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Path2Vec: A Deep Representation Learning Method for Trajectory Feature Extraction and HYSPLIT Uncertainty Quantification

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21 ago 2024

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Souza de Cursi, E. (2021). Uncertainty quantification in game theory. Chaos, Solitons & Fractals, 143, 110558. https://doi.org/10.1016/j.chaos.2020.110558 Search in Google Scholar

Gal, Y., Koumoutsakos, P., Lanusse, F., et al. (2022). Bayesian uncertainty quantification for machine-learned models in physics. Nature Reviews Physics, 4(9), 573-577. https://doi.org/10.1038/s42254-022-00498-4 Search in Google Scholar

Acar, P. (2021). Recent progress of uncertainty quantification in small-scale materials science. Progress in Materials Science, 117, 100723. https://doi.org/10.1016/j.pmatsci.2020.100723 Search in Google Scholar

Xing, Y., Su, Y., & Ma, W. (2023). Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 0(0), 1-14. https://doi.org/10.1109/tpami.2023.3288028 Search in Google Scholar

Franchi, G., Bursuc, A., Aldea, E., et al. (2024). Encoding the Latent Posterior of Bayesian Neural Networks for Uncertainty Quantification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 0(0), 1-13. doi Search in Google Scholar

Janjić, T., Lukáčová-Medviďová, M., Ruckstuhl, Y., et al. (2023). Comparison of uncertainty quantification methods for cloud simulation. Quarterly Journal of the Royal Meteorological Society, 149(756), 2895-2910. https://doi.org/10.1002/qj.4537 Search in Google Scholar

Rolph, G. D., Stein, A., & Stunder, B. J. B. (2017). Real-time Environmental Applications and Display System: READY. Environmental Modelling and Software, 95, 210-228. https://doi.org/10.1016/j.envsoft.2017.06.025 Search in Google Scholar

Yu, K., Guo, G., Li, J., et al. (2020). Quantum algorithms for similarity measurement based on Euclidean distance. International Journal of Theoretical Physics, 59(10), 3134-3144. https://doi.org/10.1007/s10773-020-04567-1 Search in Google Scholar

Zhang, H., Yang, D., Li, J., et al. (2022). Dynamic Time Warping Under Product Quantization, With Applications to Time-Series Data Similarity Search. IEEE Internet of Things Journal, 9(14), 11814-11826. https://doi.org/10.1109/jiot.2021.3132017 Search in Google Scholar

Chen, L., Özsu, M. T., & Oria, V. (2005). Robust and fast similarity search for moving object trajectories. null, 0(0), 0-0. https://doi.org/10.1145/1066157.1066213 Search in Google Scholar

Li, R., Deka, J. K., & Deka, K. N. (2023). An algorithm for the longest common subsequence and substring problem. Journal of Mathematics and Informatics, 25(0), 77-81. https://doi.org/10.22457/jmi.v25a08231 Search in Google Scholar

Hung, C. C., Peng, W. C., & Lee, W. C. (2015). Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. The VLDB Journal, 24(2), 169-192. Search in Google Scholar

Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306 Search in Google Scholar

Ren, Z. (2022). The advance of generative model and variational autoencoder. In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) (pp. 0-0). https://doi.org/10.1109/tocs56154.2022.10016057 Search in Google Scholar

Bustos, J. P., Donoso, F., Guesalaga, A., et al. (2007). Matching radar and satellite images for ship trajectory estimation using the Hausdorff distance. IET Radar Sonar & Navigation, 1(1), 50-58. Search in Google Scholar

Di, Y., Chao, Z., Zhu, Z., et al. (2017). Trajectory clustering via deep representation learning. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. Search in Google Scholar

MacQueen, J. (1965). Some methods for classification and analysis of multivariate observations. In Proceedings of the Berkeley Symposium on Mathematical Statistics & Probability (pp. 281-297). Search in Google Scholar

Kobak, D., & Linderman, G. C. (2021). Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nature Biotechnology, 39(2), 156-157. https://doi.org/10.1038/s41587-020-00809-z Search in Google Scholar