Search Processes in the Exploration of Complex Data under Different Display Conditions

The study investigated user experience, display complexity, display type (tables versus graphs), and task difficulty as variables affecting the user’s ability to navigate through complex visual data. A total of 64 participants, 39 undergraduate students (novice users) and 25 graduate students (intermediate-level users) participated in the study. The experimental design was 2 × 2 × 2 × 3 mixed design using two between-subject variables (display complexity, user experience) and two within-subject variables (display format, question difficulty). The results indicated that response time was superior for graphs (relative to tables), especially when the questions were difficult. The intermediate users seemed to adopt more extensive search strategies than novices, as revealed by an analysis of the number of changes they made to the display prior to answering questions. It was concluded that designers of data displays should consider the (a) type of display, (b) difficulty of the task, and (c) expertise level of the user to obtain optimal levels of performance.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

Interference in Complex CDMA-OFDM/OQAM for Better Performance at Low SNR

This article is about orthogonal frequency-division multiplexing with quadrature amplitude modulation combined with code division multiplexing access for complex data transmission. It aims to present a method which uses two interfering subsets in order to improve the performance of the transmission scheme. The idea is to spread in a coherent manner some data amongst two different codes belonging to the two different subsets involved in complex orthogonal frequency-division multiplexing with quadrature amplitude modulation and code division multiplexing access. This will improve the useful signal level at the receiving side and therefore improve the decoding process especially at low signal to noise ratio. However, this procedure implies some interference with other codes therefore creating a certain noise which is noticeable at high signal to noise ratio.

Linear mixed-effects model for longitudinal complex data with diversified characteristics

The increasing richness of data encourages a comprehensive understanding of economic and financial activities,where variables of interest may include not only scalar(point-like)indicators,but also functional(curve-like)and compositional(pie-like)ones.In many research topics,the variables are also chronologically collected across individuals,which falls into the paradigm of longitudinal analysis.The complicated nature of data,however,increases the difficulty of modeling these variables under the classic longitudinal frame-work.In this study,we investigate the linear mixed-effects model(LMM)for such complex data.Different types of variables arefirst consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them,which gener-alizes the theoretical framework of LMM to complex data analysis.A number of simulation studies indicate the feasibility and effectiveness of the proposed model.We further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.

Source complexity of the 2016 M_W7.8 Kaikoura (New Zealand) earthquake revealed from teleseismic and InSAR data

On November 13, 2016, an MW7.8 earthquake struck Kaikoura in South Island of New Zealand. By means of back-projection of array recordings, ASTFs-analysis of global seismic recordings, and joint inversion of global seismic data and co-seismic In SAR data, we investigated complexity of the earthquake source. The result shows that the 2016 MW7.8 Kaikoura earthquake ruptured about 100 s unilaterally from south to northeast(~N28°–33°E), producing a rupture area about 160 km long and about 50 km wide and releasing scalar moment 1.01×1021 Nm. In particular, the rupture area consisted of two slip asperities, with one close to the initial rupture point having a maximal slip value ~6.9 m while the other far away in the northeast having a maximal slip value ~9.3 m. The first asperity slipped for about 65 s and the second one started 40 s after the first one had initiated. The two slipped simultaneously for about 25 s.Furthermore, the first had a nearly thrust slip while the second had both thrust and strike slip. It is interesting that the rupture velocity was not constant, and the whole process may be divided into 5 stages in which the velocities were estimated to be 1.4 km/s, 0 km/s, 2.1 km/s, 0 km/s and 1.1 km/s, respectively. The high-frequency sources distributed nearly along the lower edge of the rupture area, the highfrequency radiating mainly occurred at launching of the asperities, and it seemed that no high-frequency energy was radiated when the rupturing was going to stop.

