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Found 5 papers, 2 papers with code
BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation
Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity.
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the prediction/reconstruction of the Last Closed-Flux Surface (LCFS).
POI-Transformers: POI Entity Matching through POI Embeddings by Incorporating Semantic and Geographic Information
A general and robust POI embedding framework, the POI-Transformers, is initially proposed in this study to address these problems of POI entity matching.
Experiment data-driven modeling of tokamak discharge in EAST
By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$.
LSHR-Net: a hardware-friendly solution for high-resolution computational imaging using a mixed-weights neural network
However, to date, there have been two major drawbacks: (1) the high-precision real-valued sensing patterns proposed in the majority of existing works can prove problematic when used with computational imaging hardware such as a digital micromirror sampling device and (2) the network structures for image reconstruction involve intensive computation, which is also not suitable for hardware deployment.
