With the increasing usage of the electronic limit order book (LOB) in modern financial markets, high-frequency algorithmic trading has captured over 70 percent of the whole trading volume in various financial markets.

Deep generative models are a category of machine learning models that utilizes deep neural networks to model data distributions and generate new samples.

Early stopping is a widely-used regularization technique to avoid overfitting in iterative algorithms.

Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions.

Spatial transcriptomics (ST) is a groundbreaking method that allows scientists to measure gene activity in a tissue sample and retain spatial information. However, most spatial ST technologies are limited by their resolution.

In the Euclidean setting, the proximal gradient method and its accelerated variants are a class of efficient algorithms for optimization problems with decomposable objective.

Black holes are predicted by Einstein's theory of general relativity, and now we have ample observational evidence for their existence.