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Found 8 papers, 1 papers with code
Compressive Electron Backscatter Diffraction Imaging
To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete dataset.
SenseAI: Real-Time Inpainting for Electron Microscopy
Despite their proven success and broad applicability to Electron Microscopy (EM) data, joint dictionary-learning and sparse-coding based inpainting algorithms have so far remained impractical for real-time usage with an Electron Microscope.
Subsampling Methods for Fast Electron Backscattered Diffraction Analysis
Despite advancements in electron backscatter diffraction (EBSD) detector speeds, the acquisition rates of 4-Dimensional (4D) EBSD data, i. e., a collection of 2-dimensional (2D) diffraction maps for every position of a convergent electron probe on the sample, is limited by the capacity of the detector.
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy
Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times.
Compressive Scanning Transmission Electron Microscopy
We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion
The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy.
