carolina, and Montana.

摘要: 准确预测县级新型冠状病毒肺炎确诊病例对于优化医疗资源至关重要。由于许多现有的预测技术借鉴了历史季节趋势,因此预测新出现的疫情暴发尤其具有挑战性。基于 lstm 细胞的回归神经网络(RNNs)具有学习时间动态的能力,是一种合理的模型选择。在本文中,我们将 Wang 等[12020]提出的州和县两级流感模型(tfsi-lonly)适用于国家和县两级流感新型冠状病毒肺炎数据。我们表明,这个模型对当前大流行的预测很差。我们分析了两周前的预测能力的 TDEFSI-LONLY 模型与正则化技术的组合。为了克服这一困难,我们利用了一个 SEIR 模型,并对该模型进行了跨县混合扩展,以模拟足够的训练数据。进一步,我们提出了一个替代的预测模型,{ it 县级流行病学推断循环网络}(alg {}) ,该模型在全国确诊病例上训练 LSTM 骨干来学习低维时间模式,并利用时间分布密集层来学习每个县确诊病例每天的变化,为期两周的预测。我们表明,最好,最坏,和中位数州预测使用 CLEIR-Net 模型分别是纽约,南卡罗来纳州和蒙大拿州。

K. Kovalenko,I. Sendia-Nadal,N. Khalil,A. Dainiak,D. Musatov,K. Alfaro-Bittner,B. Barzel,S. Boccaletti

Abstract: The past two decades have seen significant successes in our understanding of complex networked systems, from the mapping of real-world social, biological and technological networks to the establishment of generative models recovering their observed macroscopic patterns. These advances, however, are restricted to pairwise interactions, captured by dyadic links, and provide limited insight into higher-order structure, in which a group of several components represents the basic interaction unit. Such multi-component interactions can only be grasped through simplicial complexes, which have recently found applications in social and biological contexts, as well as in engineering and brain science. What, then, are the generative models recovering the patterns observed in real-world simplicial complexes? Here we introduce, study, and characterize a model to grow simplicial complexes of order two, i.e. nodes, links and triangles, that yields a highly flexible range of empirically relevant simplicial network ensembles. Specifically, through a combination of preferential and/or non preferential attachment mechanisms, the model constructs networks with a scale-free degree distribution and an either bounded or scale-free generalized degree distribution - the latter accounting for the number of triads surrounding each link. Allowing to analytically control the scaling exponents we arrive at a highly general scheme by which to construct ensembles of synthetic complexes displaying desired statistical properties.

摘要: 在过去20年中,我们在理解复杂的网络系统方面取得了重大成功,从绘制真实世界的社会、生物和技术网络图,到建立恢复观察到的宏观模式的生成模型。然而,这些进步仅限于两两之间的相互作用,由并矢链接捕获,并提供了对高阶结构的有限洞察,在高阶结构中,一组由若干个组件代表基本的相互作用单元。这种多组分的相互作用只能通过简单的复合体来掌握,这种复合体最近在社会和生物学领域,以及在工程和脑科学中得到了应用。那么,什么是生成模型恢复模式观察到的现实世界单纯复形?在这里,我们介绍,研究和刻画了一个模型生长二阶单纯复形,即节点,链路和三角形,生成一个高度灵活的范围经验相关的单纯网络集成。具体而言,通过优先和 / 或非优先连接机制的组合,该模型构造了无标度分布的网络,以及有界或无标度的广义度分布——后者占据每个连接周围的三元数。通过分析控制标度指数,我们得到了一个高度通用的方案,通过这个方案可以构造出具有所需统计特性的合成复合物的整体。

原文标题: The dynamics of a driven harmonic oscillator coupled to independent Ising spins in random fields 地址:

