Shock waves caused by earthquakes can be devastating. Generating realistic earthquake-caused ground motion waveforms help reducing losses in lives and properties, yet generative models for the task tend to generate subpar waveforms. We present High-fidelity Earthquake Groundmotion Generation System (HEGGS) and demonstrate its superior performance using earthquakes from North American, East Asian, and European regions. HEGGS exploits the intrinsic characteristics of earthquake dataset and learns the waveforms using an end-to-end differentiable generator containing conditional latent diffusion model and hi-fidelity waveform construction model. We show the learning efficiency of HEGGS by training it on a single GPU machine and validate its performance using earthquake databases from North America, East Asia, and Europe, using diverse criteria from waveform generation tasks and seismology. Once trained, HEGGS can generate three dimensional E-N-Z seismic waveforms with accurate P/S phase arrivals, envelope correlation, signal-to-noise ratio, GMPE analysis, frequency content analysis, and section plot analysis.

Gradient-descent-based optimizers are prone to slowdowns in training deep learning models, as stationary points are ubiquitous in the loss landscape of most neural networks. We present an intuitive concept of bypassing the stationary points and realize the concept into a novel method designed to actively rescue optimizers from slowdowns encountered in neural network training. The method, bypass pipeline, revitalizes the optimizer by extending the model space and later contracts the model back to its original space with function-preserving algebraic constraints. We implement the method into the bypass algorithm, verify that the algorithm shows theoretically expected behaviors of bypassing, and demonstrate its empirical benefit in regression and classification benchmarks. Bypass algorithm is highly practical, as it is computationally efficient and compatible with other improvements of first-order optimizers. In addition, bypassing for neural networks leads to new theoretical research such as model-specific bypassing and neural architecture search (NAS).

We introduce a new type of problems for math word problem (MWP) solvers, named Noun-MWPs, whose answer is a non-numerical string containing a noun from the problem text. We present a novel method to empower existing MWP solvers to handle Noun-MWPs, and apply the method on Expression-Pointer Transformer (EPT). Our model, N-EPT, solves Noun-MWPs significantly better than other models, and at the same time, solves conventional MWPs as well. Solving Noun-MWPs may lead to bridging MWP solvers and traditional question-answering NLP models.