Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

Sample-data Decentralized Reliable H∞ Hyperbolic Control for Uncertain Fuzzy Large-scale Systems with Time-varying Delay

这份报纸学习样品数据的问题为有变化时间的延期的不明确的连续时间的模糊大规模系统的可靠 H 夸张控制。第一，模糊夸张模型( FHM )被用来为某些复杂大规模系统建立模型，然后根据 Lyapunov 指导方法和大规模系统的分散的控制理论，线性 matrixine 质量( LMI )基于条件 arederived toguarantee H 性能不仅当所有控制部件正在操作很好时，而且面对一些可能的致动器失败。而且，致动器的精确失败参数没被要求，并且要求仅仅是失败参数的更低、上面的界限。条件依赖于时间延期的上面的界限，并且不依赖于变化时间的延期的衍生物。因此，获得的结果是不太保守的。最后，二个例子被提供说明设计过程和它的有效性。

Interactive Generalization on Large-Scale Topographical Map Supported by a Database Platform

This paper makes astudy on the interactive digital gener-alization, where map generalizationcan be divided into intellective reason-ing procedure and operational proce-dure, which are done by human andcomputer, respectively. And an inter-active map generalization environmentfor large scale topographic map is thendesigned and realized. This researchfocuses on: ① the significance of re-searching an interactive map generali-zation environment, ② the features oflarge scale topographic map and inter-active map generalization, ③ the con-struction of map generalization-orien-ted database platform.

Trend Analysis of Large-Scale Twitter Data Based on Witnesses during a Hazardous Event: A Case Study on California Wildfire Evacuation

Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders.

Regularized focusing inversion for large-scale gravity data based on GPU parallel computing

Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes the application of GPU parallel processing technology to the focusing inversion method, aiming at improving the inversion accuracy while speeding up calculation and reducing the memory consumption, thus obtaining the fast and reliable inversion results for large complex model. In this paper, equivalent storage of geometric trellis is used to calculate the sensitivity matrix, and the inversion is based on GPU parallel computing technology. The parallel computing program that is optimized by reducing data transfer, access restrictions and instruction restrictions as well as latency hiding greatly reduces the memory usage, speeds up the calculation, and makes the fast inversion of large models possible. By comparing and analyzing the computing speed of traditional single thread CPU method and CUDA-based GPU parallel technology, the excellent acceleration performance of GPU parallel computing is verified, which provides ideas for practical application of some theoretical inversion methods restricted by computing speed and computer memory. The model test verifies that the focusing inversion method can overcome the problem of severe skin effect and ambiguity of geological body boundary. Moreover, the increase of the model cells and inversion data can more clearly depict the boundary position of the abnormal body and delineate its specific shape.

Large scale classification with local diversity AdaBoost SVM algorithm

Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

Discussion on Key Issues of UAV Large-scale Mapping

The main constituent parts of unmanned aerial vehicle aerial photogrammetry systems are discussed.The key issues including the division of regional networks,the layout of regional networks,the correction of lens distortion,the optimization of external orientation elements,the aerial triangulation,the image matching and fusion,and the production of digital elevation models and digital orthoimages,tilt real 3 D models,and digital line drawings,were analyzed.The advantages of UAV aerial photogrammetry were compared.This study provides reference for measuring large-scale topographic maps by UAV photogrammetry systems.

Understanding the Dynamics Location of Very Large Populations Interacted with Service Points

This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.

基于空间投影和聚类划分的SVR加速算法

数据不仅能产生价值,还对统计学的科学发展提供了动力。随着科技的飞速发展,海量数据得以涌现,但大规模的数据会导致很多传统处理方法很难满足各领域对数据分析的需求。面对海量数据时代学习算法的低效性,分治法通常被认为是解决这一问题最直接、最广泛使用的策略。SVR是一种强大的回归算法,在模式识别和数据挖掘等领域有广泛应用。然而在处理大规模数据时,SVR训练效率低。为此,该文利用分治思想提出一种基于空间投影和聚类划分的SVR加速算法(PKM-SVR)。利用投影向量将数据投影到二维空间;利用聚类方法将数据空间划分为k个互不相交的区域;在每个区域上训练SVR模型;利用每个区域的SVR模型预测落入同一区域的待识别样本。在标准数据集上与传统的数据划分方法进行对比实验,实验结果表明该算法训练速度较快,并表现出更好的预测性能。