Abstract: We aim at an understanding of the dynamical properties of a periodically driven damped harmonic oscillator coupled to a ac{RFIM} at zero temperature, which is capable to show complex hysteresis. The system is a combination of a continuous (harmonic oscillator) and a discrete (ac{RFIM}) subsystem, which classifies it as a hybrid system. In this paper we focus on the hybrid nature of the system and consider only independent spins in quenched random local fields, which can already lead to complex dynamics such as chaos and multistability. We study the dynamic behavior of this system by using the theory of piecewise-smooth dynamical systems and discontinuity mappings. Specifically, we present bifurcation diagrams, Lyapunov exponents as well as results for the shape and the dimensions of the attractors and the self-averaging behavior of the attractor dimensions and the magnetization. Furthermore we investigate the dynamical behavior of the system for an increasing number of spins and the transition to the thermodynamic limit, where the system behaves like a driven harmonic oscillator with an additional nonlinear smooth external force.

摘要: 我们的目标是理解在零温下周期驱动的阻尼谐振子与交流阻尼器耦合的动力学特性,它能够显示复杂的滞后现象。这个系统是一个连续的(谐振子)和一个离散的(ac { RFIM })子系统的组合,它被归类为一个混合系统。在本文中,我们着眼于系统的混合本质,仅考虑淬灭随机局域场中的独立自旋,这已经导致了混沌和多稳定性等复动力学。利用分段光滑动力系统理论和不连续映射研究了该系统的动力学行为。给出了系统的分岔图、李雅普诺夫指数、吸引子的形状和维数以及吸引子维数和磁化强度的自平均特性。此外,我们还研究了系统在自旋数量增加和向热力学极限的跃迁时的动力学行为,在这种情况下,系统表现得像一个带有附加非线性光滑外力的驱动谐振子。

Abstract: Complex networks are characterized by latent geometries induced by their topology or by the dynamics on the top of them. In the latter case, different network-driven processes induce distinct geometric features that can be captured by adequate metrics. Random walks, a proxy for a broad spectrum of processes, from simple contagion to metastable synchronization and consensus, have been recently used in [Phys. Rev. Lett. 118, 168301 (2017)] to define the class of diffusion geometry and pinpoint the functional mesoscale organization of complex networks from a genuine geometric perspective. Here, we firstly extend this class to families of distinct random walk dynamics -- including local and non-local information -- on the top of multilayer networks -- a paradigm for biological, neural, social, transportation, biological and financial systems -- overcoming limitations such as the presence of isolated nodes and disconnected components, typical of real-world networks. Secondly, we characterize the multilayer diffusion geometry of synthetic and empirical systems, highlighting the role played by different random search dynamics in shaping the geometric features of the corresponding diffusion manifolds.

摘要: 复杂网络是由其拓扑结构或其顶部的动力学诱导的拥有属性潜在几何。在后一种情况下,不同的网络驱动过程产生了可以被适当的度量所捕获的不同的几何特征。从简单的传染到亚稳定的同步化和一致性,随机游动作为一个广泛的过程的代理,最近在[ Phys ]中得到了应用。雷夫 · 莱特。118,168301(2017)]定义扩散几何学的类别,并从真正的几何学角度精确定位复杂网络的功能性中尺度组织。在这里,我们首先将这个类扩展到具有不同随机行走动力学的家族——包括局部和非局部信息——在多层网络的顶层——生物、神经、社会、交通、生物和金融系统的范例——克服了现实世界网络中典型的孤立节点和不连续组件的存在等局限性。其次,刻画了合成扩散系统和经验扩散系统的多层扩散几何特征,突出了不同的随机搜索动力学在形成相应扩散流形的几何特征中所起的作用。

原文标题: Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application 地址:

Ana Ruiz-Frau,Andres Ospina-Alvarez,Sebastin Villasante,Pablo Pita,Isidro Maya-Jariego,Silvia de Juan Mohan

Abstract: The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter's bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different social media platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.

