Scholarship and security policy: a review of recent literature

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68058/2/10.1177_002200275900300408.pd

Entangling single atoms over 33 km telecom fibre

Quantum networks promise to provide the infrastructure for many disruptive applications, such as efcient long-distance quantum communication and distributed quantum computing1,2 . Central to these networks is the ability to distribute entanglement between distant nodes using photonic channels. Initially developed for quantum teleportation3,4 and loophole-free tests of Bell’s inequality5,6 , recently, entanglement distribution has also been achieved over telecom fbres and analysed retrospectively7,8 . Yet, to fully use entanglement over long-distance quantum network links it is mandatory to know it is available at the nodes before the entangled state decays. Here we demonstrate heralded entanglement between two independently trapped single rubidium atoms generated over fbre links with a length up to 33 km. For this, we generate atom–photon entanglement in two nodes located in buildings 400 m line-of-sight apart and to overcome high-attenuation losses in the fbres convert the photons to telecom wavelength using polarization-preserving quantum frequency conversion9 . The long fbres guide the photons to a Bell-state measurement setup in which a successful photonic projection measurement heralds the entanglement of the atoms10. Our results show the feasibility of entanglement distribution over telecom fbre links useful, for example, for device-independent quantum key distribution11–13 and quantum repeater protocols. The presented work represents an important step towards the realization of large-scale quantum network links

Control charts for multivariate spatial autoregressive models

This paper deals with spatial detection of changes in model parameters of spatial autoregressive processes. The respective sequential testing problems are formulated. Moreover, we introduce characteristic quantities to monitor means or covariances of multivariate spatial autoregressive processes. Additionally, we also take into account the simultaneous surveillance of the mean vector and the covariance matrix. The aim is to apply control charts, important tools of sequential analysis, to these quantities. The considered control procedures are based on either cumulative sums or exponential smoothing. Further, we illustrate the methodology of statistical process control studying the spectrum of additive colors in a satellite photograph. Via simulation studies, the proposed control procedures are calibrated for a predefined average run length. In addition, we compare the performance of the control procedures considering the out-of-control situation. Eventually, the control charts are applied, and the signals of the different schemes are visualized. The final results are critically discussed

Simultaneous surveillance of means and covariances of spatial models

This paper deals with the problem of statistical process control applied to multivariate spatial models. After introducing the target process that coincides with the spatial white noise, we concentrate on the out-of-control behavior taking into account both changes in means and covariances. Moreover, we propose conventional multivariate control charts either based on exponential smoothing or cumulative sums to monitor means and covariances simultaneously. Via Monte Carlo simulation the proposed control schemes are calibrated. Moreover, their out-of-control behavior is studied for specific mean shifts and scale transformation

Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tails

The purpose of this article is the statistical surveillance of spatial autoregressive models, where the observed process is monitored over both space and time. The considered spatial model contains disturbances with heavy tails. The control procedures based on exponential smoothing or cumulative sums are constructed using characteristic quantities including the first and the second moments to monitor both means and covariances. Via Monte Carlo simulation, the in-control upper control limits of the control schemes are derived. In a further simulation study, we compare the detection speed of these procedures in the out-of-control situation

Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen Regressoren = Statistical surveillance of spatial autoregressive processes with exogenous regressors

This paper deals with statistical process control of spatial autoregressive models with exogenous regressors. The main purpose is the extension of conventional methods of process control in time series analysis. These approaches are modified for applications of spatial monitoring. The method is illustrated by an example of social statistics dealing with natural as well as spatial population change regarding administrative districts of Germany. Via factor analysis latent variables are identified based on manifest variables, because independent factors are needed for the following analysis. Afterwards, the considered regions are divided into groups via cluster analysis. The results of cluster analysis helps to find a specific region of one cluster that is used for in-control estimation. The previously mentioned model is fitted to factor scores using the generalized method of moments. Multivariate control charts based on either exponential smoothing or cumulative sum are used to evaluate full-sample data regarding their control situation. Accordingly, we propose different approaches to sort the regions to be monitored. Eventually, the modified charts signalize structural changes regarding the model based on in-control data without permanent re-estimation