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2. FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting

Author

Zhang, Junhao, Tang, Junjie, Jin, Juncheng, and Qu, Zehui

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many sensors can cause a vector with a dimension greater than 800, which is difficult to process without information loss. In addition, these methods design complex mechanisms to capture spatial dependencies in MTS, resulting in slow forecasting speed. To solve the abovementioned problems, we propose a Fast Pure Transformer Network (FPTN) in this paper. First, the traffic flow data are divided into sequences along the sensor dimension instead of the time dimension. Then, to adequately represent complex spatio-temporal correlations, Three types of embeddings are proposed for projecting these vectors into a suitable vector space. After that, to capture the complex spatio-temporal correlations simultaneously in these vectors, we utilize Transformer encoder and stack it with several layers. Extensive experiments are conducted with 4 real-world datasets and 13 baselines, which demonstrate that FPTN outperforms the state-of-the-art on two metrics. Meanwhile, the computational time of FPTN spent is less than a quarter of other state-of-the-art Transformer-based models spent, and the requirements for computing resources are significantly reduced.

Published
2023

11. Major Histocompatibility Complex Class I (FLA-E*01801) Molecular Structure in Domestic Cats Demonstrates Species-Specific Characteristics in Presenting Viral Antigen Peptides

Author

Liang, Ruiying, primary, Sun, Yaping, additional, Liu, Yanjie, additional, Wang, Junya, additional, Wu, Yanan, additional, Li, Zibin, additional, Ma, Lizhen, additional, Zhang, Nan, additional, Zhang, Lijie, additional, Wei, Xiaohui, additional, Qu, Zehui, additional, Zhang, Nianzhi, additional, and Xia, Chun, additional

Published
2018
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14. Cellular miR-130b inhibits replication of porcine reproductive and respiratory syndrome virus in vitro and in vivo.

Author

Gao, Fei, Jiang, Yifeng, Tong, Guangzhi, Li, Liwei, Yu, Lingxue, Zhou, Yanjun, Zheng, Hao, Tong, Wu, Yang, Shen, Xia, Tianqi, and Qu, Zehui

Subjects
MICRORNA, PORCINE reproductive & respiratory syndrome, IN vitro studies, VIRAL replication, GENE expression
Abstract

MicroRNAs (miRNAs) can impact viral infections by binding to sequences with partial complementarity on viral RNA transcripts, usually resulting in the repression of virus replication. In the present study, we identified a potential binding site for miR-130 in the 5′ untranslated region (bps 155-162) of the porcine reproductive and respiratory syndrome virus (PRRSV) genome. We found that the delivery of multiple miR-130 family mimics, especially miR-130b, resulted in inhibition of PRRSV replication in vitro. miR-130 was effective in inhibiting the replication of multiple type 2 PRRSV strains, but not against vSHE, a classical type 1 strain. miR-130 over-expression did not induce IFN-α or TNF-α expression in either uninfected or PRRSV-infected porcine alveolar macrophages. Results from luciferase reporter assays indicated that miR-130 directly targeted the PRRSV 5′ UTR. Intranasal inoculation of piglets with miR-130b exhibited antiviral activity in vivo and partially protected piglets from an otherwise lethal challenge with HP-PRRSV strain vJX143. Overall, these results demonstrate the importance of the miR-130 family in modulating PRRSV replication and also provide a scientific basis for using cellular miRNAs in anti-PRRSV therapies. [ABSTRACT FROM AUTHOR]

Published
2015
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