摘要: 社会媒体(SM)数据的使用已经成为评估文化生态系统服务(CES)的一个有前途的工具。大多数研究集中在单一的 SM 平台的使用和照片内容的分析,以评估对 CES 的需求。在这里,我们通过图论网络分析(GTNA)对与 SM 帖子相关的 hashtag 进行分析,并将其与照片内容分析进行比较,从而提出了一种利用 SM 数据评估 CES 的新方法。我们在 Instagram 和 Twitter 这两个 SM 平台上应用了所提出的方法,并在3个世界知名的案例研究领域---- 大堡礁、科隆群岛和复活节岛---- 进行了应用。我们的研究结果表明,通过图论对 # 标签的分析提供了与照片内容分析相似的功能,用于评估 CES 服务和识别 CES 服务提供商。更重要的是,GTNA 为照片内容分析提供了更强大的识别关系价值和与自然相关的真实性方面的能力,这些方面难以捉摸。此外,GTNA 有助于减少解释器对照片内容分析的偏见,因为 GTNA 是基于用户自己提供的标签。这项研究还强调了考虑来自不同社交媒体平台的数据的重要性,因为用户类型和这些平台提供的信息可以显示不同的 CES 属性。应用 GTNA 的方便性和计算处理时间短,使其成为一种具有成本效益的方法,有可能应用于大规模的地理范围。

原文标题: Opinion Diffusion Software with Strategic Opinion Revelation and Unfriending 地址:

Abstract: We present a novel software suite for social network modeling and opinion diffusion processes. Much research on social network science has assumed networks with static topologies. More recently, attention has been turned to networks that evolve. Although software for modeling both the topological evolution of networks and diffusion processes are constantly improving, very little attention has been paid to agent modeling. Our software is designed to be robust, modular, and extensible, providing the ability to model dynamic social network topologies and multidimensional diffusion processes, different styles of agent including non-homophilic paradigms, as well as a testing environment for multi-agent reinforcement learning (MARL) experiments with diverse sets of agent types. We also illustrate the value of diverse agent modeling, and environments that allow for strategic unfriending. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on individuals' goals for the composition of their neighborhood's opinions.

摘要: 我们提出了一个新的软件套件用于社会网络建模和意见传播过程。社会网络科学的许多研究假定网络具有静态拓扑结构。最近,人们的注意力转向了进化中的网络。虽然用于建模网络拓扑演化和扩散过程的软件正在不断改进,但是对于 agent 建模的研究还很少。我们的软件被设计成健壮的、模块化的和可扩展的,提供了对动态社会网络拓扑和多维扩散过程建模的能力,不同类型的代理包括非同源范例,以及一个多代理强化学习实验的测试环境。我们还说明了多样化代理建模的价值,以及允许战略性解除好友关系的环境。我们的工作表明,极化和共识动态,以及拓扑聚类效应,可能依赖于比以前知道的更多的个人的目标组成他们的邻居的意见。

原文标题: Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction 地址:

Abstract: The spectral evolution model aims to characterize the growth of large networks (i.e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the adjacency matrices. It assumes that, while eigenvectors remain constant, eigenvalues evolve in a predictable manner over time. This paper extends the original formulation of the model twofold. First, it presents a method to compute an approximation of the spectral evolution of eigenvalues based on the Rayleigh quotient. Second, it proposes an algorithm to estimate the evolution of eigenvalues by extrapolating only a fraction of their approximated values. The proposed model is used to characterize mention networks of users who posted tweets that include the most popular political hashtags in Colombia from August 2017 to August 2018 (the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). To evaluate the extent to which the spectral evolution model resembles these networks, link prediction methods based on learning algorithms (i.e., extrapolation and regression) and graph kernels are implemented. Experimental results show that the learning algorithms deployed on the approximated trajectories outperform the usual kernel and extrapolation methods at predicting the formation of new edges.

摘要: 谱演化模型旨在通过邻接矩阵的特征值分解来刻画大型网络的增长(即,它们是如何演化成新的边的)。它假定,当特征向量保持不变时,特征值随时间以一种可预测的方式演化。本文对模型的原有公式进行了二重推广。首先,提出了一种基于瑞利商的特征值谱演化的近似计算方法。其次,它提出了一种算法来估计特征值的演化,只外推其近似值的一小部分。提议的模型用于描述2017年8月至2018年8月(即哥伦比亚革命武装部队解除武装的时期)发布包括哥伦比亚最流行的政治标签的推文的用户提及网络的特征。为了评估谱进化模型与这些网络的相似程度,实现了基于学习算法(即外推和回归)和图核的链路预测方法。实验结果表明,基于近似轨迹的学习算法在预测新边缘的形成方面优于传统的核和外推方法。

原文标题: Graph Prototypical Networks for Few-shot Learning on Attributed Networks 地址:

Abstract: Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes only contain limited labeled instances, rendering a long-tail node class distribution. Existing node classification algorithms are unequipped to handle the textit{few-shot} node classes. As a remedy, few-shot learning has attracted a surge of attention in the research community. Yet, few-shot node classification remains a challenging problem as we need to address the following questions: (i) How to extract meta-knowledge from an attributed network for few-shot node classification? (ii) How to identify the informativeness of each labeled instance for building a robust and effective model? To answer these questions, in this paper, we propose a graph meta-learning framework -- Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task. Extensive experiments demonstrate the superior capability of GPN in few-shot node classification.

摘要: 如今,属性化网络在无数高影响力的应用中无处不在,比如社交网络分析、金融欺诈检测和药物发现。节点分类作为属性网络的核心分析任务,受到了研究界的广泛关注。在现实的属性化网络中,很大一部分节点类只包含有限的标记实例,呈现出长尾节点类分布。现有的节点分类算法无法处理 textit { few-shot }节点类。作为一种补救措施,“几杆学习”在研究界引起了极大的关注。然而,少镜头节点分类仍然是一个具有挑战性的问题,因为我们需要解决以下问题: (i)如何从属性网络中提取元知识进行少镜头节点分类?(ii)如何确定每个被标记实例的信息性,以建立一个健全和有效的模型?为了回答这些问题,本文提出了一个图元学习框架——图原型网络(GPN)。通过构造一个半监督的节点分类任务池来模拟真实的测试环境,GPN 能够在一个属性化网络上执行文本{元学习} ,并推导出一个高度通用的目标分类任务处理模型。大量的实验证明了 GPN 在少镜头节点分类方面的优越性能。

原文标题: Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications 地址:

Jamie Pool,Ebrahim Beyrami,Vishak Gopal,Ashkan Aazami,Jayant Gupchup,Jeff Rowland,Binlong Li,Pritesh Kanani,Ross Cutler,Johannes Gehrke

Abstract: Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population, and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We present Lumos, a Python library built using the principles of AB testing to systematically diagnose metric regressions to automate such analysis. Lumos has been deployed across the component teams in Microsoft's Real-Time Communication applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application of Lumos has resulted in freeing up as much as 95% of the time allocated to metric-based investigations. In this work, we open source Lumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.

摘要: Web 规模的应用程序可以按照每天到每周的节奏发布代码。这些应用程序依赖于在线指标来监视新版本的健康状况。公制值的回归需要尽早检测和诊断,以减少对用户和产品所有者的干扰。指标回归可能出现的原因有很多: 真正的产品回归、用户群体的变化、遥测数据丢失(或处理)导致的偏见都是常见的原因。对于工程团队来说,诊断这些度量回归的原因成本很高,因为他们需要花费时间尽快找到问题的根本原因。我们介绍了 Lumos,这是一个使用 AB 测试原理构建的 Python 库,用于系统地诊断度量回归以自动进行这种分析。Lumos 已经部署在微软实时通信应用程序 Skype 和微软团队的组件团队中。它使工程团队能够检测到100个真实的指标变化,并拒绝由异常检测器检测到的1000个假警报。Lumos 的申请使得多达95% 的时间用于基于公制的调查。在这项工作中,我们开放源码 Lumos,并在数百万个会话中将其应用于 RTC 组中的两个不同组件,从而显示我们的结果。这个通用库可以与任何生产系统相结合,以有效地管理报警的数量。

原文标题: The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks 地址:

Abstract: We propose a novel probabilistic framework to model continuous-time interaction events data. Our goal is to infer the emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the community structure influences the interaction dynamics among these nodes. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities. Moreover, our model can flexibly incorporate the auxiliary individuals' attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via P'olya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance for temporal link prediction compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.

