Formalising the Pathways to Life Using Assembly Spaces

Assembly theory (referred to in prior works as pathway assembly) has been developed to explore the extrinsic information required to distinguish a given object from a random ensemble. In prior work, we explored the key concepts relating to deconstructing an object into its irreducible parts and then evaluating the minimum number of steps required to rebuild it, allowing for the reuse of constructed sub-objects. We have also explored the application of this approach to molecules, as molecular assembly, and how molecular assembly can be inferred experimentally and used for life detection. In this article, we formalise the core assembly concepts mathematically in terms of assembly spaces and related concepts and determine bounds on the assembly index. We explore examples of constructing assembly spaces for mathematical and physical objects and propose that objects with a high assembly index can be uniquely identified as those that must have been produced using directed biological or technological processes rather than purely random processes, thereby defining a new scale of aliveness. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.     

基于动态资源预测的模糊最小相对容量损失路由算法

提出了一种适用于波分复用光网络的模糊最小相对容量损失路由模型及算法.该算法能够基于不完全的网络状态信息作出路由判断,从而减少对整个网络状态信息的需求.在此算法中,我们提出了层状态信息处理规则和模糊化的网络状态信息模型,使得该路由算法具有一定的动态资源预测的能力,并深入探讨了模糊最小相对容量损失的路由选择及性能优化原则.通过仿真试验表明,我们提出的基于层状态信息处理规则的模糊最小相对容量损失路由算法的性能与基于全网状态信息下的耗尽算法EA(exhaustive algorithm)和最小阻塞算法 LCP(least-congested-path)非常接近.当网络负载较重时,在单位信息量下的网络阻塞性能要优于EA和LCP路由.这说明与其他己知算法相比,模糊最小相对容量损失路由算法更适用于不完全状态信息下的负载较重的网络路由.     

Dynamics of Information Flow between the Chinese A-Share Market and the U.S. Stock Market: From the 2008 Crisis to the COVID-19 Pandemic Period

The relationship between the Chinese market and the US market is widely concerned by researchers and investors. This paper uses transfer entropy and local random permutation (LRP) surrogates to detect the information flow dynamics between two markets. We provide a detailed analysis of the relationship between the two markets using long-term daily and weekly data. Calculations show that there is an asymmetric information flow between the two markets, in which the US market significantly affects the Chinese market. Dynamic analysis based on weekly data shows that the information flow evolves, and includes three significant periods between 2004 and 2021. We also used daily data to analyze the dynamics of information flow in detail over the three periods and found that changes in the intensity of information flow were accompanied by major events affecting the market, such as the 2008 financial crisis and the COVID-19 pandemic period. In particular, we analyzed the impact of the S&P500 index on different industry indices in the Chinese market and found that the dynamics of information flow exhibit multiple patterns. This study reveals the complex information flow between two markets from the perspective of nonlinear dynamics, thereby helping to analyze the impact of major events and providing quantitative analysis tools for investment practice.     

Quantum Information of the Aharanov–Bohm Ring with Yukawa Interaction in the Presence of Disclination

We investigate quantum information by a theoretical measurement approach of an Aharanov–Bohm (AB) ring with Yukawa interaction in curved space with disclination. We obtained the so-called Shannon entropy through the eigenfunctions of the system. The quantum states considered come from Schrödinger theory with the AB field in the background of curved space. With this entropy, we can explore the quantum information at the position space and reciprocal space. Furthermore, we discussed how the magnetic field, the AB flux, and the topological defect influence the quantum states and the information entropy.     

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.     

Monte Carlo Simulation of Stochastic Differential Equation to Study Information Geometry

Information Geometry is a useful tool to study and compare the solutions of a Stochastic Differential Equations (SDEs) for non-equilibrium systems. As an alternative method to solving the Fokker–Planck equation, we propose a new method to calculate time-dependent probability density functions (PDFs) and to study Information Geometry using Monte Carlo (MC) simulation of SDEs. Specifically, we develop a new MC SDE method to overcome the challenges in calculating a time-dependent PDF and information geometric diagnostics and to speed up simulations by utilizing GPU computing. Using MC SDE simulations, we reproduce Information Geometric scaling relations found from the Fokker–Planck method for the case of a stochastic process with linear and cubic damping terms. We showcase the advantage of MC SDE simulation over FPE solvers by calculating unequal time joint PDFs. For the linear process with a linear damping force, joint PDF is found to be a Gaussian. In contrast, for the cubic process with a cubic damping force, joint PDF exhibits a bimodal structure, even in a stationary state. This suggests a finite memory time induced by a nonlinear force. Furthermore, several power-law scalings in the characteristics of bimodal PDFs are identified and investigated.     

相位展开的6种算法比较

相位展开算法主要可以分为路径跟踪算法和全局展开算法。为了比较这些算法的性能,采用计算机模拟斜面和高斯面,并加入不同程度噪声产生包裹相位;从两大类中各选取3种比较典型的算法分别对其进行相位展开,计算它们的均方根误差;最后对实验数据进行比较和理论分析。结果表明,基于网络规划的最小费用流算法和基于贝叶斯推断的ZπM算法是较为有效的算法,在实际应用中值得选择和改进。     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.     

基于FGCM的冠脉OCT图像纤维斑块分割算法

冠脉OCT图像中的纤维斑块精确分割对于冠心病的诊断具有重大意义。针对模糊C均值算法邻域信息和空间信息利用率低,在分割具有弱边界的纤维化斑块过程中容易产生过分割的情况,提出了基于自定义窗口的邻域信息项和具有全局约束项G的FCM算法,即FGCM算法。首先,对OCT图像进行预处理,去除噪声。然后,采用自定义窗口获取像素强度的邻域信息,并将邻域信息和创造性提出的全局约束项G添加到FCM算法,使用FGCM算法分割纤维化斑块。最后,采用数学形态学方法作为后处理操作平滑斑块边界。通过对具有典型纤维化斑块特征的8个病例进行测试,与医生手动标记纤维化斑块(金标准)和其他文献的算法进行对比,测试结果显示所提算法分割精确度进一步提高,达到90%,能够进一步对血管狭窄程度进行定量分析,辅助医生制定诊断方案。     

基于实时公交信息的乘客乘车决策研究

实时交通信息的发布使乘客出行更具灵活性.调查乘客对实时公交系统的应用情况及功能需求,并提取乘客乘车决策规则.考虑乘客决策的"有限理性"特征构建决策模型:将乘车舒适性因素转换为时间成本,运用云模型建立定量时间到定性概念的映射;依据出行参考点确,定出行方案的因素权重,建立期望效用函数;构建不同的出行场景,运用决策模型进行出...