Presence Of Systematic Errors Research Articles

Effective protocol for generating NOON states of resonator modes.

We propose a protocol for the generation of NOON states of resonator modes. The physical model is composed of two Kerr-nonlinear resonators and a four-level qudit. Using the off-resonant couplings between the resonators and the qudit, qudit-level-dependent frequency shifts on the two resonators are induced. The frequency shifts allow us to drive different resonators to the N-photon state when the qudit is in different intermediate levels. Consequently, the generation of NOON states with arbitrary photon number N can be completed in only three steps, i.e., driving the qudit to a superposition state of the two intermediate levels, driving one of the resonators to its N-photon state, and driving the qudit back to its ground level. Numerical simulations show that, in the regime of strong Kerr nonlinearity and coupling strengths, the protocol can produce the NOON state with high fidelity in the cases of different photon numbers. In addition, it is possible for the protocol to produce acceptable fidelity in the presence of systematic errors and decoherence factors. Therefore, the protocol may provide some useful perspectives for effective generation of photonic NOON states.

Evaluation of Long Time-Series Soil Moisture Products Using Extended Triple Collocation and In Situ Measurements in China

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts’ Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China.

Performance, Test-retest Reliability, and Measurement Error of the Upper Limb Seated Shot Put Test According to Different Positions of Execution.

The unilateral Seated Shot-Put Test (USSPT) is an easy to apply, inexpensive tool that can be used to assess shoulder performance unilaterally. Two different positions of execution have been described in previous studies, however, differences regarding reference values and psychometric properties were not assessed. To investigate the performance, test-retest reliability and measurement error of the USSPT according to different positions of execution (floor versus chair) in overhead athletes. The hypothesis was that both positions would present similar values, good to excellent test-retest reliability and clinically acceptable measures. Test-retest reliability. Forty-four overhead athletes performed the USSPT on the floor (USSPT-F) and on a chair (USSPT-C). Normative values were established according to gender, age, and dominance. Test-retest reliability was determined using Intraclass Correlation Coefficient and measurement error through Standard Error of Measurement, Smallest Detectable Change, as well as Bland and Altman plots. Reference values for both positions were provided. Women performed better on the USSPT-C than USSPT-F. Excellent test-retest reliability 0.97 (0.89 - 0.99) for dominant side and 0.95 (0.80 - 0.98) for non-dominant side was found for the USSPT-F. Moderate to excellent reliability 0.91 (0.67 - 0.98) for dominant side and 0.74 (0.01 - 0.93) for non-dominant side was found for the USSPT-C. Presence of systematic error (14.76 cm) was found only for USSPT-C dominant (p=0.011). Differences were found only for women with better performance on the USSPT-C. The USSPT-F presented higher reliability values. Both tests presented clinically acceptable measures. Presence of systematic error was found only in the USSPT-C. 3.

Diagnosis of wind turbine systematic yaw error through nacelle anemometer measurement analysis

The power produced by a wind turbine can be considerably affected by the presence of systematic errors, which are particularly difficult to diagnose. This study deals with wind turbine systematic yaw error and proposes a novel point of view for diagnosing and quantifying its impact on the performance. The keystone is that, up to now in the literature, the effect of the yaw error on the nacelle wind speed measurements of the affected wind turbine has been disregarded. Given this, in this work a new method based on the general principle of flow equilibrium is proposed for the diagnosis of such type of error. It is based on recognizing that a misaligned wind turbine measures the wind speed differently with respect to when it is aligned. The method is shown to be effective for the diagnosis of two test cases, about which an independent estimate of the yaw error is available from upwind measurements (spinner anemometer). A data-driven generalization of the concept of relative performance is then formulated and employed for estimating how much the systematic yaw error affects wind turbine performance. It is shown that the proposed method is more appropriate than methods employing wind speed measurements (like the power curve), which are biased by the presence of the error. The results of this study support that SCADA-collected data can be very useful to diagnose wind turbine systematic yaw error, provided that a critical analysis about their use is done.