MapReduce模型在大规模数据并行挖掘中的应用

MapReduce并行编程模型通过定义良好的接口和运行支持库,能够自动并行执行大规模计算任务,隐藏底层实现细节,降低并行编程的难度。系统阐述MapReduce的基本工作原理及其工作流程,以TeraSort算法为例,针对其存在的问题,提出动态数据分区和数据压缩等优化建议。研究成果表明,优化后的TeraSort算法能够显著缩短数据处理时间,优化系统的吞吐量,并改善资源分配的均衡性。

超大规模数据处理中并行计算技术的应用研究

随着人工智能和大数据时代的到来,超大规模数据处理成了一个重要的研究领域。该文主要探讨并行计算技术在超大规模数据处理中的应用,首先详细阐述并行计算和超大规模数据处理的基本理论与概念,特别是并行计算的编程模型与工具,最后通过分析并行计算在搜索引擎、气象预报和金融分析等中的实际案例,阐述并行计算技术在超大规模数据处理中的实际应用。

战略需求导向下的资金集中管理模式变革研究——基于美的集团的纵向案例分析

基于美的集团纵向单案例,分析企业资金集中管理模式随战略需求变化而动态调整的实施效果。研究发现,从机会成长阶段—规模成长阶段—优化升级阶段,美的集团战略需求呈现出“异地扩张—产业扩张—国际化与多元化扩张”的变化态势,其资金集中管理模式经历了“结算中心异地结算模式—数据大集中模式—财务公司”的动态调整,资金集中管理模式对应职能也在动态调整中完成了“异地资金管理能力—跨业务协调能力—金融发展能力”的叠加。美的集团根据战略需求变化动态调整资金集中管理模式是资金集中管理活动成功的关键,这一研究发现对战略需求影响资金集中管理模式的理论解释提供了案例依据,也为集团企业的资金集中管理模式选择提供了启示。

空间站大规模复杂控制软件基于数据池的软件框架设计

针对复杂航天任务中控制软件不断增长的规模和复杂度带来的问题,对国外先进航天航空机构软件架构设计经验和最佳实践进行了调研,基于此对空间站大规模复杂航天器控制软件的通用框架设计进行研究,提出了一种基于数据池的软件框架设计方法,解决了空间站大规模复杂控制软件多舱段多机多总线资源管理和可扩展性的难题,实现了软件复杂的底层通信与上层应用处理之间的数据隔离和解耦,有效提高了软件复用率和可扩展能力。

基于三阶张量的大规模数据谱聚类集成算法

为了降低大规模数据谱聚类计算负担,进一步提高聚类的准确性和鲁棒性,提出了一种基于三阶张量的大规模数据谱聚类集成算法。首先,提出一种混合代表最近邻近似方法构造数据间的稀疏亲和子矩阵;然后将稀疏亲和子矩阵表示为二部图,通过图分割的方法得到初步聚类结果;最后,提出三阶张量集成方法,将多个聚类结果进行融合,得到最终的聚类结果。在大规模的真实数据集和合成数据集上验证,相较经典的谱聚类算法、聚类集成算法以及近年来对其改进的算法,该算法表现出更优异的性能。

高校院级平台大型仪器设备年度使用报告的建立及作用研究

大型仪器设备缺少精细化管理以及开放共享不足一直是各高校设备管理中存在的主要问题。为了解决以上问题,通过对学校的分析测试共享平台网络大数据进行统计、归类、分析和总结,建立院级平台各仪器设备的年度使用报告。年度使用报告的建立以每台设备为核心,通过对设备的年度使用情况、每月的使用机时、用户使用情况、维修情况、人才培养、科研成果等进行全面的分析,可及时发现仪器设备在管理流程中的漏洞,制定纠正措施,有助于构建精确的管理流程和策略,提升管理水平,促进仪器开放共享。

