Collaborative Research: ATD: Robust, Accurate and Efficient Graph-Structured RNN for Spatio-Temporal Forecasting and Anomaly Detection

合作研究：ATD：用于时空预测和异常检测的鲁棒、准确和高效的图结构 RNN

• 批准号: 1924548
• 负责人: Jack Xin
• 金额: $ 13万
• 依托单位: University of California-Irvine
• 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: 2019-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924548&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Robust; Accurate

The project aims to develop robust, efficient, and transferrable deep learning algorithms for prediction and anomaly detection in human spatio-temporal dynamics. This will be a fundamental step in providing reliable and speedy decision support for mitigating infectious diseases and countering threats in a time varying and spatially complex environment. The project shall advance recent computational tools (deep neural networks) in adversarial conditions and on resource limited (low cost, low energy) platform, thereby contribute to information technology in adversarial learning, mobile computing and effective decision making. A broad range of applications include threat detection and prediction for traffic and public transportation networks, security and privacy critical data analysis and prediction, threat detection and error correction for hydraulic, electrical and nuclear power systems. The approaches to be used involve novel techniques in high dimensional non-smooth non-convex optimization and graph representation. Specifically, the project shall study (1) multi-scale graph-structured recurrent neural networks for spatio-temporal data modeling, prediction and anomaly detection; (2) adversarially robust, accurate, and transferable deep learning algorithms based on advection-diffusion equations; (3) efficient quantization algorithms under adversarial conditions to reduce the latency of deep networks. The projects shall train a diverse body of graduate and undergraduate students at the Irvine and Los Angeles campuses of University of California through collaborative education and research activities in applied mathematics, computer science, data science and social science.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.

该项目旨在开发强大，高效和可转移的深度学习算法，用于人类时空动态的预测和异常检测。这将是在时变和空间复杂的环境中为减轻传染病和应对威胁提供可靠和快速决策支持的基本步骤。 该项目将在对抗条件下和资源有限（低成本，低能耗）的平台上推进最新的计算工具（深度神经网络），从而为对抗学习，移动的计算和有效决策中的信息技术做出贡献。广泛的应用包括交通和公共交通网络的威胁检测和预测、安全和隐私关键数据分析和预测、水力、电力和核电系统的威胁检测和错误纠正。 所使用的方法涉及高维非光滑非凸优化和图形表示的新技术。具体而言，该项目将研究（1）用于时空数据建模，预测和异常检测的多尺度图结构递归神经网络;（2）基于对流扩散方程的对抗性鲁棒，准确和可转移的深度学习算法;（3）对抗条件下的有效量化算法，以减少深度网络的延迟。该项目将通过应用数学、计算机科学、数据科学和社会科学方面的合作教育和研究活动，在加州大学欧文分校和洛杉矶校区培养多样化的研究生和本科生。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19

基于循环神经网络和微分方程的时空传染病模型及其在 COVID-19 中的应用

• DOI: 10.1101/2020.07.20.20158568
• 发表时间: 2020
• 期刊: of the 12th International Conference on Knowledge Discovery and Information Retrieval
• 作者: Li, Zhijian;Zheng, Yunling;Xin, Jack;Zhou, Guofa
• 通讯作者: Zhou, Guofa

Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization

• DOI: 10.3389/fams.2020.529564
• 发表时间: 2021-02
• 作者: Kevin Bui;Fredrick Park;Shuai Zhang;Y. Qi;J. Xin

Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction

• DOI: 10.1109/icassp43922.2022.9747394
• 发表时间: 2022-01
• 期刊: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Yunling Zheng;Carson Hu;Guang Lin;Meng Yue;Bao Wang;Jack Xin

A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19

基于时空图的混合传染病模型及其在 COVID-19 中的应用

• DOI: 10.5220/0010349003570364
• 发表时间: 2021
• 期刊: The 10th International Conference on Pattern Recognition Applications and Method
• 作者: Zheng, Yunling;Li, Zhijian;Xin, Jack;Zhou, Guofa
• 通讯作者: Zhou, Guofa

Computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks

通过超分辨率深度神经网络的自适应基础学习计算残余扩散率

• DOI: 10.1007/978-3-030-38364-0_25
• 发表时间: 2020
• 期刊: Applied Mathematics and Applications
• 作者: Lyu, Jiancheng;Xin, Jack;Yu, Yifeng
• 通讯作者: Yu, Yifeng