Colloquium: Quantum anomalous Hall effect
The quantum Hall (QH) effect, quantized Hall resistance combined with zero longitudinal
resistance, is the characteristic experimental fingerprint of Chern insulators—topologically …
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
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 …
formally link these domains, provide a mechanistic understanding of the hippocampal role in …
Deep denoising autoencoder for seismic random noise attenuation
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 …
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
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 …
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
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 …
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
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 …
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
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 …
unsupervised deep learning (DL) framework, which does not require any prior information …
Pressure–strain interaction as the energy dissipation estimate in collisionless plasma
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 …
studies without a consensus solution. We compare several energy dissipation estimates …
Deep learning seismic random noise attenuation via improved residual convolutional neural network
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 …
procedures, the noise attenuation is important. We propose an adaptive random noise …
Denoising of distributed acoustic sensing data using supervised deep learning
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 …
due to its high-density and low-cost advantages. Because of the harsh acquisition …