no code implementations • 21 Jun 2023 • Jiangtao Liu, Yuchen Bian, Chaopeng Shen
While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model.
no code implementations • 27 Jan 2023 • Jiangtao Liu, Kai Wu, Tao Su, J. Andrew Zhang
Joint radar and communications (JRC) can realize two radio frequency (RF) functions using one set of resources, greatly saving hardware, energy and spectrum for wireless systems needing both functions.
no code implementations • 30 Mar 2022 • Yingtian Tang, Jiangtao Liu, Cheng Zhou, Tingguang Li
Motion style transfer is highly desired for motion generation systems for gaming.
no code implementations • 28 Mar 2022 • Dapeng Feng, Jiangtao Liu, Kathryn Lawson, Chaopeng Shen
Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0. 732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0. 748 from a state-of-the-art LSTM model with the same setup.
no code implementations • 30 Jul 2020 • Wen-Ping Tsai, Dapeng Feng, Ming Pan, Hylke Beck, Kathryn Lawson, Yuan Yang, Jiangtao Liu, Chaopeng Shen
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.