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This paper introduces the Gauge-Fixed Ordinal Network (GON), a temporal convolutional model that assigns consistent predictability scores across different dynamical systems by fixing the gauge freedom of ordinal scoring. The method transfers better than training from scratch on held-out systems, with zero-shot scores retaining ordinal structure at the stochastic boundary.
A minimalist visual-inertial odometry approach uses four photodiodes with optical Gabor masks and a temporal convolutional network to achieve accurate planar motion estimation for differential-drive robots, validated across diverse indoor and outdoor terrains without real-world fine-tuning.