大规模图神经网络研究综述

图神经网络凭借其处理非欧氏空间数据及其复杂特征方面的优越性受到了大量的关注,并且被广泛应用于推荐系统、知识图谱、交通道路分析等场景中.面对大规模数据,图结构的不规则性、节点特征的复杂性以及训练样本之间的依赖性对图神经网络模型的计算效率、内存管理以及分布式系统中的通信开销造成了巨大的压力.为应对和缓解以上问题,研究者从应用场景、算法模型、编程框架和硬件结构等多个层面对其进行了优化.本文主要回顾和总结了算法模型及编程框架方面的优化,为读者了解面向大规模数据的图神经网络采样算法以及框架优化相关工作提供帮助,为未来算法-框架协同优化奠定基础.具体来说,本文首先简要介绍图神经网络模型中的消息传递机制,分类介绍常见的图神经网络模型,并分析其在大规模数据训练中面临的困难和挑战;然后对面向大规模数据的图神经网络算法模型进行分类总结和分析,包括基于节点、边和子图的采样算法;接着介绍图神经网络编程框架加速的相关进展,主要包括主流框架的介绍以及优化技术的分类总结和分析;最后对未来面向大规模数据的图神经网络研究进行展望.

大尺度自然地理实体边界智能化提取方法——以大别山为例

为了解决语义明确但空间位置与分布范围并不明确的自然地理实体(PGO)在地图中的分布范围或边界表达问题,提出一种PGO边界的智能化提取方法。首先利用给定的语义词,自动搜索网络地图大数据;接着在顾及PGO的连续空间分布特性基础上,运用空间聚类算法确定PGO的大致范围;然后利用PGO的几何特征(如山地的起伏变化),运用特征识别算法,进一步确定自然实体的分布范围和边界。考虑到此类实体的复杂性,仅以山地(大别山)为例进行实证研究,验证所提方法的有效性。

水利遥感数据规模化加工处理平台框架研究

以卫星遥感为代表的地表信息空天获取手段是支撑新时期智慧水利建设的重要途径。当前,遥感数据获取能力与处理服务能力之间出现了失衡,大量的对地观测卫星遥感数据被获取,而能够支撑水利业务的遥感数据产品即时服务能力和动态更新频次仍然存在瓶颈,“卫星数据量大、处理流程复杂、产品服务不足”的现状长期存在,制约着水利行业遥感应用的质量和效率。本文通过调研当前国内外遥感大数据加工处理进展,分析了面向智慧水利建设的水利遥感数据处理平台需求,设计了一种水利遥感数据规模化加工处理平台的框架,用于海量卫星遥感数据产品全链路自动化处理,以提升水利遥感数据产品处理质量和服务效率。该平台可以实现超过30种多源高分遥感数据全流程处理,通过动态分配GPU和CPU计算缓存,实现卫星遥感数据正射校正、融合、镶嵌、质检以及水利遥感专题信息提取等流程规模化、并行化和自动化处理,生产满足数字孪生流域建设的卫星遥感专题产品,支撑新时期智慧水利建设的遥感深度应用。

SVM样本约简算法研究综述

支持向量机(Support Vector Machine, SVM)是基于统计学习理论和结构风险最小化原则发展起来的一种有监督的机器学习算法,它有效克服了局部最小和维数灾难等问题,具有良好的泛化性能,并被广泛应用于模式识别和人工智能领域。但SVM的学习效率随着训练样本数量的增加而显著降低,对于大规模训练集,采用标准优化方法的传统SVM面临着内存需求过大、执行速度慢,有时甚至无法执行的问题。为了缓解SVM在大规模训练集上存储需求高、训练时间长等问题,学者们提出了SVM样本约简算法。文中首先介绍了SVM理论基础,然后从基于聚类、几何分析、主动学习、增量学习和随机抽样5个方面系统综述了SVM样本约简算法的研究现状,讨论了各种SVM样本约简算法的优缺点,最后总结全文并展望未来。