Serendipitous measurements of deep internal wave signatures are evident in oscillatory variations around the background descent rates reported by one model Deep Argo float. For 10,045 profiles analyzed here, average root-mean-square vertical velocity variances, , from 1,000 m to seafloor, is 0.0045 s−1, with a 5%–95% range 0.0028–0.0067 s−1. Dominant wavelengths, λz, estimated integrals lagged ...

Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FV...

This study examines the effects of calibration errors on model assumptions and data-analytic tools in direct calibration assays. These effects encompass induced dependencies, inflated variances, and heteroscedasticity among the calibrated measurements, whose distributions arise as mixtures. These anomalies adversely affect conventional inferences, including the inconsistency of sample means; th...

We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run–times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co)variances is Σ 2–complete. As a consequence, ne...

We introduce a bootstrap procedure to test the hypothesis Ho that K + 1 variances are homogeneous. The procedure uses a variance-based statistic, and is derived from a normal-theory test for equality of variances. The test equivalently expressed the hypothesis as Ho : η = (η1, . . . , ηK+1) = 0, where ηi’s are log contrasts of the population variances. A box-type acceptance region is constructe...

We introduce an adaptive algorithm to estimate the uncertain parameter of a stochastic optimization problem. The procedure estimates the one-step-ahead means, variances and covariances of a random process in a distribution-free and multidimensional framework when these means, variances and covariances are slowly varying on a given past interval. The quality of the approximate problem obtained w...