ATD: Nonparametric Testing and Fast Computing Methods for Spatiotemporal Models with Applications to Threat Detection

ATD：时空模型的非参数测试和快速计算方法及其在威胁检测中的应用

• 批准号: 1925066
• 负责人: Ping Ma
• 金额: $ 39.99万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925066&HistoricalAwards=false
• 关键词: ATD; Nonparametric; Testing; Fast; Computing

The study of human dynamics aims to understand human behaviors using analytical models. It has received substantial attention in the security and defense area not only for its potential in detecting human anomalies but also for its capability in containing potential disastrous damages and mental horror in human society. The recent development in social media has revolutionized daily life and inaugurated a new era in human dynamics study. It has been demonstrated that certain human behavior can be modeled quantitatively using proxy tools. Social media data and wearable device data are two major sources of proxies that can be used to understand spatiotemporal trends from which we can identify abnormal patterns in human dynamics. The abnormal patterns can be used as an indicator for disasters. Although social media data and wearable device data contain a wealth of information to understanding human behavior, they are constantly evolving as users generate new content or as new routes are introduced. Classic statistical models are not enough to model constantly-evolving spatial and temporal trends. More importantly, the computational cost for a spatiotemporal model is extremely high, which poses a significant challenge for real-time analysis of human dynamics data. To overcome these challenges, we propose novel statistical theory, methods, algorithms for efficiently analyzing local and global trends of a spatiotemporal model. The project will train students to participate in cutting-edge and interdisciplinary big data research. Despite the urgent need, statistical tools for human dynamic studies are still lacking. In this project, we aim to develop spatiotemporal models to understand the inherent spatial/temporal trends of social media and personal wearables data. The key challenge for analyzing large-scale spatiotemporal data is the super-large sample size. For example, every day Twitter takes in hundreds of million tweets. Many of these tweets are recorded in public streams. We develop scalable computational methods to surmount the challenge. The fast, in-network computing principles and middleware developed in this project are fundamental and indispensable tools for "big data" computation and autonomous systems. The proposed framework can be used to discover unusual events in any super-large dynamic data set and inspire a new line of research in big data analytics. The proposed statistical tools are widely applicable in science, engineering, and humanities. The research will be conducted in collaboration with experts in both geography and statistics; consequently, the proposed work will be informed by rapid empirical feedback and can incorporate modern advances in spatiotemporal modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类动力学的研究旨在使用分析模型来理解人类行为。它在安全和防御领域受到了极大的关注，不仅因为它在检测人类异常方面的潜力，而且因为它在遏制人类社会潜在的灾难性损害和精神恐怖方面的能力。社交媒体的最新发展彻底改变了日常生活，开创了人类动力学研究的新时代。 已经证明，可以使用代理工具对某些人类行为进行定量建模。社交媒体数据和可穿戴设备数据是代理的两个主要来源，可用于了解时空趋势，从中我们可以识别人类动态中的异常模式。异常模式可以用作灾害的指示器。虽然社交媒体数据和可穿戴设备数据包含了丰富的信息来理解人类行为，但随着用户生成新内容或引入新路线，它们也在不断发展。经典的统计模型不足以模拟不断演变的空间和时间趋势。更重要的是，时空模型的计算成本非常高，这对人体动力学数据的实时分析提出了重大挑战。为了克服这些挑战，我们提出了新的统计理论，方法，算法，有效地分析局部和全局趋势的时空模型。该项目将培养学生参与尖端和跨学科的大数据研究。 尽管迫切需要，但仍然缺乏用于人类动态研究的统计工具。在这个项目中，我们的目标是开发时空模型，以了解社交媒体和个人可穿戴设备数据的内在空间/时间趋势。分析大规模时空数据的关键挑战是超大样本容量。例如，Twitter每天都会收到数亿条推文。这些推文中的许多都记录在公共流中。 我们开发可扩展的计算方法来克服挑战。该项目中开发的快速网络计算原理和中间件是“大数据”计算和自治系统的基本和不可或缺的工具。该框架可用于发现任何超大型动态数据集中的异常事件，并激发大数据分析的新研究方向。所提出的统计工具广泛适用于科学，工程和人文学科。该研究将与地理学和统计学专家合作进行;因此，拟议的工作将通过快速的经验反馈提供信息，并可以纳入时空建模的现代进步。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Communities and Brokers: How the Transnational Advocacy Network Simultaneously Provides Social Power and Exacerbates Global Inequalities

社区和经纪人：跨国倡导网络如何同时提供社会力量并加剧全球不平等

• DOI: 10.1093/isq/sqab037
• 发表时间: 2021
• 期刊: International Studies Quarterly
• 影响因子: 2.6
• 作者: Cheng, Huimin;Wang, Ye;Ma, Ping;Murdie, Amanda
• 通讯作者: Murdie, Amanda

Projection‐based techniques for high‐dimensional optimal transport problems

• DOI: 10.1002/wics.1587
• 发表时间: 2022-05
• 期刊: Wiley Interdisciplinary Reviews: Computational Statistics
• 作者: Jingyi Zhang;Ping Ma;Wenxuan Zhong;Cheng Meng-

MINIMAX NONPARAMETRIC MULTI-SAMPLE TEST UNDER SMOOTHING

• DOI: 10.5705/ss.202022.0141
• 发表时间: 2024-10-01
• 期刊: STATISTICA SINICA
• 影响因子: 1.4
• 作者: Xing，Xin;Shang，Zuofeng;Liu，Jun S.
• 通讯作者: Liu，Jun S.

Model Checking in Large-Scale Dataset via Structure-Adaptive-Sampling

通过结构自适应采样对大规模数据集进行模型检查

• DOI: 10.5705/ss.202020.0303
• 发表时间: 2023
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Han, Yixin;Ma, Ping;Ren, Haojie;Wang, Zhaojun
• 通讯作者: Wang, Zhaojun

B-scaling: A novel nonparametric data fusion method

• DOI: 10.1214/21-aoas1537
• 发表时间: 2021-09
• 期刊: The Annals of Applied Statistics
• 作者: Yiwen Liu;Xiaoxiao Sun;Wenxuan Zhong;Bing Li