Systematic errors in the maximum-likelihood regression of Poisson count data: introducing the overdispersed χ2 distribution

ABSTRACT This paper presents a new method to estimate systematic errors in the maximum-likelihood regression of count data. The method is applicable in particular to X-ray spectra in situations where the Poisson log-likelihood, or the Cash goodness-of-fit statistic, indicate a poor fit that is attributable to overdispersion of the data. Overdispersion in Poisson data is treated as an intrinsic model variance that can be estimated from the best-fit model, using the maximum-likelihood Cmin statistic. The paper also studies the effects of such systematic errors on the ΔC likelihood-ratio statistic, which can be used to test for the presence of a nested model component in the regression of Poisson count data. The paper introduces an overdispersed χ2 distribution that results from the convolution of a χ2 distribution that models the usual ΔC statistic, and a zero-mean Gaussian that models the overdispersion in the data. This is proposed as the distribution of choice for the ΔC statistic in the presence of systematic errors. The methods presented in this paper are applied to XMM–Newton data of the quasar 1ES 1553+113 that were used to detect absorption lines from an intervening warm-hot intergalactic medium (WHIM). This case study illustrates how systematic errors can be estimated from the data, and their effect on the detection of a nested component, such as an absorption line, with the ΔC statistic.

ON THE USE OF SOUND SPEED FOR EVALUATION OF THE PERFORMANCE OF LIQUID ULTRASONIC FLOW METER IN ZERO FLOW CONDITIONS

At present, ultrasonic flow meters have become well known in custody transfer applications in the petroleum industry. Since facilities for calibration of these meters are costly and limited, methods for performance verification in zero flow conditions have been developed. However, for liquid measurement, a method of zero flow verification is not available yet. In this context, this study presents a new method for zero flow test in liquid ultrasonic flow meters, using a highly accurate instrument as a reference for sound speed. The aim is to evaluate the performance, of a multipath liquid ultrasonic flow meter under different pressures and temperatures, by comparing the calculated sound speed with the result from the reference instrument. Additionally, transit time measurement is also verified in order to account for systematic errors. The experiments were conducted with two test fluids: water and mineral oil, with a maximum deviation of 0.25% during comparison. On the other hand, performing a transit time analysis indicates the presence of systematic errors, meaning that transit times can be adjusted in order to improve the performance of the flow meter. Furthermore, a factorial design is performed to investigate the effect of pressure and temperature, indicating a minimal pressure effect on sound speed when compared to the temperature effect.

Application of statistical criteria in the presence of systematic errors in GED

When systematic errors (external radiation, faults in drawing the background curve, too rough model or too strict constraints on the set of determined parameters, etc.) in gas electron diffraction are significant and comparable to random ones, it is necessary to modify the generally accepted statistical formulas used to estimate the errors of the fitted parameters. We present a modified computational procedure accounting for systematic errors in the input data and examples of parameter evaluation for molecules with internal rotation. In all these examples, the confidence intervals for the parameters become narrower.

Regularization and concave loss functions for estimation of chemical kinetic models

Non-linear regression is the primary tool for estimating kinetic models of chemical reactions. The default approach of minimizing the sum of squared residuals tends to underperform in the presence of systematic errors, non-normal distribution of residuals or identifiability issues such as a high correlation between parameters. Therefore, we argue for a careful choice of the fit criteria and propose new, concave loss functions. Together with regularization, they form a robust objective for the regression procedure. Discussion of the rationale behind the proposed approach and its effects is illustrated by laboratory data on the transesterification of palm oil. A dedicated simulation study complements qualitative examples. All of the top-performing methods use regularization. Concave loss functions were among the best in 6–7 out of 8 test cases, compared to 2–3 for the classical square loss confirming both statistical and practical usefulness of the novel fit criteria. This result holds for a variety of modern optimizers. In 76% of our simulations, we obtained results not significantly worse than the best, whereas methods currently used in the literature provide 38% for the relative and 0% for the square loss.

Nonseparable modulation strategy for channeled spatiotemporal Stokes polarimeters.

Spatiotemporally modulated polarimeters have shown promising imaging performance by leveraging the tradeoff between spatial bandwidth and temporal bandwidth to outperform polarimeters that use spatial or temporal modulation alone. However, the existing separable modulation strategy, in which the spatial carriers are generated independently from the temporal carriers, makes such devices sensitive to the systematic errors of the rotation element inevitably. In this paper, we propose two novel strategies that have spatiotemporal modulation that is inherently mixed. The method enables different elements of the Mueller matrix to be used to create the carriers, reducing the effects of systematic errors in different ways. We present the indepth comparison of the channel structure and the reconstruction accuracy of each modulation strategy in various bandwidth scenarios under the presence of systematic error. Simulation results show that the nonseparable modulation can provide higher reconstruction accuracy of polarimetric information as compared to the separable strategy.

