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In this article, we present an event‐triggered control law for systems with an unknown control direction, where the adaptation dynamics of the Nussbaum gain are sampled. Specifically, we employ the emulation method and introduce a specific set of sampling errors. This approach enables the design of an event‐triggered law without encountering Zeno behavior. Additionally, we introduce a lemma to address the non‐integrability issue of the Nussbaum gain dynamics during the event‐triggered law design. We apply this design philosophy to co‐design both the continuous‐time controller and the event‐triggered law, aiming to achieve the global stabilization of systems with an unknown control direction. Finally, we validate the theoretical results through a numerical example. [ABSTRACT FROM AUTHOR]

This systematic review and meta‐analysis assesses independently the acute effects of anodal and cathodal motor cortex transcranial direct current stimulation (tDCS) on athletic performance in healthy adults. Besides, it evaluates the unique and conjoint effects of potential moderators (i.e., stimulation parameters, exercise type, subjects' training status and risk of bias). Online database search was performed from inception until March 18th 2024 (PROSPERO: CRD42023355461). Forty‐three controlled trials were included in the systematic review, 40 in the anodal tDCS meta‐analysis (68 effects), and 9 (11 effects) in the cathodal tDCS meta‐analysis. Performance enhancement between pre‐ and post‐stimulation was the main outcome measure considered. The anodal tDCS effects on physical performance were small to moderate (g =.29, 95%CI [.18,.40], PI = −.64 to 1.23, I2 = 64.0%). Exercise type, training status and use of commercial tDCS were significant moderators of the results. The cathodal tDCS effects were null (g =.04, 95%CI [−.05,.12], PI = −.14 to.23, I2 = 0%), with a small to moderate heterogeneity entirely due to sampling error, thus impairing further moderator analysis. These findings hold significant implications for the field of brain stimulation and physical performance, as they not only demonstrate a small to moderate effect of acute tDCS but also identify specific categories of individuals, devices and activities that are more susceptible to improvements. By addressing the multidimensional factors influencing the mechanisms of tDCS, we also provide suggestions for future research. Results indicate a small favouring effect of anodal tDCS on athletic performance with large heterogeneity. Exercise type, training status and use of commercial tDCS significantly moderate anodal tDCS effects. Cathodal tDCS shows null effects, with small to moderate heterogeneity entirely due to sampling error. [ABSTRACT FROM AUTHOR]

The primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the n$$ n $$‐step time delays associated with the existing NN updating laws derived by the common n$$ n $$‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common n$$ n $$‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Composite DNA letters, by merging all four DNA nucleotides in specified ratios, offer a pathway to substantially increase the logical density of DNA digital storage (DDS) systems. However, these letters are susceptible to nucleotide errors and sampling bias, leading to a high letter error rate, which complicates precise data retrieval and augments reading expenses. To address this, Derrick‐cp is introduced as an innovative soft‐decision decoding algorithm tailored for DDS utilizing composite letters. Derrick‐cp capitalizes on the distinctive error sensitivities among letters to accurately predict and rectify letter errors, thus enhancing the error‐correcting performance of Reed‐Solomon codes beyond traditional hard‐decision decoding limits. Through comparative analyses in the existing dataset and simulated experiments, Derrick‐cp's superiority is validated, notably halving the sequencing depth requirement and slashing costs by up to 22% against conventional hard‐decision strategies. This advancement signals Derrick‐cp's significant role in elevating both the precision and cost‐efficiency of composite letter‐based DDS. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion The policy implemented in Israel with the purpose of eradicating the COVID‐19 pandemic restricted Israeli citizens’ individual freedom. During this period, Israel was suffering from government instability that led to recurring election campaigns within a short period. In addition, the prime minister was under legal investigation for suspicions of government corruption. This study examines the legitimization of government by the Israeli public during the COVID‐19 pandemic.The research findings are based on a representative attitude survey conducted by a survey company among Israeli citizens (men and women older than 18), including 604 respondents from the Jewish population and others and another 135 respondents from the Arab sector, that is, a total of 739 respondents, where the sampling error was 4.4 percent.The findings indicate that right‐wing voters tended to support governmental legitimization at a higher rate compared to voters from opposing blocs. Correspondingly, they tended to oppose claims attacking governmental legitimization at a lower rate in comparison to coalition party voters.The conclusion indicates that the party identification of the respondents is a mediating variable in their perception of governmental legitimacy vis‐à‐vis each of the three variables examined, thereby strengthening the perceptual screening. [ABSTRACT FROM AUTHOR]

Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars. [ABSTRACT FROM AUTHOR]

This article addresses the global regulation by periodic event‐triggered output feedback control for a class of feedforward nonlinear systems. Particularly, the considered systems suffer from parameter uncertainties and unknown measurement sensitivity, which entail integrating advanced compensation strategies within the event‐triggered framework. To this end, a time‐varying gain is devised to tackle uncertain nonlinearities as well as the sampling error. Subsequently, a novel periodic event‐triggered output feedback controller is constructed based on the delicate choice of design parameters. Notably, the proposed control scheme is able to avoid the continuous monitoring of the system behavior, and guarantee that all closed‐loop system states are globally bounded and ultimately converge to zero. Finally, a practical example is given to illustrate the main results. [ABSTRACT FROM AUTHOR]

This study is concerned with the stabilization analysis and controller design for networked systems with stochastic sampling and two‐channel deception attacks. First, we give a general matrix decomposition approach which is applicable to scenarios where the system matrix A$A$ contains complex‐value eigenvalues. Then, a discrete stochastic framework is established for a class of networked systems which considers the joint effects of sampling errors and two‐channel deception attacks. Utilizing the matrix decomposition approach introduced in this study, it becomes feasible to decouple the expectation operations for specific coupling matrices characterized by substantial nonlinearity and randomness. Based on this, a stabilization controller is constructed that ensures the exponential mean‐square stability of the resulting discrete stochastic system. Finally, three simulation examples are provided to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Questions: We asked how interobserver error in sampling vegetation (excluding trees) varied over time, space and habitat type; determined whether there were any obvious correlates of observer error; and evaluated evidence of bias among observers. Location: Nine national park units in the Midwestern USA. Methods: We quantified observer error in the context of a long‐term monitoring program employing three observers, evaluating interobserver error across 11 locations in the Midwestern USA over five years. The vegetation (excluding trees) was sampled independently by two teams of observers at prairie and woodland locations (n = 94 plots total). Results: Total pseudoturnover ranged between 20.2% and 22.1% at prairie locations, and between 16.8% and 28.6% at woodland locations. The overlooking component of pseudoturnover accounted for 75% or more of total pseudoturnover, with misidentification and cautious components each contributing 19% or less of the total, depending on location. The percentage of comparisons in which both observers recorded the same cover class ranged from 71.3% to 78.5% at the prairie locations and 56.9% to 85.6% at woodland locations. When observers did not agree on cover class, they were off by more than one class less than 6% of the time. Overlooking error was more likely to occur for species with less cover, while estimation error was more likely to occur for species with greater cover. A bias existed among observers, as the least experienced observer recorded 6.2%–11.8% more species than the other two observers. Interobserver bias also existed for rates of estimation error, as one observer consistently recorded higher cover classes. Conclusions: Observer error is a pervasive aspect of vegetation sampling. Continued training and experience yielded limited increases in precision. Elements of the sampling design probably reduced observer error to a certain degree, although some level of interobserver error in vegetation surveys is unavoidable. [ABSTRACT FROM AUTHOR]

Specification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to reducing statistical errors in generative models, with a specific focus on generative adversarial networks (GANs). Inspired by the error analysis of GANs, we find that statistical errors mainly arise from random sampling, leading to significant uncertainties in GANs. To address this issue, we propose a selective sampling mechanism called space‐filling sampling. Our method aims to increase the sampling probability in areas with insufficient data, thereby improving the learning performance of the generator. Theoretical analysis confirms the effectiveness of our approach in reducing statistical errors and accelerating convergence in GANs. This research represents a pioneering effort in targeting the reduction of statistical errors in GANs, and it demonstrates the potential for enhancing the training of other generative models. [ABSTRACT FROM AUTHOR]

