Dynamic Quality-of-Service (QoS) data capturing temporal variations in user-service interactions, are essential source for service selection and user behavior understanding. Approaches based on Latent Feature Analysis (LFA) have shown to be beneficial for discovering effective temporal patterns in QoS data. However, existing methods cannot well model the spatiality and temporality implied in dynamic interactions in a unified form, causing abundant accuracy loss for missing QoS estimation. To address the problem, this paper presents a novel Graph Convolutional Networks (GCNs)-based dynamic QoS estimator namely Spatiotemporal Coupling GCN (SCG) model with the three-fold ideas as below. First, SCG builds its dynamic graph convolution rules by incorporating generalized tensor product framework, for unified modeling of spatial and temporal patterns. Second, SCG combines the heterogeneous GCN layer with tensor factorization, for effective representation learning on bipartite user-service graphs. Third, it further simplifies the dynamic GCN structure to lower the training difficulties. Extensive experiments have been conducted on two large-scale widely-adopted QoS datasets describing throughput and response time. The results demonstrate that SCG realizes higher QoS estimation accuracy compared with the state-of-the-arts, illustrating it can learn powerful representations to users and cloud services.
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；University of Virginia（弗吉尼亚大学）；Department of Engineering Systems and Environment, University of Virginia（工程系统与环境系，弗吉尼亚大学）；Department of Mechanical and Aerospace Engineering, University of Virginia（机械与航空航天工程系，弗吉尼亚大学）；University of Iowa（爱荷华大学）
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
The University of Edinburgh（爱丁堡大学）；University of Cambridge（剑桥大学）；Central European University（中欧大学）；Obuda University（欧布达大学）；Corvinus University of Budapest（布达佩斯考文纽斯大学）
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Southwest University（西南大学）
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Department of Mathematics and Systems Engineering（数学与系统工程系）；Florida Institute of Technology（佛罗里达理工学院）；Department of Mathematics（数学系）；Embry-Riddle Aeronautical University（埃姆布里-瑞德尔航空大学）；Department of Mechanical and Civil Engineering（机械与土木工程系）
Adaptive AI Research Group, University of Lübeck, Germany（适应性人工智能研究组，吕贝克大学）；Neuro-Cognitive Modeling Group, University of Tübingen, Germany（神经认知建模组，图宾根大学）
Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks
School of Information Science and Engineering, East China University of Science and Technology（信息科学与工程学院，东华大学）；School of Computer Science and Technology, Changsha University of Science and Technology（计算机科学与技术学院，长沙理工大学）
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
School of Information Technology, University of Cincinnati（辛辛那提大学信息技术学院）；School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉法叶分校计算与信息学院）
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Lynxi Technologies Co., Ltd.（灵汐科技有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）
基于Leaky Integrate and Fire（LIF）模型的脉冲神经网络（SNNs）已被应用于能量有效的时间和时空处理任务。由于生物合理的神经元动力学和简单性，LIF-SNN受益于事件驱动处理，然而，通常面临性能降低的尴尬。这可能是因为在LIF-SNN中，神经元通过尖峰信号传输信息。为了解决这个问题，在这项工作中，我们提出了一个Leaky Integrate and Analog Fire（LIAF）神经元模型，以便模拟值可以在神经元之间传输，并在此基础上构建了一个称为LIAF-Net的深度网络，以实现高效的时空处理。在时间域中，LIAF遵循传统的LIF动力学以保持其时间处理能力。在空间域中，LIAF能够通过卷积集成或全连接集成来集成空间信息。作为一个时空层，LIAF也可以与传统的人工神经网络（ANN）层联合使用。实验结果表明，LIAF-Net在bAbI问答（QA）任务上实现了与GRU和长短期记忆（LSTM）相当的性能，并在时空动态视觉传感器（DVS）数据集上实现了最先进的性能，包括MNIST-DVS，CIFAR 10-DVS和DVS 128 Gesture，与LSTM、GRU、卷积LSTM（ConvLSTM）或3D卷积（Conv 3D）构建的传统网络相比，突触权重和计算开销要少得多。与传统的LIF-SNN相比，LIAF-Net在所有这些实验中也显示出显着的准确性增益。总之…
Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting
Charles F. Dolan School of Business, Fairfield University（Fairfield大学商学院）；Department of Industrial & Systems Engineering, The University of Tennessee（田纳西大学工业与系统工程系）
P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution
Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, USA（工业与系统工程系，田纳西大学， Knoxville，TN，美国）；Charles F. Dolan School of Business, Fairfield University, Fairfield, USA（查尔斯·F·多兰商学院，费尔菲尔德大学， Fairfield，美国）；Department of Electrical Engineering & Computer Science, The University of Tennessee, Knoxville, TN, USA（电气工程与计算机科学系，田纳西大学， Knoxville，TN，美国）
A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks
Asia University（亚洲大学）；China Medical University Hospital, China Medical University（中国医药大学附设医院，中国医药大学）；Universitas Muhammadiyah Yogyakarta（穆罕默迪亚日惹大学）；Western Norway University of Applied Sciences（挪威西部应用科学大学）
Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；University Hospital Carl Gustav Carus（卡尔·古斯塔夫·卡鲁斯大学医院）；TU Dresden（德累斯顿工业大学）；Centre for Tactile Internet with Human-in-the-Loop (CeTI)（人在环触觉互联网中心）
Learning from Videos with Deep Convolutional LSTM Networks
University of Illinois（伊利诺伊大学）
本文探讨了使用卷积LSTM来同时学习视频中的空间和时间信息。卷积LSTM的深度网络允许模型在数据的所有空间尺度上访问整个时间信息范围。我们描述了我们的实验，涉及卷积LSTM唇读，证明该模型能够选择性地选择哪些时空尺度是最相关的特定数据集。所提出的深度架构在时空特征发挥重要作用的其他应用中也有希望，而不必专门针对问题中存在的特定时空特征来设计网络。对于Lip Reading in the Wild（LRW）数据集，我们的模型略优于以前的最新技术水平（83.4% vs. 83.0%），并且当模型在Lip Reading Sentences（LRS2）数据集上进行预训练时，将新的最新技术水平设置为85.2%。
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
School of Data Science（数据科学学院）；University of Virginia（弗吉尼亚大学）；Department of Astronomy（天文学系）；Department of Mechanical Engineering（机械工程系）；University of Iowa（爱荷华大学）；School of Mechanical Engineering（机械工程学院）；Kyungpook National University（庆北国立大学）；Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）
Dynamic Causal Graph Convolutional Network for Traffic Prediction
Tsinghua University（清华大学）；University of Cologne（科隆大学）；SenseTime Research（商汤科技研究院）；The Shanghai AI Laboratory（上海人工智能实验室）；Qing Yuan Research Institute of Shanghai Jiao Tong University（上海交通大学清源研究院）
