The price of anarchy in mobility-driven contagion dynamics

Public policy and individual incentives determine the patterns of human mobility through transportation networks. In the event of a health emergency, the pursuit of maximum social or individual utility may lead to conflicting objectives in the routing strategies of network users. Individuals tend to avoid exposure so as to minimize the risk of contagion, whereas policymakers aim at coordinated behaviour that maximizes the social welfare. Here, we study agent-driven contagion dynamics through transportation networks, coupled to the adoption of either selfish- or policy-driven rerouting strategies. In analogy with the concept of price of anarchy in transportation networks subject to congestion, we show that maximizing individual utility leads to a loss of welfare for the social group, measured here by the total population infected after an epidemic outbreak.     

Theory and data for simulating fine-scale human movement in an urban environment

Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.     

具分布时滞的周期运动细胞神经网络周期解的存在性(英文)

运用Mawhin连续性定理研究具分布时滞的周期运动细胞神经网络周期解的存在性,假设行为函数位于一带型区域内,激活函数位于两线性函数所夹的区域内。     

基于注意力BiGRU的机械故障诊断方法研究

为了解决机械故障诊断领域传统方法自适应性差、参数选择过于依赖人工的问题,提出了一种基于循环神经网络的机械故障诊断算法.该方法利用预处理后的机械振动信号,搭建了双向门控循环单元的故障诊断模型,并进行了基于注意力机制的模型优化,提高了特征提取效率.经过美国凯斯西储大学轴承数据集以及自采集的柴油机故障实验数据验证,相比于传统...     

Coupling human mobility and social ties

Studies using massive, passively collected data from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion and organizational dynamics. More recently, these data have come tagged with geographical information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns among social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals'' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user''s ego network in terms of the type of contacts they keep is correlated with mobility behaviour. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare its ability to reproduce empirical measurements with two additional models of mobility.     

Quantifying long-term evolution of intra-urban spatial interactions

Understanding the long-term impact that changes in a city''s transportation infrastructure have on its spatial interactions remains a challenge. The difficulty arises from the fact that the real impact may not be revealed in static or aggregated mobility measures, as these are remarkably robust to perturbations. More generally, the lack of longitudinal, cross-sectional data demonstrating the evolution of spatial interactions at a meaningful urban scale also hinders us from evaluating the sensitivity of movement indicators, limiting our capacity to understand the evolution of urban mobility in depth. Using very large mobility records distributed over 3 years, we quantify the impact of the completion of a metro line extension: the Circle Line (CCL) in Singapore. We find that the commonly used movement indicators are almost identical before and after the project was completed. However, in comparing the temporal community structure across years, we do observe significant differences in the spatial reorganization of the affected geographical areas. The completion of CCL enables travellers to re-identify their desired destinations collectively with lower transport cost, making the community structure more consistent. These changes in locality are dynamic and characterized over short timescales, offering us a different approach to identify and analyse the long-term impact of new infrastructures on cities and their evolution dynamics.     

BP神经网络在测试系统动态补偿中的应用

测试系统的非线性动态补偿是仪器技术的一个重要方面．采用BP神经网络对测试系统进行动态补偿．BP神经网络的结果决定于网络输入、隐层和输出节点．由于其非线性映射特性,BP神经网络完全能够反映测试系统的动态响应特性．采用了收敛速度较快的递推预报误差算法训练神经网络．试验结果表明,BP神经网络的特性完全能够满足测试系统的动态补偿要求．表明本文的方法是有效的．     

基于深度循环神经网络的社交网络用户情感研究

目的 将深度学习与社交网络、情感计算相结合,探索利用深度神经网络进行社交网络用户情感研究的新方法和新技术,探索模型在用户需求分析和推荐上的应用.方法 自动筛选和挖掘海量社交网络数据,研究具有长时记忆的非先验情感预测方法,对网络中海量的用户数据、人与人之间关系进行建模,为关联时间序列创建LSTM模型,并结合其相互关系融入统一的大型深度循环网络中.具体包括:基于注意力模型的社交网络异构数据处理;基于深度LSTM的长时记忆建模,研究子网络选取、深度LSTM设计,以及针对社交网络的大型网络结构设计;基于社交网络情感模型和强化学习的推荐算法.结果 提高了分析的准确度,降低了对先验假设的依赖,减轻了人工情感模型的工作量和偏差,增强了对不同网络数据的普适性;供深度模型使用.结论 研究成果促进了深度学习与情感计算的结合,可推动网络用户行为分析和预测的研究,可用于个性化推荐、定向广告等领域,具有广泛的学术意义和应用前景.     

Fractional derivatives and recurrent neural networks in rheological modelling – part I: theory

The aim of this paper was to develop a general approach based on fractional time derivatives and recurrent neural networks to model the rheological behaviour of asphalt materials. The paper focuses on elastic and viscoelastic material characteristics. It consists of two parts. In this first part, the theoretical aspects of modelling are discussed. A brief introduction into the theory of rheological elements based on fractional time derivatives is provided. The fractional differential equation of a general rheological element (base element) is developed from which a huge variety of other rheological elements can be derived, e.g. fractional Newton, Kelvin and standard solid elements. A new approach is presented for solving the fractional differential equations. Artificial neural networks are developed to compute the stress–strain–time behaviour of fractional rheological elements in a numerical efficient way. The approach is tested and verified. The second part of this work will appear later. It will be focused on applications of the new theoretical work to pavement engineering problems.     

用ANN法对房间空调冷负荷进行预测的讨论

通过影响空调负荷的参数的研究,认为空调负荷是一个动态过程;结合神经网络的内在特点和功能,对某一空调系统的冷负荷进行了预测,结果能满足计算要求.在这基础上考虑了为提高神经网络预测空调负荷准确性还应进一步开展的工作.