Probing the limit of hydrologic predictability with the Transformer network

For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of daily hydrograph metrics on known, comparable benchmarks. Outside of hydrology, Transformers have now become the model of choice for sequential prediction tasks, making it a curious architecture to investigate. Here, we first show that a vanilla Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS dataset, and lagged especially for the high-flow metrics due to short-term processes. However, a recurrence-free variant of Transformer can obtain mixed comparisons with LSTM, producing the same Kling-Gupta efficiency coefficient (KGE), along with other metrics. The lack of advantages for the Transformer is linked to the Markovian nature of the hydrologic prediction problem. Similar to LSTM, the Transformer can also merge multiple forcing dataset to improve model performance. 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. As a non-recurrent model, the Transformer may bear scale advantages for learning from bigger datasets and storing knowledge. This work serves as a reference point for future modifications of the model.

Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment

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. Frequency-hopping (FH) MIMO radar is a popular candidate for JRC, as the achieved communication symbol rate can greatly exceed radar pulse repetition frequency. However, practical transceiver imperfections can fail many existing theoretical designs. In this work, we unveil for the first time the non-trivial impact of hardware imperfections on FH-MIMO JRC and analytically model the impact. We also design new waveforms and, accordingly, develop a low-complexity algorithm to jointly estimate the hardware imperfections of unsynchronized receiver. Moreover, employing low-cost software-defined radios and commercial off-the-shelf (COTS) products, we build the first FH-MIMO JRC experiment platform with radar and communications simultaneously validated over the air. Corroborated by simulation and experiment results, the proposed designs achieves high performances for both radar and communications.

Online Motion Style Transfer for Interactive Character Control

Motion style transfer is highly desired for motion generation systems for gaming. Compared to its offline counterpart, the research on online motion style transfer under interactive control is limited. In this work, we propose an end-to-end neural network that can generate motions with different styles and transfer motion styles in real-time under user control. Our approach eliminates the use of handcrafted phase features, and could be easily trained and directly deployed in game systems. In the experiment part, we evaluate our approach from three aspects that are essential for industrial game design: accuracy, flexibility, and variety, and our model performs a satisfying result.

Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly-insurmountable performance in modeling rainfall-runoff and other geoscientific variables, yet they cannot predict unobserved physical variables and remain challenging to interpret. Here we show that differentiable, learnable, process-based models (called {\delta} models here) can approach the performance level of LSTM for the intensively-observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, replace, or enhance the process-based model modules. Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0.715 for 671 basins across the USA for a particular forcing data, compared to 0.72 from a state-of-the-art LSTM model with the same setup. Meanwhile, the resulting learnable process-based models can be evaluated (and later, to be trained) by multiple sources of observations, e.g., groundwater storage, evapotranspiration, surface runoff, and baseflow. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

Robust data analysis and imaging with computational ghost imaging

Nowadays the world has entered into the digital age, in which the data analysis and visualization have become more and more important. In analogy to imaging the real object, we demonstrate that the computational ghost imaging can image the digital data to show their characteristics, such as periodicity. Furthermore, our experimental results show that the use of optical imaging methods to analyse data exhibits unique advantages, especially in anti-interference. The data analysis with computational ghost imaging can be well performed against strong noise, random amplitude and phase changes in the binarized signals. Such robust data data analysis and imaging has an important application prospect in big data analysis, meteorology, astronomy, economics and many other fields.

From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data

The behaviors and skills of models in many geoscientific domains strongly depend on spatially varying parameters that lack direct observations and must be determined by calibration. Calibration, which solves inverse problems, is a classical but inefficient and stochasticity-ridden approach to reconcile models and observations. Using a widely applied hydrologic model and soil moisture observations as a case study, here we propose a novel, forward-mapping parameter learning (fPL) framework. Whereas evolutionary algorithm (EA)-based calibration solves inversion problems one by one, fPL solves a pattern recognition problem and learns a more robust, universal mapping. fPL can save orders-of-magnitude computational time compared to EA-based calibration, while, surprisingly, producing equivalent ending skill metrics. With more training data, fPL learned across sites and showed super-convergence, scaling much more favorably. Moreover, a more important benefit emerged: fPL produced spatially-coherent parameters in better agreement with physical processes. As a result, it demonstrated better results for out-of-training-set locations and uncalibrated variables. Compared to purely data-driven models, fPL can output unobserved variables, in this case simulated evapotranspiration, which agrees better with satellite-based estimates than the comparison EA. The deep-learning-powered fPL frameworks can be uniformly applied to myriad other geoscientific models. We contend that a paradigm shift from inverse parameter calibration to parameter learning will greatly propel various geoscientific domains.