The contribution of quality aspects to process control

Process operators often have difficulties with quality supervision and control for the following reasons: (i) analytical results are infrequent and much delayed, (ii) conventional automatic control cannot sufficiently reduce quality deviations, and (iii) several set values can be candidates for correction of quality deviations. Control performance is discussed with regard to these problems, in relation to the degree of buffering, and types of process perturbations and measuring errors. Some methods are discussed for improving the situation, namely, on-line quality estimation from simpler measurements, and integration of off-line quality measurements and on-line quality measurement and estimation by means of state estimators

Statistical Estimation of Quantum Tomography Protocols Quality

A novel operational method for estimating the efficiency of quantum state tomography protocols is suggested. It is based on a-priori estimation of the quality of an arbitrary protocol by means of universal asymptotic fidelity distribution and condition number, which takes minimal value for better protocol. We prove the adequacy of the method both with numerical modeling and through the experimental realization of several practically important protocols of quantum state tomography

Learning Single-Image Depth from Videos using Quality Assessment Networks

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild

Exploring Prediction Uncertainty in Machine Translation Quality Estimation

Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments. Such scenarios can be improved if quality predictions are accompanied by a measure of uncertainty. However, models in this task are traditionally evaluated only in terms of point estimate metrics, which do not take prediction uncertainty into account. We investigate probabilistic methods for Quality Estimation that can provide well-calibrated uncertainty estimates and evaluate them in terms of their full posterior predictive distributions. We also show how this posterior information can be useful in an asymmetric risk scenario, which aims to capture typical situations in translation workflows.Comment: Proceedings of CoNLL 201

Effect of Synchronizing Coordinated Base Stations on Phase Noise Estimation

In this paper, we study the problem of oscillator phase noise (PN) estimation in coordinated multi-point (CoMP) transmission systems. Specifically, we investigate the effect of phase synchronization between coordinated base stations (BSs) on PN estimation at the user receiver (downlink channel). In this respect, the Bayesian Cram\'er-Rao bound for PN estimation is derived which is a function of the level of phase synchronization between the coordinated BSs. Results show that quality of BS synchronization has a significant effect on the PN estimation