Reliable forecasting of traffic flow requires efficient modeling of traffic data. Indeed, different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different methods to capture traffic networks' complex underlying spatial-temporal relations. However, given the heterogeneity of traffic data, consistently capturing both spatial and temporal dependencies presents a significant challenge. Also, as more and more sophisticated methods are being proposed, models are increasingly becoming memory-heavy and, thus, unsuitable for low-powered devices. To this end, we propose Spatio-Temporal Lightweight Graph GRU, namely STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Specifically, our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks using memory-augmented attention and gating mechanisms in a continuously synchronized manner. Moreover, instead of employing separate temporal and spatial components, we show that our memory module and gated unit can successfully learn the spatial-temporal dependencies with reduced memory usage and fewer parameters. Extensive experimental results on three real-world public traffic datasets demonstrate that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Our code is available at https://github.com/Kishor-Bhaumik/STLGRU
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…
Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation
Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）；Brown University（布朗大学）；Northeastern University（东北大学）；Rochester Institute of Technology（罗切斯特理工学院）
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
COSYS-GRETTIA, Gustave Eiffel University, 77420 France（COSYS-GRETTIA，巴黎-伊夫林大学）；ENTPE, University of Lyon（ENTPE，里昂大学）；the LICIT-ECO7 University Gustave Eiffel, France（LICIT-ECO7 巴黎-伊夫林大学）
Hierarchical Spatio-Temporal Attention Network with Adaptive Risk-Aware Decision for Forward Collision Warning in Complex Scenarios
organization= School of Automation \& School of Industrial Internet, Chongqing University of Posts；organization= Platform Technology Development Department, AVATR Technology Co. LTD. , city= Chongqing , postcode= 400000 , country= China；organization= School of Vehicle；Mobility, Tsinghua University , city= Beijing , postcode= 100084 , country= China
BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
The Hong Kong University of Science（香港理工大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究所）；Ginlong Technologies Co., Ltd.（金龙科技有限公司）；Wuhan University（武汉大学）；Zhejiang Key Laboratory of Industrial Intelligence（浙江工业智能关键实验室）
针对多建筑能源负荷预测中的空间依赖建模问题，提出 BuildSTG 时空图神经网络方法，结合图构建、注意力图卷积与图结构解释，并在 Building Data Genome Project 2 上验证其优于多种基线。
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）
Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算与人工智能学院）；SWJTU-Leeds Joint School, Southwest Jiaotong University（西南交通大学-利兹学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
GCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding
Faculty of frontier sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；Doumaomao NetTech Co., Ltd.（杜马莫莫网络技术有限公司）；University of Melbourne（墨尔本大学）；The University of New South Wales（新南威尔士大学）
A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch
Center for Automotive Research (CAR), The Ohio State University（汽车研究中心（CAR），俄亥俄州立大学）；Informatics Institute, Istanbul Technical University（信息学院，伊斯坦布尔技术大学）；Computer Engineering Department, Konkuk University（计算机工程系，韩国康 kuk 大学）；Computer Science and Information Technology Department, Maulana Azad National Urdu University（计算机科学与信息技术系，莫拉尔·阿扎德国家乌尔都语大学）