Data Driven Uncertainty Evaluation for Complex Engineered System Design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

Analysis of Complex Correlated Interval-Censored HIV Data from Population Based Survey

In studies of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the survey. Infections are often clustered within geographical areas such as enumerator areas (EAs) and thus inducing unobserved frailty. In this paper we consider an approach for estimating parameters when infection time is unknown and assumed correlated within an EA where dependency is modeled as frailties assuming a normal distribution for frailties and a Weibull distribution for baseline hazards. The data was from a household based population survey that used a multi-stage stratified sample design to randomly select 23,275 interviewed individuals from 10,584 households of whom 15,851 interviewed individuals were further tested for HIV (crude prevalence = 9.1%). A further test conducted among those that tested HIV positive found 181 (12.5%) recently infected. Results show high degree of heterogeneity in HIV distribution between EAs translating to a modest correlation of 0.198. Intervention strategies should target geographical areas that contribute disproportionately to the epidemic of HIV. Further research needs to identify such hot spot areas and understand what factors make these areas prone to HIV.

基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一):概念、挑战与研究框架

态势感知是能源互联网的核心任务,为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响,能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴,亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用,提出能源互联网数字孪生系统;进一步,延伸出一种新型数据驱动的态势感知方法论,即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间,继而借助复杂系统理论和大数据分析等工具予以解决,具体涉及孪生体建模、分析和认知等核心环节。此外,该文也讨论元宇宙技术对孪生体互联的增强作用,以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合,对于企业数字化转型、数字城市建设等具备参考价值。

基于类C语言内存模型的复杂数据结构验证方法

对系统中操作复杂结构程序的正确性验证是保证软件高可信的重要途径,目前大多数基于高层抽象建模和程序结构拆分的方法难以满足复杂数据结构程序的验证要求。针对这一问题,论文提出基于类C语言内存模型的验证方法。首先,以内存块为基础将复杂数据结构的操作进行函数形式的定义和描述,形式化描述内存对象操作性质;其次,针对程序层定义了符合复杂结构描述的文法和语义,并基于符号化的程序逻辑进行推理。实验对嵌入式操作系统内核μC/OS-III中的复杂数据结构进行分析和自动化验证,断言描述和验证条件脚本通过了自动定理证明器的求解。

Baddeleyite from Large Complex Deposits: Significance for Archean-Paleozoic Plume Processes in the Arctic Region (NE Fennoscandian Shield) Based on U-Pb (ID-TIMS) and LA-ICP-MS Data

Baddeleyite is an important mineral geochronometer. It is valued in the U-Pb (ID-TIMS) geochronology more than zircon because of its magmatic origin, while zircon can be metamorphic, hydrothermal or occur as xenocrysts. Detailed mineralogical (BSE, KL, etc.) research of baddeleyite started in the Fennoscandian Shield in the 1990s. The mineral was first extracted from the Paleozoic Kovdor deposit, the second-biggest baddeleyite deposit in the world after Phalaborwa (2.1 Ga), South Africa. The mineral was successfully introduced into the U-Pb systematics. This study provides new U-Pb and LA-ICP-MS data on Archean Ti-Mgt and BIF deposits, Paleoproterozoic layered PGE intrusions with Pt-Pd and Cu-Ni reefs and Paleozoic complex deposits (baddeleyite, apatite, foscorite ores, etc.) in the NE Fennoscandian Shield. Data on concentrations of REE in baddeleyite and temperature of the U-Pb systematics closure are also provided. It is shown that baddeleyite plays an important role in the geological history of the Earth, in particular, in the break-up of supercontinents.

细微粒度视域下国内外跨社交网络用户对齐研究综述

针对多源复杂网络用户管理所面临的冷启动、信息孤岛问题难以满足企业在不同社交网络中识别同一用户的困境,传统的仅使用单一维度特征提取源的跨社交网络用户对齐方法难以实现迅速、精准的用户识别,而使用细微粒度视域下的多维特征提取源可以有效耦合各类用户信息的优势以提升识别效率和精准度。论文首先以国内外跨社交网络用户对齐的相关文献为研究对象进行系统性分析,从细微粒度视域出发对文献主要内容进行汇总与梳理;然后逐一细化地对比分析以总结现有不同研究所采用的特征提取源之间的差异、优势和桎梏;最后从隐私数据集获取、多维数据集成和多源社交网络数据耦合3个方面来对未来用户对齐领域中的研究前沿方向与研究深入挖掘点作出展望。

Multilevel Modeling of Binary Outcomes with Three-Level Complex Health Survey Data

Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producing a pseudo likeli-hood. In a 3-level weighted analysis for a binary outcome, we implemented two methods for scaling the sampling weights in the National Health Survey of Pa-kistan (NHSP). For NHSP with health care utilization as a binary outcome we found age, gender, household (HH) goods, urban/rural status, community de-velopment index, province and marital status as significant predictors of health care utilization (p-value < 0.05). The variance of the random intercepts using scaling method 1 is estimated as 0.0961 (standard error 0.0339) for PSU level, and 0.2726 (standard error 0.0995) for household level respectively. Both esti-mates are significantly different from zero (p-value < 0.05) and indicate consid-erable heterogeneity in health care utilization with respect to households and PSUs. The results of the NHSP data analysis showed that all three analyses, weighted (two scaling methods) and un-weighted, converged to almost identical results with few exceptions. This may have occurred because of the large num-ber of 3rd and 2nd level clusters and relatively small ICC. We performed a sim-ulation study to assess the effect of varying prevalence and intra-class correla-tion coefficients (ICCs) on bias of fixed effect parameters and variance components of a multilevel pseudo maximum likelihood (weighted) analysis. The simulation results showed that the performance of the scaled weighted estimators is satisfactory for both scaling methods. Incorporating simulation into the analysis of complex multilevel surveys allows the integrity of the results to be tested and is recommended as good practice.

移动应用服务UI界面复杂数据显示系统设计

针对传统数据显示系统存在数据显示延时较长、运行费用较高以及安全性较低等问题,设计并提出一种移动应用服务UI界面复杂数据显示系统。系统的硬件部分采用B/S模式进行设计,主要由缓冲模块、存储模块、显示模块以及警报模块组成。同时结合用户需求,组建用户友好界面模型,通过模型进行移动应用服务UI界面复杂数据在界面视觉层中的设计重构,以有效显示UI界面复杂数据。仿真实验结果表明,所设计系统能够有效降低显示延时以及运行费用,提升系统的安全性。

A Complexity Analysis and Entropy for Different Data Compression Algorithms on Text Files

In this paper, we analyze the complexity and entropy of different methods of data compression algorithms: LZW, Huffman, Fixed-length code (FLC), and Huffman after using Fixed-length code (HFLC). We test those algorithms on different files of different sizes and then conclude that: LZW is the best one in all compression scales that we tested especially on the large files, then Huffman, HFLC, and FLC, respectively. Data compression still is an important topic for research these days, and has many applications and uses needed. Therefore, we suggest continuing searching in this field and trying to combine two techniques in order to reach a best one, or use another source mapping (Hamming) like embedding a linear array into a Hypercube with other good techniques like Huffman and trying to reach good results.

Pinning sampled-data synchronization for complex networks with probabilistic coupling delay

We deal with the problem of pinning sampled-data synchronization for a complex network with probabilistic time-varying coupling delay. The sampling period considered here is assumed to be less than a given bound. Without using the Kronecker product, a new synchronization error system is constructed by using the property of the random variable and input delay approach. Based on the Lyapunov theory, a delay-dependent pinning sampled-data synchronization criterion is derived in terms of linear matrix inequalities （LMIs） that can be solved effectively by using MATLAB LMI toolbox. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

随机矩阵理论在高速路关键路径辨识中的应用

高速公路网络是我国各地区相互连接的重要纽带,高速公路网络关键路径辨识对确保高速网络的可靠运行具有重要意义。传统的关键路径分析方法基于拓扑结构,未考虑交通网络的运输量特性;而现有的基于运输量数据的分析方法只考虑部分路径的运输量特性,难以反映交通网络的实际运行情况。利用路径运输量数据,搭建运输量随机矩阵模型,针对高速公路网络异常后的运输量变化特性,定义关键路径评估指数,实现异常影响程度的量化评估,在此基础上提出一种基于数据驱动的高速公路网络关键路径辨识方法。最后,采用辽宁省高速公路网络进行分析,验证了所提方法的合理性和有效性,并将该方法应用于城市路网案例中,进一步证明该方法具有普适性。

