In paired randomized experiments units are grouped in pairs, often based on covariate information, with random assignment within the pairs. Average treatment effects are then estimated by averaging the within-pair differences in outcomes. Typically the variance of the average treatment effect estimator is estimated using the sample variance of the within-pair differences. However, conditional on the covariates the variance of the average treatment effect estimator may be substantially smaller. Here we propose a simple way of estimating the conditional variance of the average treatment effect estimator by forming pairs of-pairs with similar covariate values and estimating the variances within these pairs-of-pairs. Even though these within-pairs-of-pairs variance estimators are not consistent, their average is consistent for the conditional variance of the average treatment effect estimator and leads to asymptotically valid confidence intervals.
The world's largest scientific machine -the Large Hadron Collider (LHC), situated outside Geneva, Switzerland -will generate some 15PB of data at rates up to 1.5GB/s (in the case of the heavy-ion experiment, ALICE) to tape per year of operation. The processing of this data will be performed using a world-wide Grid, the (worldwide) LHC Computing Grid built on top of the Enabled Grid for E-sciencE and Open Science Grid infrastructures. The LHC Computing Grid, which has offered a service for over two years now, is based upon a tier model comprising some 150 sites in tens of countries.In this paper, we describe the data management middleware stack -one of the key services provided by data grids.We give an overview of the different services implemented, a disk-based storage system which can support encryption, tools to manage the storage system and access files, the LCG File Catalogue, and the File Transfer Service. We also review the relationship between these services.
Nano force sensors based on passive diamagnetic levitation with a macroscopic seismic mass are a possible alternative to classical Atomic Force Microscopes when the force bandwidth to be measured is limited to a few Hertz. When an external unknown force is applied to the levitating seismic mass, this one acts as a transducer that converts this unknown input into a displacement that is the measured output signal. Because the little damped and long transient response of this kind of macroscopic transducer can not be neglected, it is then necessary to deconvolve the output to correctly estimate the unknown input force. The deconvolution approach proposed in this article is based on a Kalman filter that use an uncertain a priori model to represent the unknown nanoforce to be estimated. The main advantage of this approach is that the end-user can directly control the unavoidable trade-off that exists between the wished resolution on the estimatedforce and the response time of the estimation.
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