Deep neural networks are in the limelight of machine learning with their excellent performance many data-driven applications. However, they can lead to inaccurate predictions when queried out-of-distribution data points, which have detrimental effects especially sensitive domains, such as healthcare and transportation, where erroneous be very costly and/or dangerous. Subsequently, quantifying u...

The quantum multiparameter estimation is very different from the classical due to Heisenberg's uncertainty principle in mechanics. When optimal measurements for parameters are incompatible, they cannot be jointly performed. We find a correspondence relationship between inaccuracy of measurement estimating unknown parameter with error context relations. Taking this as bridge, we incorporate into...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

The minimum achievable statistical uncertainty in the estimation of physical parameters is determined by the quantum Fisher information. Its computation for noisy systems is still a challenging problem. Using a variational approach, we present an equation for obtaining the quantum Fisher information, which has an explicit dependence on the mathematical description of the noise. This method is a...