Purpose: To acquire accurate volumetric multi‐channel B1+$$ {\mathrm{B}}_1^{+} $$ maps in under 14 s whole‐brain or 23 heartbeats whole‐heart for parallel transmit (pTx) applications at 7 T. Theory and Methods: We evaluate the combination of three recently proposed techniques. The acquisition of multi‐channel transmit array B1+$$ {\mathrm{B}}_1^{+} $$ maps is accelerated using transmit low rank (TxLR) with absolute B1+$$ {\mathrm{B}}_1^{+} $$ mapping (Sandwich) acquired in a B1+$$ {\mathrm{B}}_1^{+} $$ time‐interleaved acquisition of modes (B1TIAMO) fashion. Simulations using synthetic body images derived from Sim4Life were used to test the achievable acceleration for small scan matrices of 24 × 24. Next, we evaluated the method by retrospectively undersampling a fully sampled B1+$$ {\mathrm{B}}_1^{+} $$ library of nine subjects in the brain. Finally, Cartesian undersampled phantom and in vivo images were acquired in both the brain of three subjects (8Tx/32 receive [Rx]) and the heart of another three subjects (8Tx/8Rx) at 7 T. Results: Simulation and in vivo results show that volumetric multi‐channel B1+$$ {\mathrm{B}}_1^{+} $$ maps can be acquired using acceleration factors of 4 in the body, reducing the acquisition time to within 23 heartbeats, which was previously not possible. In silico heart simulations demonstrated a RMS error to the fully sampled native resolution ground truth of 4.2° when combined in first‐order circularly polarized mode (mean flip angle 66°) at an acceleration factor of 4. The 14 s 3D B1+$$ {\mathrm{B}}_1^{+} $$ maps acquired in the brain have a RMS error of 1.9° to the fully sampled (mean flip angle 86°). Conclusion: The proposed method is demonstrated as a fast pTx calibration technique in the brain and a promising method for pTx calibration in the body. [ABSTRACT FROM AUTHOR]

The impact of an episodic river flood is intimately linked to its duration. Yet it is still unclear how often should a river be observed to accurately determine the occurrence and duration of extreme events. Here we assess flow statistics along with peak flow event detection and duration as a function of the discharge sampling period for large tributaries of the Mississippi basin using hourly gages over 2010–2022. Median event durations above high quantiles spatially vary from around 2 days upstream to 30 days downstream. Discharge mean, standard deviation, and quantiles can all be estimated within 2.5% error for sampling periods up to 8 days. A minimum temporal sampling 4× (2×) finer than peak flow event median duration is required to detect 95 ± 3% (85 ± 5%) of events and to estimate their duration within 90 ± 5% (75 ± 10%) median accuracy. Our findings have direct implications for future satellite missions concerned with capturing flood events. Plain Language Summary: High flow events and their duration in rivers are key aspects of flood studies. Yet, it is still unclear how often a river should be observed to detect floods and determine their durations. Analyzing hourly stream gage data in the Mississippi River basin from 2010 to 2022, we calculate that flood events vary widely in duration from upstream (2 days) to downstream (30 days). We show that observing large rivers every few days is sufficient to compute common long‐term river flow statistics within 2.5% error. However, capturing most floods and accurately assessing their duration (within 10% error) requires monitoring at least four times more frequently than the typical duration of these events. This means that if a major flood typically lasts 10 days, the river data should be collected at least every 2–3 days. Our findings have implications for the design of monitoring systems concerned with the accurate capture of floods, including those using Earth orbiting satellites. Key Points: Peak flow events in the Mississippi basin range from 2 days upstream to 30 days downstream, hence exhibiting acute spatial variabilityDecadal mean, standard deviation and quantiles of hourly discharge can all be computed within 2.5% error up to an 8‐day sampling periodSampling periods must be a factor of 4 finer than median peak flow event durations to estimate durations within 90 ± 5% median accuracy [ABSTRACT FROM AUTHOR]

A too‐weak eddy feedback in models has been proposed to explain the signal‐to‐noise paradox in seasonal‐to‐decadal forecasts of the winter Northern Hemisphere. We show that the "eddy feedback parameter" (EFP) used in previous studies is sensitive to sampling and multidecadal variability. When these uncertainties are accounted for, the EFP diagnosed from CMIP6 historical simulations generally falls within the reanalysis uncertainty. We find the EFP is not independent of the sampled North Atlantic Oscillation (NAO). Within the same dataset, a sample containing larger NAO variability will show a larger EFP, suggesting that the link between eddy feedbacks and the signal‐to‐noise paradox could be due to sampling effects with the EFP. An alternative measure of eddy feedback, the barotropic energy generation rate, is less sensitive to sampling errors and delineates CMIP6 models that have weak, strong, or unbiased eddy feedbacks, but shows little relation to NAO variability. Plain Language Summary: Model forecasts on seasonal‐to‐decadal timescales have recently been shown to have significant skill in predicting the North Atlantic Oscillation (NAO, a large‐scale pattern of variability). However, these forecasts are undermined by signal‐to‐noise ratios that are lower than expected given the skill, meaning the models are underconfident. This problem is known as the "signal‐to‐noise paradox". Previous work has shown that models underestimate the strength of feedback from atmospheric eddies onto the midlatitude circulation, but models with a stronger eddy feedback suffer less from the signal‐to‐noise paradox. However, we find that the "eddy feedback parameter" (EFP) used in these studies exhibits large sampling uncertainty that has not previously been taken into account. When accounting for this sampling uncertainty, the EFP in models is generally consistent with reanalysis data. Furthermore, across samples, the EFP correlates with the variability of the NAO, meaning they are not independent, which makes the EFP problematic for understanding the causes of the signal‐to‐noise paradox. Samples with larger NAO variability are diagnosed with a larger EFP, even within the same dataset. An alternative measure of eddy feedback is less sensitive to sampling and better identifies models which have weak, strong, or unbiased eddy feedbacks. Key Points: The "eddy feedback parameter" is a highly non‐stationary quantity, making reanalysis and model comparisons problematic on short time periodsSampling uncertainty in the eddy feedback parameter from reanalysis data is comparable to the intermodel spreadBarotropic energy generation rate is a more stable quantity, but does not explain model spread in North Atlantic climate variability [ABSTRACT FROM AUTHOR]

Ecological meta‐analyses usually exhibit high relative heterogeneity of effect size: most among‐study variation in effect size represents true variation in mean effect size, rather than sampling error. This heterogeneity arises from both methodological and ecological sources. Methodological heterogeneity is a nuisance that complicates the interpretation of data syntheses. One way to reduce methodological heterogeneity is via coordinated distributed experiments, in which investigators conduct the same experiment at different sites, using the same methods. We tested whether coordinated distributed experiments in ecology exhibit 1) low heterogeneity in effect size, and 2) lower heterogeneity than meta‐analyses, using data on 17 effects from eight coordinated distributed experiments, and 406 meta‐analyses. Consistent with our expectations, among‐site heterogeneity typically comprised <50% of the variance in effect size in distributed experiments. In contrast, heterogeneity within and among studies typically comprised >90% of the variance in effect size in meta‐analyses. However, this difference largely reflected the small size of most coordinated distributed experiments, and was no longer significant after controlling for size (number of studies or sites). These results are consistent with the hypothesis that methodological heterogeneity rarely comprises a substantial fraction of variance in effect size in ecology. We also conducted pairwise comparisons of absolute heterogeneity between coordinated distributed experiments and meta‐analyses on the same topics. Coordinated distributed experiments did not consistently exhibit lower absolute heterogeneity in effect size than meta‐analyses on the same topics. Our findings suggest that coordinated distributed experiments rarely increase uniformity of results by reducing methodological heterogeneity. Our results help refine the numerous distinct reasons for conducting coordinated distributed experiments. [ABSTRACT FROM AUTHOR]

Ensemble data assimilation methods, such as the Ensemble Kalman Filter (EnKF), are well suited for climate reanalysis because they feature flow‐dependent covariance. However, because Earth System Models are heavy computationally, the method uses a few tens of members. Sampling error in the covariance matrix can introduce biases in the deep ocean, which may cause a drift in the reanalysis and in the predictions. Here, we assess the potential of the hybrid covariance approach (EnKF‐OI) to counteract sampling error. The EnKF‐OI combines the flow‐dependent covariance computed from a dynamical ensemble with another covariance matrix that is static but less prone to sampling error. We test the method within the Norwegian Climate Prediction Model, which combines the Norwegian Earth System Model and the EnKF. We test the performance of the reanalyzes in an idealized twin experiment, where we assimilate synthetic sea surface temperature observations monthly over 1980–2010. The dynamical and static ensembles consist respectively of 30 members and 315 seasonal members sampled from a pre‐industrial run. We compare the performance of the EnKF to an EnKF‐OI with a global hybrid coefficient, referred to as standard hybrid, and an EnKF‐OI with adaptive hybrid coefficients estimated in space and time. Both hybrid covariance methods cure the bias introduced by the EnKF at intermediate and deep water. The adaptive EnKF‐OI performs best overall by addressing sampling noise and rank deficiencies issues and can sustain low analysis errors by doing smaller updates than the standard hybrid version. Plain Language Summary: Data assimilation is a statistical method that reduces uncertainty in a model, based on observations. Because of their ease of implementation, the ensemble data assimilation methods, that rely on the statistics of a finite ensemble of realizations of the model, are popular for climate reanalysis and prediction. However, observations are sparse—mostly near the surface—and the sampling error from data assimilation method introduces a deterioration in the deep ocean. We use a method that complements this ensemble with a pre‐existing database of model states to reduce sampling error. We show that the approach substantially reduces error at the intermediate and deep ocean. The method typically requires the tunning of a parameter, but we show that it can be estimated online, achieving the best performance. Key Points: Hybrid covariance handles sampling error and improves the update of deep water masses when assimilating surface observation with an Ensemble Kalman FilterThe method is well suited to provide a long coupled reanalysis of the past centuryHybrid covariance with adaptive hybrid coefficients explicitly estimated in space and time achieved the best performance [ABSTRACT FROM AUTHOR]

When CO2 saline aquifer storage is carried out, the heterogeneity of reservoir rock is an important factor affecting CO2 transport, and the reservoir heterogeneity in numerical simulations is mainly manifested as the heterogeneity of the parameter field. Since the parameter distributions across the reservoir are not available with the existing probes, the stochastic finite element method is combined with a two‐phase flow model to establish an unconditional random field of permeability, and computations are performed using the Monte Carlo method. The permeability, CO2 maximum migration distance (Md) and CO2 sweep area (Sa) were analyzed for mutual correlation. The permeability correlation area affecting Md and Sa was obtained, and the changes in the correlation area under the coefficient of variation (Cv) and correlation length (λx) of the permeability field in the different reservoirs were analyzed. The kriging superposition approach (KSA) was subsequently used to estimate both the Md and Sa of the target reservoir by establishing conditional random fields based on the sampling parameters in regions with different correlations, resulting in errors of 0.66% for Md and 0.96% for Sa in the high correlation region and 4.86% and 3.12% for Md and Sa in the low correlation region, which suggested that the sampling results from the high correlation region were less biased in the estimation. Under limited sampling conditions, it is recommended that samples be collected in regions with high correlations to reduce the uncertainty of CO2 transport analysis due to unknown heterogeneity. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study mainly concerns correction for measurement error using the meta‐analysis of Fisher's z‐transformed correlations. The disattenuation formula of Spearman (American Journal of Psychology, 15, 1904, 72) is used to correct for individual raw correlations in primary studies. The corrected raw correlations are then used to obtain the corrected z‐transformed correlations. What remains little studied, however, is how to best correct for within‐study sampling error variances of corrected z‐transformed correlations. We focused on three within‐study sampling error variance estimators corrected for measurement error that were proposed in earlier studies and is proposed in the current study: (1) the formula given by Hedges (Test validity, Lawrence Erlbaum, 1988) assuming a linear relationship between corrected and uncorrected z‐transformed correlations (linear correction), (2) one derived by the first‐order delta method based on the average of corrected z‐transformed correlations (stabilized first‐order correction), and (3) one derived by the second‐order delta method based on the average of corrected z‐transformed correlations (stabilized second‐order correction). Via a simulation study, we compared performance of these estimators and the sampling error variance estimator uncorrected for measurement error in terms of estimation and inference accuracy of the mean correlation as well as the homogeneity test of effect sizes. In obtaining the corrected z‐transformed correlations and within‐study sampling error variances, coefficient alpha was used as a common reliability coefficient estimate. The results showed that in terms of the estimated mean correlation, sampling error variances with linear correction, the stabilized first‐order and second‐order corrections, and no correction performed similarly in general. Furthermore, in terms of the homogeneity test, given a relatively large average sample size and normal true scores, the stabilized first‐order and second‐order corrections had type I error rates that were generally controlled as well as or better than the other estimators. Overall, stabilized first‐order and second‐order corrections are recommended when true scores are normal, reliabilities are acceptable, the number of items per psychological scale is relatively large, and the average sample size is relatively large. [ABSTRACT FROM AUTHOR]

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms cannot only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, the anomaly detection of quantum states encoding audio samples with a three‐qubit quantum processor consisting of solid‐state spins in diamond is experimentally demonstrated. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices. [ABSTRACT FROM AUTHOR]

We present a non‐Gaussian ensemble data assimilation method based on the maximum‐likelihood ensemble filter, which allows for any combination of Gaussian, lognormal, and reverse lognormal errors in both the background and the observations. The technique is fully nonlinear, does not require a tangent linear model, and uses a Hessian preconditioner to minimise the cost function efficiently in ensemble space. When the Gaussian assumption is relaxed, the results show significant improvements in the analysis skill within two atmospheric toy models, and the performance of data assimilation systems for (semi)bounded variables is expected to improve. [ABSTRACT FROM AUTHOR]