摘要: 我们提出了一个新的概率框架来模拟连续时间的相互作用事件数据。我们的目标是推断实体之间时间相互作用背后的 emph { implicit }社区结构,并利用社区结构如何影响这些节点之间的相互作用动力学。为此,我们使用相互激励的霍克斯过程来模拟个体之间的往复交互作用。基于层次伽玛过程边界划分模型(HGaP-EPM)推导出每对个体的 Hawkes 过程的基本概率。特别是,我们的模型允许每对个体之间的相互作用动态被他们各自的附属社区所调整。此外,我们的模型可以灵活地结合辅助个体的属性,或者与交互事件相关的协变量。利用 p olya-Gamma 数据增强策略,开发了高效的 Gibbs 采样和期望最大化算法来进行推理。在现实数据集上的实验结果表明,该模型不仅在时间链路预测方面比现有方法具有更强的竞争性,而且还发现了隐藏在观察到的时间交互背后的可解释的潜在结构。

原文标题: Identification of single spectral lines through supervised machine learning in a large HST survey (WISP): a pilot study for Euclid and WFIRST 地址:

I. Baronchelli,C. M. Scarlata,G. Rodighiero,L. Rodrguez-Muoz,M. Bonato,M. Bagley,A. Henry,M. Rafelski,M. Malkan,J. Colbert,Y. S. Dai,H. Dickinson,C. Mancini,V. Mehta,L. Morselli,H. I. Teplitz

A bstract:Future surveys focusing on understanding the nature of dark energy (e.g., Euclid and WFIRST) will cover large fractions of the extragalactic sky in near-IR slitless spectroscopy. These surveys will detect a large number of galaxies that will have only one emission line in the covered spectral range. In order to maximize the scientific return of these missions, it is imperative that single emission lines are correctly identified. Using a supervised machine-learning approach, we classified a sample of single emission lines extracted from the WFC3 IR Spectroscopic Parallel survey (WISP), one of the closest existing analogs to future slitless surveys. Our automatic software integrates a SED fitting strategy with additional independent sources of information. We calibrated it and tested it on a "gold" sample of securely identified objects with multiple lines detected. The algorithm correctly classifies real emission lines with an accuracy of 82.6%, whereas the accuracy of the SED fitting technique alone is low (~50%) due to the limited amount of photometric data available (=6 bands). While not specifically designed for the Euclid and WFIRST surveys, the algorithm represents an important precursor of similar algorithms to be used in these future missions.

摘要: 未来的调查将着眼于了解暗能量的本质(例如,欧几里得和 WFIRST) ,将在近红外无缝光谱学中覆盖银河系外天空的大部分。这些勘测将探测到大量的星系,这些星系在被覆盖的光谱范围内只有一条发射线。为了最大限度地提高这些飞行任务的科学回报,必须正确地确定单一发射线。利用有监督的机器学习方法,我们对从 WFC3 IR 光谱平行测量(WISP)中提取的单发射线样品进行了分类。WFC3 IR 光谱平行测量是目前最接近未来无缝测量的类似物之一。我们的自动化软件集成了 SED 拟合策略与其他独立的信息来源。我们对它进行校准,并在一个“黄金”样本上进行测试,该样本由多条线探测到的安全识别物体组成。该算法对实际发射线的正确分类准确率为82.6% ,而单独使用 SED 拟合技术的准确率较低(约50%) ,原因是可用的光度数据量有限( = 6波段)。虽然该算法不是专门为欧几里得和 WFIRST 调查设计的,但它代表了类似算法的重要先驱,将用于未来的任务。

原文标题: Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography 地址:

Abstract: Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate) and show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution.

