Colloquium: Quantum anomalous Hall effect

CZ Chang, CX Liu, AH MacDonald - Reviews of Modern Physics, 2023 - APS
The quantum Hall (QH) effect, quantized Hall resistance combined with zero longitudinal
resistance, is the characteristic experimental fingerprint of Chern insulators—topologically …

The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation

JCR Whittington, TH Muller, S Mark, G Chen, C Barry… - Cell, 2020 - cell.com
The hippocampal-entorhinal system is important for spatial and relational memory tasks. We
formally link these domains, provide a mechanistic understanding of the hippocampal role in …

Deep denoising autoencoder for seismic random noise attenuation

OM Saad, Y Chen - Geophysics, 2020 - library.seg.org
Attenuation of seismic random noise is considered an important processing step to enhance
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …

Nonstationary predictive filtering for seismic random noise suppression—A tutorial

H Wang, W Chen, W Huang, S Zu, X Liu, L Yang… - Geophysics, 2021 - library.seg.org
Predictive filtering (PF) in the frequency domain is one of the most widely used denoising
algorithms in seismic data processing. PF is based on the assumption of linear or planar …

Unsupervised 3-D random noise attenuation using deep skip autoencoder

L Yang, S Wang, X Chen, OM Saad… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …

Self-attention deep image prior network for unsupervised 3-D seismic data enhancement

OM Saad, YASI Oboue, M Bai, L Samy… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
We develop a deep learning framework based on deep image prior (DIP) and attention
networks for 3-D seismic data enhancement. First, the 3-D noisy data are divided into …

Unsupervised deep learning for 3D interpolation of highly incomplete data

OM Saad, S Fomel, R Abma, Y Chen - Geophysics, 2023 - library.seg.org
We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …

Pressure–strain interaction as the energy dissipation estimate in collisionless plasma

Y Yang, WH Matthaeus, S Roy… - The Astrophysical …, 2022 - iopscience.iop.org
The dissipative mechanism in weakly collisional plasma is a topic that pervades decades of
studies without a consensus solution. We compare several energy dissipation estimates …

Deep learning seismic random noise attenuation via improved residual convolutional neural network

L Yang, W Chen, H Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing
procedures, the noise attenuation is important. We propose an adaptive random noise …

Denoising of distributed acoustic sensing data using supervised deep learning

L Yang, S Fomel, S Wang, X Chen, W Chen, OM Saad… - Geophysics, 2023 - library.seg.org
Distributed acoustic sensing (DAS) is an emerging technology for acquiring seismic data
due to its high-density and low-cost advantages. Because of the harsh acquisition …