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deepManReg uses deep neural networks to map various data types onto a topological space (manifolds) and unfold unseen data connections, thus improving prediction of phenotypes from multi-modal data.
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.
Tensor networks exploit the structure of turbulence to offer a compressed description of flows, which leads to efficient fluid simulation algorithms that can be implemented on both classical and quantum computers.