ATD: Sparsity Models for Forecasting Spatio-Temporal Human Dynamics

ATD:预测时空人类动力学的稀疏模型

基本信息

  • 批准号:
    1737770
  • 负责人:
  • 金额:
    $ 56.45万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-15 至 2021-08-31
  • 项目状态:
    已结题

项目摘要

The US continued achievement at the forefront of science and technology requires a significant investment in new research in information technology to tackle the most challenging problems created by the vast data footprint created by digital recording of human activity. This project develops novel models and methods for forecasting human activity in time and space using sparse, heterogeneous data. The goals are very general and are focused on predicting and filling in missing data. An example of the type of data this project addresses would be a year's worth of geotagged Twitter data from a major city along with other informative geospatial information from that region. This project combines expertise of senior scientists in both Mathematics and Anthropology. The project develops analytical tools for understanding a diverse array of cyber-geospatial-temporal datasets. While focused on basic research, the project has tremendous potential to impact national security. This three-year project trains postdocs, graduate students, and undergraduate researchers. The mentees will be trained in research, in presentation of their work in written and spoken formats, with an emphasis on refereed journal publications and conference presentations. They will also be connected to future employers and will be given career advice throughout the length of their training.The project focuses on information technology at the interface between large-scale cultural, social and behavioral processes and the situational conditions that lead to the expression of specific behaviors. This work extends a general conceptualization of text-based topic modeling to handle diverse collections of data types. The project develops methods to detect situational probabilistic effects through spatially-explicit topic modeling. One goal is to organize situational effects into different categories: (a) relatively stationary (e.g., the spatially discrete, but temporally stable role that the physical airport plays in driving airport related topics), (b) intermittent (e.g., discrete holidays) and (c) ephemeral (e.g., Foursquare). Another goal is temporal forecasting while a third goal is filling in missing information from a latent space. The research approach focuses on algorithms that are flexible enough to extend to a variety of datasets. The work interweaves several very useful models and algorithms for large data including self-exciting point process models for temporal information, soft topic modeling such as nonnegative matrix factorization and latent Dirichlet allocation for linear mixture models of data, hard clustering methods built around total variation minimization on graphs and graph Laplacians, and data fusion methods to combine these ideas in which latent space information is studied for forecasting and filling in missing information.
美国在科学和技术前沿的持续成就需要对信息技术的新研究进行大量投资,以解决人类活动数字记录所产生的巨大数据足迹所带来的最具挑战性的问题。 该项目开发了新的模型和方法,用于使用稀疏,异构数据预测人类活动的时间和空间。这些目标非常笼统,侧重于预测和填补缺失的数据。该项目处理的数据类型的一个例子是来自一个主要城市的一年的地理标记Twitter数据沿着来自该地区的其他信息地理空间信息。该项目结合了数学和人类学高级科学家的专业知识。 该项目开发分析工具,以了解各种各样的网络-地理空间-时间数据集。虽然该项目专注于基础研究,但它具有影响国家安全的巨大潜力。这个为期三年的项目培养博士后,研究生和本科生研究人员。学员将接受研究培训,以书面和口头形式介绍他们的工作,重点是参考期刊出版物和会议演讲。他们还将与未来的雇主建立联系,并将在整个培训期间获得职业建议,该项目侧重于大规模文化、社会和行为过程与导致具体行为表达的情境条件之间的界面上的信息技术。这项工作扩展了基于文本的主题建模的一般概念化,以处理不同的数据类型的集合。该项目开发的方法来检测情景概率的影响,通过空间明确的主题建模。一个目标是将情境效应组织成不同的类别:(a)相对静止(例如,物理机场在驱动机场相关主题中所起的空间离散但时间稳定的作用),(B)间歇性的(例如,离散的假期)和(c)短暂的(例如,Foursquare)。另一个目标是时间预测,而第三个目标是从潜在空间中填充缺失的信息。研究方法侧重于足够灵活的算法,以扩展到各种数据集。这项工作交织了几个非常有用的大数据模型和算法,包括时间信息的自激点过程模型,数据线性混合模型的非负矩阵分解和潜在Dirichlet分配等软主题建模,围绕图和图拉普拉斯算子的总变差最小化构建的硬聚类方法,数据融合方法是将这些思想结合起来,研究潜在的空间信息,用于预测和填补缺失信息。

项目成果

期刊论文数量(32)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Alternative SIAR models for infectious diseases and applications in the study of non-compliance
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
多元时空霍克斯过程与网络重建
  • DOI:
    10.1137/18m1226993
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Yuan, Baichuan;Li, Hao;Bertozzi, Andrea L.;Brantingham, P. Jeffrey;Porter, Mason A.
  • 通讯作者:
    Porter, Mason A.
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
  • DOI:
    10.1080/2330443x.2018.1438940
  • 发表时间:
    2018-02-08
  • 期刊:
  • 影响因子:
    1.6
  • 作者:
    Brantingham, P. Jeffrey;Valasik, Matthew;Mohler, George O.
  • 通讯作者:
    Mohler, George O.
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
  • DOI:
  • 发表时间:
    2020-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Baichuan Yuan;Xiaowei Wang;Jianxin Ma;Chang Zhou;A. Bertozzi;Hongxia Yang
  • 通讯作者:
    Baichuan Yuan;Xiaowei Wang;Jianxin Ma;Chang Zhou;A. Bertozzi;Hongxia Yang
Competitive dominance, gang size and the directionality of gang violence
  • DOI:
    10.1186/s40163-019-0102-3
  • 发表时间:
    2019-08-30
  • 期刊:
  • 影响因子:
    6.1
  • 作者:
    Brantingham, P. Jeffrey;Valasik, Matthew;Tita, George E.
  • 通讯作者:
    Tita, George E.
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Andrea Bertozzi其他文献

Incorporating Texture Features into Optical Flow for Atmospheric Wind Velocity Estimation
将纹理特征纳入光流中进行大气风速估计
Encased Cantilevers and Alternative Scan Algorithms for Ultra-Gantle High Speed Atomic Force Microscopy
  • DOI:
    10.1016/j.bpj.2011.11.3193
  • 发表时间:
    2012-01-31
  • 期刊:
  • 影响因子:
  • 作者:
    Paul Ashby;Dominik Ziegler;Andreas Frank;Sindy Frank;Alex Chen;Travis Meyer;Rodrigo Farnham;Nen Huynh;Ivo Rangelow;Jen-Mei Chang;Andrea Bertozzi
  • 通讯作者:
    Andrea Bertozzi

Andrea Bertozzi的其他文献

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{{ truncateString('Andrea Bertozzi', 18)}}的其他基金

Collaborative Research: RAPID: Rapid computational modeling of wildfires and management with emphasis on human activity
合作研究:RAPID:野火和管理的快速计算建模,重点关注人类活动
  • 批准号:
    2345256
  • 财政年份:
    2023
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
ATD: Active Learning Activity Detection in Multiplex Networks of Geospatial-Cyber-Temporal Data
ATD:地理空间网络时空数据多重网络中的主动学习活动检测
  • 批准号:
    2318817
  • 财政年份:
    2023
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
Collaborative Research: Differential Equations Motivated Multi-Agent Sequential Deep Learning: Algorithms, Theory, and Validation
协作研究:微分方程驱动的多智能体序列深度学习:算法、理论和验证
  • 批准号:
    2152717
  • 财政年份:
    2022
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
RAPID: Analysis of Multiscale Network Models for the Spread of COVID-19
RAPID:针对 COVID-19 传播的多尺度网络模型分析
  • 批准号:
    2027438
  • 财政年份:
    2020
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
FRG: Collaborative Research: Robust, Efficient, and Private Deep Learning Algorithms
FRG:协作研究:稳健、高效、私密的深度学习算法
  • 批准号:
    1952339
  • 财政年份:
    2020
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
ATD: Algorithms for Threat Detection in Knowledge Graphs
ATD:知识图中的威胁检测算法
  • 批准号:
    2027277
  • 财政年份:
    2020
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks
NRT-HDR:建模和理解人类行为:利用从基因到社交网络的数据
  • 批准号:
    1829071
  • 财政年份:
    2018
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
Extreme-scale algorithms for geometric graphical data models in imaging, social and network science
成像、社会和网络科学中几何图形数据模型的超大规模算法
  • 批准号:
    1417674
  • 财政年份:
    2014
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Continuing Grant
Collaborative Research: Modeling, Analysis, and Control of the Spatio-temporal Dynamics of Swarm Robotic Systems
协作研究:群体机器人系统时空动力学的建模、分析和控制
  • 批准号:
    1435709
  • 财政年份:
    2014
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Standard Grant
Particle laden flows - theory, analysis and experiment
颗粒负载流 - 理论、分析和实验
  • 批准号:
    1312543
  • 财政年份:
    2013
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Continuing Grant

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Adaptive Dependent Data Models via Graph-Informed Shrinkage and Sparsity
通过图通知收缩和稀疏性的自适应相关数据模型
  • 批准号:
    2214726
  • 财政年份:
    2022
  • 资助金额:
    $ 56.45万
  • 项目类别:
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Low-rank and sparsity-based models in Magnetic Resonance Imaging (B03)
磁共振成像中的低秩和稀疏模型(B03)
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  • 财政年份:
    2021
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    $ 56.45万
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Time Series Models: Sparsity, Mis-specification and Forecasting
时间序列模型:稀疏性、错误指定和预测
  • 批准号:
    RGPIN-2017-06082
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    2021
  • 资助金额:
    $ 56.45万
  • 项目类别:
    Discovery Grants Program - Individual
CRII: III: Learning Predictive Models with Structured Sparsity: Algorithms and Computations
CRII:III:学习具有结构化稀疏性的预测模型:算法和计算
  • 批准号:
    1948341
  • 财政年份:
    2020
  • 资助金额:
    $ 56.45万
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    Standard Grant
Time Series Models: Sparsity, Mis-specification and Forecasting
时间序列模型:稀疏性、错误指定和预测
  • 批准号:
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Time Series Models: Sparsity, Mis-specification and Forecasting
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    $ 56.45万
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Time Series Models: Sparsity, Mis-specification and Forecasting
时间序列模型:稀疏性、错误指定和预测
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  • 批准号:
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  • 财政年份:
    2015
  • 资助金额:
    $ 56.45万
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    Standard Grant
Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets
合作研究:大型时空数据集贝叶斯推理的层次稀疏诱导高斯过程模型
  • 批准号:
    1513481
  • 财政年份:
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