Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.Comment: Accepted as CIKM2023 Short Pape

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns. In this paper, we develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. We name this framework SCNN, as an acronym of Structured Component-based Neural Network. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. In line with its reverse process, SCNN decouples MTS data into structured and heterogeneous components and then respectively extrapolates the evolution of these components, the dynamics of which are more traceable and predictable than the original MTS. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets. Additionally, we examine SCNN with different configurations and perform in-depth analyses of the properties of SCNN

MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.Comment: Preprint submitted to Artificial Intelligence. arXiv admin note: substantial text overlap with arXiv:2211.1470

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware

Pseudorabies gD protein protects mice and piglets against lethal doses of pseudorabies virus

IntroductionPseudorabies (PR) is a highly contagious viral disease caused by the pseudorabies virus (PRV), which can cause disease in a wide range of domestic and wild animals. Studies have shown that new mutant strains have emerged in pig farms in many regions and that commercial inactivated and live attenuated vaccines are becoming less effective at protecting pigs.MethodsPorcine pseudorabies glycoprotein D (gD) gene (GenBank: QEY95774.1) with hexa-His tag to the C terminus for further purification processes was cloned into the lentiviral expression plasmid pLV-CMV-eGFP by restriction enzyme, the resulting plasmid was designated as pLV-CMV-gD. HEK-293T cells with robust and stable expression of recombinant gD protein was established by infection with recombinant lentivirus vector pLV-CMV-gD. We expressed porcine pseudorabies virus gD protein using HEK-293T cells.ResultsWe describe in this study that individual gD proteins produced by a mammalian cell expression system are well immunogenic and stimulate high levels of PRV-specific and neutralizing antibodies in mice and piglets. All mice and piglets survived lethal doses of PRV, significantly reducing the amount of PRV virus in piglets’ lymph nodes, lungs, spleen, and other tissues. It also significantly reduced the time cycle and amount of viral excretion from piglets to the environment through the nasal and anal cavities.DiscussionThe results suggest that PRV gD protein is expected to be a potential candidate for the preparation of genetically engineered PR vaccines for the prevention of PRV infection and the control of PR epidemics

Seasonal human coronavirus NL63 epidemics in children in Guilin, China, reveal the emergence of a new subgenotype of HCoV-NL63

IntroductionSeasonal human coronavirus NL63 (HCoV-NL63) is a frequently encountered virus linked to mild upper respiratory infections. However, its potential to cause more severe or widespread disease remains an area of concern. This study aimed to investigate a rare localized epidemic of HCoV-NL63-induced respiratory infections among pediatric patients in Guilin, China, and to understand the viral subtype distribution and genetic characteristics.MethodsIn this study, 83 pediatric patients hospitalized with acute respiratory infections and positive for HCoV-NL63 were enrolled. Molecular analysis was conducted to identify the viral subgenotypes and to assess genetic variations in the receptor-binding domain of the spiking protein.ResultsAmong the 83 HCoV-NL63-positive children, three subgenotypes were identified: C4, C3, and B. Notably, 21 cases exhibited a previously unreported subtype, C4. Analysis of the C4 subtype revealed a unique amino acid mutation (I507L) in the receptor-binding domain of the spiking protein, which was also observed in the previously reported C3 genotype. This mutation may suggest potential increases in viral transmissibility and pathogenicity.DiscussionThe findings of this study highlight the rapid mutation dynamics of HCoV-NL63 and its potential for increased virulence and epidemic transmission. The presence of a unique mutation in the C4 subtype, shared with the C3 genotype, raises concerns about the virus’s evolving nature and its potential public health implications. This research contributes valuable insights into the understanding of HCoV-NL63’s epidemiology and pathogenesis, which is crucial for effective disease prevention and control strategies. Future studies are needed to further investigate the biological significance of the observed mutation and its potential impact on the virus’s transmissibility and pathogenicity

From concept to action: a united, holistic and One Health approach to respond to the climate change crisis

It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided