Error recovery in a hospital pharmacy

A field study was performed in a hospital pharmacy aimed at identifying positive and negative influences on the process of detection of and further recovery from initial errors or other failures, thus avoiding negative consequences. Confidential reports and follow-up interviews provided data on 31 near-miss incidents involving such recovery processes. Analysis revealed that organizational culture with regard to following procedures needed reinforcement, that some procedures could be improved, that building in extra checks was worthwhile and that supporting unplanned recovery was essential for problems not covered by procedures. Guidance is given on how performance in recovery could be measured. A case is made for supporting recovery as an addition to prevention-based safety methods.

Improved algorithms for rare event simulation with heavy tails

The estimation of P(S-n > u) by simulation, where S, is the sum of independent. identically distributed random varibles Y-1,..., Y-n, is of importance in many applications. We propose two simulation estimators based upon the identity P(S-n > u) = nP(S, > u, M-n = Y-n), where M-n = max(Y-1,..., Y-n). One estimator uses importance sampling (for Y-n only), and the other uses conditional Monte Carlo conditioning upon Y1,..., Yn-1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Operator quantum error correction

This paper is an expanded and more detailed version of the work [1] in which the Operator Quantum Error Correction formalism was introduced. This is a new scheme for the error correction of quantum operations that incorporates the known techniques - i.e. the standard error correction model, the method of decoherence-free subspaces, and the noiseless subsystem method - as special cases, and relies on a generalized mathematical framework for noiseless subsystems that applies to arbitrary quantum operations. We also discuss a number of examples and introduce the notion of unitarily noiseless subsystems.

Using "war stories" to train for adaptive performance: Is it better to learn from error or success?

The study examines whether error exposure training can enhance adaptive performance. Fifty-nine experienced fire-fighters undergoing training for incident command participated in the study. War stories were developed based on real events to illustrate successful and unsuccessful incident command decisions. Two training methodologies were compared and evaluated. One group was trained using case studies that depicted incidents containing errors of management with severe consequences in fire-fighting outcomes (error-story training) while a second group was exposed to the same set of case studies except that the case studies depicted the incidents being managed without errors and their consequences (errorless-story training). The results provide some support for the hypothesis that it is better to learn from other people's errors than from their successes. Implications for training are discussed.

Error models for mode mismatch in linear optics quantum computing

One of the most significant challenges facing the development of linear optics quantum computing (LOQC) is mode mismatch, whereby photon distinguishability is introduced within circuits, undermining quantum interference effects. We examine the effects of mode mismatch on the parity (or fusion) gate, the fundamental building block in several recent LOQC schemes. We derive simple error models for the effects of mode mismatch on its operation, and relate these error models to current fault-tolerant-threshold estimates.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.