Balanced Frames: A Useful Tool in Signal Processing with Good Properties

So far there has not been paid attention to frames that are balanced, i.e. those frames which sum is zero. In this paper we consider balanced frames, and in particular balanced unit norm tight frames, in finite dimensional Hilbert spaces. Here we discover various advantages of balanced unit norm tight frames in signal processing. They give an exact reconstruction in the presence of systematic errors in the transmitted coefficients, and are optimal when these coefficients are corrupted with noises that can have non-zero mean. Moreover, using balanced frames we can know that the transmitted coefficients were perturbed, and we also have an indication of the source of the error. We analyze several properties of these types of frames. We define an equivalence relation in the set of the dual frames of a balanced frame, and use it to show that we can obtain all the duals from the balanced ones. We study the problem of finding the nearest balanced frame to a given frame, characterizing completely its existence and giving its expression. We introduce and study a concept of complement for balanced frames. Finally, we present many examples and methods for constructing balanced unit norm tight frames.

Reliability of a markerless motion capture system to measure the trunk, hip and knee angle during walking on a flatland and a treadmill

BackgroundMarkerless motion capture system (MLS) using an infrared sensor such as Microsoft Kinect has been used for gait analysis. Several studies have shown that kinematic measurements of trunk and lower limb joint angles during walking measured by MLS are valid. However, the reproducibility, presence of systematic error, or degree of random error of kinematic measurements during walking using MLS with Kinect v2 were not demonstrated. This study was made to confirm the reliability of kinematic measurements using Kinect v2 during gait. Twenty-two young, injury-free individuals volunteered to participate. Walks were made at 2 miles per hour (mph) on both the flatland and the treadmill. Intra-class correlation coefficients (ICCs) were calculated, systematic errors identified, and minimal detectable changes (MDCs) were estimated to assess the reliability of kinematic measurements of trunk, hip, and knee joint angles during walking. For trunk angles measured on the flatland, ICC was higher than 0.6, systematic error was smaller, and MDC was 2.2° smaller than that in gait on the treadmill (6.6°). In hip joint angles measured on the flatland, although systematic error was slight, ICC was not higher than on the treadmill and MDC exceeded 5°. The results for the knee joint were similar to those of the hip joint. Kinect can detect kinematic abnormalities of the trunk during slow walking on the flatland easier than on the treadmill.

On reverberation mapping lag uncertainties

ABSTRACT We broadly explore the effects of systematic errors on reverberation mapping lag uncertainty estimates from javelin and the interpolated cross-correlation function (ICCF) method. We focus on simulated light curves from random realizations of the light curves of five intensively monitored AGNs. Both methods generally work well even in the presence of systematic errors, although javelin generally provides better error estimates. Poorly estimated light-curve uncertainties have less effect on the ICCF method because, unlike javelin , it does not explicitly assume Gaussian statistics. Neither method is sensitive to changes in the stochastic process driving the continuum or the transfer function relating the line light curve to the continuum. The only systematic error we considered that causes significant problems is if the line light curve is not a smoothed and shifted version of the continuum light curve but instead contains some additional sources of variability.

Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation

State estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. However, if this assumption happens approximately due to the presence of systematic errors, as outliers, classic dynamic data reconciliation provides biased results. In this work, a two-step methodology of Robust Dynamic Data Reconciliation and Systematic Error Detection is proposed. It takes advantages of a moving measurement window of fixed dimension and the features of the M-estimators. Furthermore, the presence of outliers is detected using a Robust Measurement Test. Two case studies are proposed, which work with the Huber and Biweigth M-estimators. A nonlinear benchmark extracted from the literature is considered, and performance measures are reported. The results obtained demonstrate the effectiveness of the proposed methodology.

How Well Does Your Phylogenetic Model Fit Your Data?

