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1. cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction

   https://doi.org/10.1109/icdm50108.2020.00187  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Luo, Jun (November 2020, 2020 IEEE International Conference on Data Mining (ICDM))

   null (Ed.)

   Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this paper, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: 1) cST-ML captures the dynamics of traffic prediction tasks using variational inference; 2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic related features; 3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models. 

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2. Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach

   https://doi.org/10.1145/3474837  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Luo, Jun; Zhang, Zhi-Li (April 2022, ACM Transactions on Intelligent Systems and Technology)

   Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented. 

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3. Changes in Prevalence but Not Hosts of Antibiotic Resistance Genes during the COVID-19 Pandemic versus Prepandemic in Wastewater Influent

   https://doi.org/10.1021/acsestwater.3c00384  

   Deshpande, Aishwarya S; Ehasz, Genevieve; Eramo, Alessia; Fahrenfeld, Nicole (November 2023, ACS ES&T Water)

   The COVID-19 pandemic offered a unique opportunity to study shifts in environmental antibiotic resistance that could be associated with the changes in disinfectant and/or antibiotic usage patterns, coinfections, or other behaviors. The aim of this study was to document temporal changes (pre-, early-, versus later-pandemic) in antibiotic resistance genes (ARGs), ARG hosts, biomarkers of potential coinfections, and the total microbiome in municipal wastewater influent from one separate sanitary and one combined sewer system. The 16S rRNA gene copy normalized concentration of qacE was higher in early- than prepandemic samples, and sul1 and tet(G) were higher in early- than later-pandemic samples. Metagenomics revealed significant changes in the abundance of the macrolide and sulfonamide ARG classes. COVID-19 cases positively correlated with the disinfectants/antiseptics group of ARGs and negatively correlated with the sulfonamide and aminoglycoside resistance classes. Discussion is provided regarding the correspondence of these observations with antibiotic prescription pattern changes during the study period. Putative waterborne pathogens were identified, which is of potential interest for understanding the prevalence of community coinfections. No changes in host-ARG associations were observed. Overall, the results of this study may help in understanding the impact of the pandemic and/or lack thereof on another public health crisis: antibiotic resistance. 

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4. MetaTP: Traffic Prediction with Unevenly-Distributed Road Sensing Data via Fast Adaptation

   https://doi.org/10.1145/3478083  

   Zhong, Weida; Suo, Qiuling; Gupta, Abhishek; Jia, Xiaowei; Qiao, Chunming; Su, Lu (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework. 

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5. HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

   https://doi.org/10.1609/aaai.v36i4.20338  

   Wang, Chenyu; Lin, Zongyu; Yang, Xiaochen; Sun, Jiao; Yue, Mingxuan; Shahabi, Cyrus (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN. 

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