Abstract: Deep neural networks (DNNs) have been applied to address electromagnetic inverse scattering problems (ISPs) and shown superior imaging performances, which can be affected by the training ...

Abstract: The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, ...

We also prove that the two sets of Maxwell equations only depend on the non-linear elations of the conformal group of ...

Codes for the paper: FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames. Preprint version: https://arxiv.org ...

What makes the human brain special in the AI era? The answer lies in the way it changes when we start working in ...

Professor Zhou's team provides a rigorous theoretical foundation in their paper. They demonstrate that a specific form of offline Inverse Reinforcement Learning (IRL) reward function can be recovered ...

We propose a new method called Decoupled Annealing Posterior Sampling (DAPS) that relies on a novel noise annealing process to solve posterior sampling with diffusion prior. Specifically, we decouple ...

We’d all like to be innovative, but few people have "creativity switches" they can turn on at will. (I definitely don’t.) ...

Inverse exchange-traded funds (ETFs) offer a way for contrarian traders to bet against the expected daily performance of an ...

Covering the why to the what to the when, HMRC’s Making Tax Digital programme director Craig Ogilvie cut a convincing figure when he was grilled ...

Abstract Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical ...

The enlightened truth of the role of fire in human evolution. Illustration by Paul Garland The next time you find yourself lost in thought while gazing at a fireplace ablaze or even a solitary candle ...