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B0853
Title: Quantum mechanics uncertainty, data science inference, and AI in complex time (kime) Authors:  Ivo Dinov - University of Michigan (United States) [presenting]
Milen Velev - BTU Burgas (Bulgaria)
Yueyang Shen - University of Michigan (United States)
Abstract: By using complex time (kime) to lift the classical 4D space-time into the 5D space-kime manifold, translating quantum mechanics principles to address data science and predictive analytics challenges will be demonstrated. We extend the physical laws of velocity, momentum, Lorentz transformations, and 4D solutions of Einstein's equations to their corresponding counterparts in 5D spacekime. Direct AI applications include transforming classical random sampling in spacetime to spacekime phase-uncertainty and a Bayesian formulation of spacekime analytics. Using neuroimaging and macroeconomics data, we will show examples mapping longitudinal data (e.g., time-series) to 2D manifolds (e.g., kime-surfaces) and discuss the subsequent modeling, inference, and AI based on space-kime representations.