Information flow between stock markets: A Koopman decomposition approach

Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.     

Collective behaviors of suprachiasm nucleus neurons under different light–dark cycles

The principal circadian clock in the suprachiasm nucleus(SCN) regulates the circadian rhythm of physiological and behavioral activities of mammals. Except for the normal function of the circadian rhythm, the ensemble of SCN neurons may show two collective behaviors, i.e., a free running period in the absence of a light–dark cycle and an entrainment ability to an external T cycle. Experiments show that both the free running periods and the entrainment ranges may vary from one species to another and can be seriously influenced by the coupling among the SCN neurons. We here review the recent progress on how the heterogeneous couplings influence these two collective behaviors. We will show that in the case of homogeneous coupling, the free running period increases monotonically while the entrainment range decreases monotonically with the increase of the coupling strength. While in the case of heterogenous coupling, the dispersion of the coupling strength plays a crucial role. It has been found that the free running period decreases with the increase of the dispersion while the entrainment ability is enhanced by the dispersion. These findings provide new insights into the mechanism of the circadian clock in the SCN.     

A uniform framework of projection and community detection for one-mode network in bipartite networks

Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network(named IERCP) is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.     

考虑出行行为调整的疫情传播模型与新冠疫情预测

由于新冠病毒不断变异,在很长的时期内疫情会多次爆发,每次有不同的特点.对局部地区爆发的每一波疫情进行预测,成为人们制定应对策略的关键.在宏观层面对疫情防控措施优化,意味着疫情演化数据的缺乏,这给基于实证数据的疫情预测带来了特殊的困难.考虑疫情与出行行为的相互影响,本文提出了一个改进的虫口模型,用以描述新冠疫情传播动力学过程,试图利用少量疫情相关数据对局部地区爆发的某一特定疫情进行预测.实证分析表明,该模型可以很好地复现上海市2022年3月1日到6月28日发布的新冠病毒阳性感染者数据.采用这一模型对上海市2022年12月以来的疫情趋势和关键节点进行了预测.建议决策部门按照统计学抽样原则,建立和完善疫情监测系统,为疫情预测提供可靠的数据.