摘要: 基于图像的诊断是解释上下文的空间分辨率。断层图像重建也是如此。目前,基于经验驱动的空间分辨率量化方法依赖于基于点扩散函数的确定性公式,忽略了统计先验信息,这是秩亏层析成像不可或缺的一部分。提出了一种基于重建协方差(点估计)的统计空间分辨率测度方法,证明了先验信息是空间分辨率的一个下限。此外,考虑到空间分辨率的不均匀性,空间分辨率测度可用于层析成像系统的设计。

原文标题: Gaussian Processes for Surrogate Modeling of Discharged Fuel Nuclide Compositions 地址:

Abstract: Several applications such as nuclear forensics, nuclear fuel cycle simulations and sensitivity analysis require methods to quickly compute spent fuel nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have been pre-computed from nuclear reactor simulations for a grid of input parameters, using fits such as Cubic Spline. We propose the use of Gaussian Processes (GP) to create surrogate models, which not only provide nuclide compositions, but also the gradient and estimates of their prediction uncertainty. The former is useful for applications such as forward and inverse optimization problems, the latter for uncertainty quantification applications. For this purpose, we compare GP-based surrogate model performance with Cubic- Spline-based interpolators based on infinite lattice simulations of a CANDU 6 nuclear reactor using the SERPENT 2 code, considering burnup and temperature as input parameters. Additionally, we compare the performance of various grid sampling schemes to quasirandom sampling based on the Sobol sequence. We find that GP-based models perform significantly better in predicting spent fuel compositions than Cubic-Spline-based models, though requiring longer computational runtime. Furthermore, we show that the predicted nuclide uncertainties are reasonably accurate. While in the studied two-dimensional case, grid- and quasirandom sampling provide similar results, quasirandom sampling will be a more effective strategy in higher dimensional cases.

摘要: 一些应用,如核取证,核燃料循环模拟和敏感度分析,需要快速计算各种辐照历史的乏燃料核素组成的方法。传统上,这是通过插值之间的单组截面已预先计算从核反应堆模拟的输入参数网格,使用拟合,如三次样条。我们建议使用高斯过程(GP)来建立替代模型,它不仅提供核素组成,而且提供它们的预测不确定性的梯度和估计。前者适用于正向和反向优化问题,后者适用于不确定性量化应用。为此,我们采用 serp2程序对 candu6反应堆进行了无限格点模拟,将燃耗和温度作为输入参数,比较了基于 gp 的代理模型和基于三次样条插值的代理模型的性能。此外,我们比较了各种网格采样方案和基于 Sobol 序列的准随机采样方案的性能。我们发现基于 gp 的模型在预测乏燃料成分方面比基于三次样条的模型表现得更好,尽管需要更长的计算时间。此外,我们还证明了预测的核素不确定度是合理的。在二维情况下,网格和准随机抽样得到了相似的结果,而在高维情况下,准随机抽样将是一种更有效的策略。

原文标题: Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels 地址:

Abstract: While several electrocardiogram-derived respiratory (EDR) algorithms have been proposed to extract breathing activity from a single-channel ECG signal, conclusively identifying a superior technique is challenging. We propose viewing each EDR algorithm as a {em software sensor} that records the breathing activity from the ECG signal, and ensembling those software sensors to achieve a higher quality EDR signal. We refer to the output of the proposed ensembling algorithm as the {em ensembled EDR}. We test the algorithm on a large scale database of 116 whole-night polysomnograms and compare the ensembled EDR signal with four respiratory signals recorded from four different hardware sensors. The proposed algorithm consistently improves upon other algorithms, and we envision its clinical value and its application in future healthcare.

摘要: 虽然人们已经提出了多种心电图衍生的呼吸算法(EDR)来从单通道 ECG 信号中提取呼吸活动,但是最终确定一种优越的技术仍然是一个挑战。我们建议将每个 EDR 算法视为一个{ em 软件传感器} ,从 ECG 信号中记录呼吸活动,并将这些软件传感器集成以获得更高质量的 EDR 信号。我们将所提出的集成算法的输出称为{ em 集成 EDR }。我们在116个全夜多导睡眠图的大规模数据库上测试了该算法,并将 EDR 集成信号与4个不同硬件传感器记录的4个呼吸信号进行了比较。本文提出的算法不断改进其他算法,我们展望其临床价值及其在未来医疗保健中的应用。