海南岛抱板杂岩记录的多期构造热事件:锆石和独居石U-Pb年代学的制约

抱板杂岩(抱板群)是海南岛目前已发现的最古老前寒武纪结晶基底,记录了多期构造热事件,是研究海南岛大地构造属性的重要窗口。本文通过对海南岛冲卒岭地区抱板杂岩变质沉积岩、变质基性岩和伟晶岩脉的锆石和独居石LA-ICP-MS U-Pb年代学研究,来确定抱板杂岩所经历的多期构造热事件及其所代表的构造意义。研究结果显示,抱板杂岩主要由中元古代变质沉积岩、花岗质片麻岩和变质基性岩组成,经历了中元古代晚期-新元古代早期(1.3~0.9Ga)、早古生代(470~440Ma)、三叠纪(~230Ma)等多期构造热事件的改造。中元古代晚期-新元古代早期构造热事件与罗迪尼亚超大陆的汇聚有关;早古生代构造热事件与东冈瓦纳古陆北缘的增生造山作用有关;三叠纪构造热事件是印支陆块与华南陆块碰撞拼合的远程响应。综合上述研究成果和区域对比分析,认为海南岛与印支陆块具有相似的前寒武纪结晶基底和多期构造热事件的记录,表明二者具有明显的亲缘关系。

出口技术复杂度“瘸腿”型深化与全球价值链分工地位——基于数据赋能的视角

基于WIOD2016数据库和全球价值链研究院的GVC指数,本文从出口技术复杂度中存在的“瘸腿”型深化现象出发,探究提升本土数字化水平、降低高技术中间品进口依赖对一国全球价值链分工地位的影响。研究发现,出口技术复杂度“瘸腿”型深化抑制GVC分工地位的提升,主要作用机制为制约资本和人力资本的积累;“瘸腿”型深化对中低收入国家具有更强的“低端锁定”效应,对中高知识密集度行业GVC分工地位的负面影响更大;数据赋能可有效减轻“瘸腿”型深化对GVC分工地位提升的抑制作用。据此,应加大对中高数字强度行业的资源倾斜,通过数据赋能改善出口技术复杂度“瘸腿”型深化,降低对高技术产业中间品的进口依赖程度,以提升价值链分工地位。

俄乌冲突背景下全球海运网络的结构特征分析

在俄乌冲突背景下,基于AIS轨迹数据和复杂网络分析法,分析全球海运网络的整体和局部结构特征,探究海上通道的枢纽作用以及区域海运网络的抗毁性。结果表明:俄乌冲突加剧了全球海运网络的动态演变,干散货海运网络的稳定性强于集装箱海运网络和液化天然气(LNG)海运网络。全球枢纽港口呈现明显的地域性分布和结构性差异特征,中国仅少数核心港口在全球海运网络中具有优势地位。在俄乌冲突紧张局势下,俄罗斯港口在LNG海运网络中保持了一定的竞争力。欧洲地区的直布罗陀海峡和英吉利海峡在三类海运网络中起到关键的中介枢纽作用,同时海运网络对于亚洲地区的琉球群岛、新加坡海峡、朝鲜海峡等也具有较高依赖性。中国与“一带一路”沿线国家之间的集装箱海运网络和干散货海运网络、与金砖国家之间的干散货海运网络均表现出较高的发展水平和较强的抗毁性。

针刺治疗抑郁症的选穴特点及腧穴配伍规律

目的运用数据挖掘技术分析针刺治疗抑郁症的选穴特点和腧穴配伍规律。方法检索自建库以来至2021年7月的中文数据库有关针刺治疗抑郁症的文献,包括中国知网期刊全文数据库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(维普网)以及中国生物医学文献数据库(CBM)。经筛选后形成针刺治疗抑郁症的处方数据库,并利用关联规则、聚类分析、复杂网络分析等实现数据挖掘以及可视化展示。结果针刺治疗抑郁症腧穴以百会应用频次最高;腧穴归经督脉使用频次最高;关联规则中以百会-印堂、太冲-百会和太冲-内关关联性最强。结论治疗抑郁症的针刺处方以多穴配伍为主,配伍原则为远近配穴、按部位配穴与按经脉配穴相结合,针刺核心处方的核心腧穴为百会、太冲、印堂、内关、三阴交、神门、四神聪、神庭。