The test for model-to-data fitness is a fundamental principle within the statistical sciences. The purpose of such a test is to assess whether the selected best-fitting model adequately describes the behavior in the data. Despite their broad application across many areas of statistics, goodness of fit tests for phylogenetic models have received much less attention than model selection methods in the last decade. At present a number of approaches have been suggested. However, these are often flawed, with problems ranging from the presence of systematic error in the models themselves to the difficulties presented by the nature of phylogenetic data. Ultimately these problems lead to an inadequate choice of statistic. This is one of the main reasons why goodness of fit assessment is often a neglected step within phylogenetic analysis. We argue not only for the necessity of these goodness of fit measures to test how well the model reflects the data, but additionally for the need for "useful" tests that explain why the model-to-data fit may be inadequate. Such tests are a critical part of the model building process, allowing the model to be adapted to provide a better model-to-data fit or to reject a model class outright due to such an inadequate fit that the intended use of the class may be compromised. Proposed and existing methods in both the maximum likelihood and Bayesian framework will be discussed here, whilst highlighting their strengths and limitations for assessing goodness of fit. The final section discusses some critical open statistical problems in goodness of fit assessment for this field, with the hope of encouraging more research into such a fundamental yet underdeveloped area of phylogenetic inference. [Bayesian phylogenetics; Goodness of fit; maximum likelihood; molecular phylogenetics; outlier detection; residual diagnostics.].

Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale.

Use of quantum mechanical/molecular mechanical (QM/MM) methods in binding free energy calculations, particularly in the SAMPL challenge, often fail to achieve improvement over standard additive (MM) force fields. Frequently, the implementation is through use of reference potentials, or the so-called "indirect approach", and inherently relies on sufficient overlap existing between MM and QM/MM configurational spaces. Thisoverlap is generallypoor, particularly for the use of free energy perturbation to perform the MM to QM/MM free energy correction at the end states of interest (e.g., bound and unbound states). However, by utilizing MM parameters that best reproduce forces obtained at the desired QM level of theory, it is possible to lessen the configurational disparity between MM and QM/MM. To this end, we sought to use force matching to generate MM parameters for the SAMPL6 CB[8] host-guest binding challenge, classically compute binding free energies, and apply energetic end state corrections to obtain QM/MM binding free energy differences. For the standard set of 11 molecules and the bonus set (including three additional challenge molecules), error statistics, such as the root mean square deviation (RMSE) were moderately poor (5.5 and 5.4 kcal/mol). Correlation statistics, however, were in the top two for both standard and bonus set submissions ([Formula: see text] of 0.42 and 0.26, [Formula: see text] of 0.64 and 0.47 respectively). High RMSE and moderate correlation strongly indicated the presence of systematic error. Identifiable issues were ameliorated for two of the guest molecules, resulting in a reduction of error and pointing to strong prospects for thefuture use of this methodology.

The Quest for the Inflationary Spectral Runnings in the Presence of Systematic Errors

Abstract Cosmological inflation predicts that the scalar spectral index “runs” with scale. Constraints on the values of the spectral runnings, and , therefore provide a fundamental test of the physics of inflation. Here, we study the feasibility of measuring the runnings when information from upcoming large-volume galaxy surveys is used to supplement the information provided by a cosmic microwave background (CMB)-S4 experiment, particularly focusing on the effect of including high-k, nonlinear scales. Since these measurements will be sensitive to modeling uncertainties for the nonlinear power spectrum, we examine how three different ways of parameterizing those systematics—introducing zero, two, or several hundred nuisance parameters—affect constraints and protect against parameter biases. Considering statistical errors alone, we find that including strongly nonlinear scales can substantially tighten constraints. However, these constraints weaken to levels not much better than those from a CMB-S4 experiment alone when we limit our analysis to scales where estimates are not strongly affected by systematic biases. Given these considerations, near-future large-scale structure (LSS) surveys are unlikely to add much information to the CMB-S4 measurement of the first running α s . There is more potential for improvement for the second running, β s , for which LSS information will allow constraints to be improved by a factor of 3–4 relative to using the CMB alone. Though these constraints are still above the value predicted by slow-roll inflation, they do probe regions of parameter space relevant to nonstandard inflationary models with large runnings, for example, those that can generate an appreciable abundance of primordial black holes.

