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Data visualization is widely used in science, but interpreting such visualizations is prone to error. Here a dynamic visualization is introduced for capturing more information and improving the reliability of visual interpretations.
A computational workflow centered on probabilistic machine learning is developed to reconstruct the energy dispersion from photoemission band-mapping data. The workflow uncovers previously inaccessible momentum-space structural information at scale.
The Absolut! framework can generate synthetic three-dimensional antibody–antigen structures to assist machine learning and dataset construction for antibody design. Most importantly, the relative machine learning performance learnt on Absolut! datasets is shown to transfer to experimental datasets.
The authors present an open-source framework that enables fast and accurate time–frequency analysis of signals and demonstrate it on real-world applications, such as signals from the brain–computer interface.