Inference for Errors-in-Variables Models in the Presence of Systematic Errors with an Application to a Satellite Remote Sensing Campaign

ABSTRACTMotivated by a satellite remote sensing mission, this article proposes a multivariable errors-in-variables (EIV) regression model with heteroscedastic errors for relating the satellite data products to similar products from a well-characterized but globally sparse ground-based dataset. In the remote sensing setting, the regression model is used to estimate the global divisor for the satellite data. The error structure of the proposed EIV model comprises two components: A random-error component whose variance is inversely proportional to sample size of underlying individual observations which are aggregated to obtain the regression data, and a systematic-error component whose variance remains the same as the underlying sample size increases. In this article, we discuss parameter identifiability for the proposed model and obtain estimates from two-stage parameter estimation. We illustrate our proposed procedure through both simulation studies and an application to validating measurements of atmospheric column-averaged CO2 from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite. The validation uses coincident target-mode OCO-2 data that are temporally and spatially sparse and ground-based measurements from the Total Carbon Column Observing Network (TCCON) that are spatially sparse but more accurate. Supplementary materials for the article are available online.

NEW RESULTS IN REDUCED ESTIMATION WITH SYSTEMATIC ERRORS IN MEASUREMENTS AND THEIR APPLICATION FOR THE FORMATION OF A GROUP TIME SCALE

The article deals with the behavior of reduced scalar estimates in the presence of systematic errors in the observational data. The proposed procedure with a different method of forming the reduction coefficient. A quasi-optimal variant of the compression parameter formation is considered. Simulation results for different conditions of application of the proposed algorithms are presented. Currently, one of the ways to improve the accuracy of the formation of the time scale in solving the problems of frequency-time customer support is the averaging of the readings of several generators. At the same time, this approach, as shown in the theory of statistical estimation, is effective for parrying the random component of the error of the estimated process. However, for frequency generators random error can be effectively compensated for a long range of observations, but the systematic component - frequency drift - is a serious problem, which can be eliminated by averaging only under certain conditions. Therefore, the article proposes a version of the reduced estimate, effective, as shown, to parry the departure of the time scale by introducing a shift in the implementation of compression, defined by the reduction procedure. The conditions in which the degree of the achieved positive effect has a practical sense are considered.

Optimal bandwidth and systematic error of full-Stokes micropolarizer arrays

In this paper, we present the first in-depth analysis of the bandwidth tradeoffs, error performance, and noise resiliency of full-Stokes micropolarizer array (MPA) designs. By applying our Fourier domain tools that provide a systematic way for arranging information carriers and allocating bandwidth, we develop a number of new full-Stokes MPA layouts and compare them to the existing full-Stokes MPAs in the literature, all of which use 2×2 pixel unit cells to build the MPA. We compare the reconstruction accuracy afforded by these traditional designs with the generalized 2×L family of MPAs, a 3×3 tiling, as well as a 2×2×3 layout that uses multiple snapshots and trades off temporal resolution for spatial resolution. Of those systems, the hybrid spatiotemporally modulated 2×2×3 MPA provisions the most bandwidth and provides the highest reconstruction accuracy, while the modified 2×L family remains the best performing single-snapshot MPA. Additionally, we study the degradation of reconstruction accuracy under the presence of systematic error in MPA fabrication. We find that reducing the amount of correlated error is by far the largest factor in ensuring robust performance.

Integrated Sachs-Wolfe map reconstruction in the presence of systematic errors

The decay of gravitational potentials in the presence of dark energy leads to an additional, late-time contribution to anisotropies in the cosmic microwave background (CMB) at large angular scales. The imprint of this so-called integrated Sachs-Wolfe (ISW) effect to the CMB angular power spectrum has been detected and studied in detail, but reconstructing its spatial contributions to the CMB $\textit{map}$, which would offer the tantalizing possibility of separating the early- from the late-time contributions to CMB temperature fluctuations, is more challenging. Here we study the technique for reconstructing the ISW map based on information from galaxy surveys and focus in particular on how its accuracy is impacted by the presence of photometric calibration errors in input galaxy maps, which were previously found to be a dominant contaminant for ISW signal estimation. We find that both including tomographic information from a single survey and using data from multiple, complementary galaxy surveys improve the reconstruction by effectively self-calibrating the estimator against spurious power contributions from calibration errors. A high-fidelity reconstruction further requires one to account for the contribution of calibration errors to the observed galaxy power spectrum in the model used to construct the ISW estimator. We find that if the photometric calibration errors in galaxy surveys can be independently controlled at the level required to obtain unbiased dark energy constraints, then it is possible to reconstruct ISW maps with excellent accuracy using a combination of maps from two galaxy surveys with properties similar to Euclid and SPHEREx.