We present new observations of core‐diffracted shear waves which contain anomalous waveforms sampling the lowermost mantle beneath the southern Pacific region. Data in two distinct geometries, one from New Zealand to North America and the other from the Fiji and Solomon Islands to South America, show evidence of postcursor phases. The postcursor delays and move‐outs imply that they are caused by an ultra‐low velocity zone (ULVZ). Beamforming analyses of the observed diffracted postcursors show a strong backazimuthal deviation, suggesting this new ULVZ is likely to have a cylindrical shape similar to broad ULVZs sampled by shear diffracted waves elsewhere. Full‐waveform modeling suggests that the postcursors seen in North America might be due to the previously modeled ULVZ located to the west of the Galápagos, while those seen in South America are due to a previously unknown ULVZ beneath the Southern Pacific. We cannot fit observations in both geometries by a single ULVZ. For the new location, we propose one cylindrical ULVZ model with a radius of 400 km and a shear wave velocity decrease of 20% centered at geographical coordinates (−33.6, 130) close to the Pitcairn hotspot. Despite some uncertainty in the west‐east direction, this new ULVZ observation likely provides another example to support the hypothesis that ULVZs exist at the base of mantle plumes where primordial signatures are observed in the ocean island basalts. Plain Language Summary: We analyze shear waves that diffract along the core‐mantle boundary, called Sdiff, beneath the southern part of the Pacific Ocean. Sdiff waves are very sensitive to the seismic velocity structure of the lowermost mantle just above the boundary. For diffracted phases from several earthquakes and across a range of seismic stations, we observe anomalous waveform signals, which we refer to as Sdiff postcursors, and which arrive up to tens of seconds after the main Sdiff phase. We use the seismic data from many seismic stations to find from what direction the postcursor energy is coming from. We infer the cause of the anomalous postcursors are ultra‐low velocity zones (ULVZs) on the core‐mantle boundary, which are the strongest heterogeneities observed in the lower mantle. Additionally, we compute synthetic data for a range of potential ULVZ models. We find that some of the data can be explained by a previously modeled ULVZ near the Galápagos, but that other data point to a previously unknown ULVZ, beneath the southern Pacific region, although its exact location is somewhat uncertain, particularly in the west‐east direction. ULVZs, like this one, might represent the bases of upwelling mantle plumes that feed the hotspots at the surface. This particular one is in a location roughly beneath several southern Pacific hotspots, where geochemists find primordial isotope anomalies. Key Points: Detection of anomalous Sdiff postcursors sampling the core‐mantle boundary beneath the Southern PacificBeamforming and forward waveform modeling analyses find a new ultra‐low velocity zone (ULVZ) with an uncertain location beneath the southern PacificThis provides a potential new seismic example to support the correlation between surface ocean island basalts and ULVZs [ABSTRACT FROM AUTHOR]

Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions. Information about covariation can be learned from either a summary description of frequencies (i.e., contingency tables) or trial‐by‐trial experience (i.e., sampling individual instances). Two studies were conducted to investigate the impact of information learning mode (i.e., description vs. self‐truncated sampling) on covariation judgment and related decision. In each trial under the description condition, participants were presented with a contingency table with explicit cell frequencies, whereas in each trial under the self‐truncated sampling condition, participants were allowed to determine when to stop sampling instances and thus the actual sample size. To eliminate sampling error, an other‐yoked (i.e., between‐subject) design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self‐truncated sampling condition. Experiment 1 showed that the self‐truncated sampling condition led to more moderate covariation judgments than the description condition (i.e., a description–experience gap). Experiment 2 demonstrated further that the same gap extended to covariation‐related decisions in terms of relative contingent preference (RCP). Regressive frequency estimation under self‐truncated sampling appeared to underlie the consistent gaps found in the two studies, whereas the impact of regressive diagnosticity (i.e., the same sample of instances was viewed as less diagnostic under description than under self‐truncated sampling) or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data. Future research might examine alternative accounts of the observed gaps, such as the impacts of belief updating and stopping rule under self‐truncated sampling. [ABSTRACT FROM AUTHOR]

Studies of stream macroinvertebrates traditionally use sampling methods that target benthic habitats. These methods could underestimate biodiversity if important assemblage components exist outside of the benthic zone. To test the efficacy of different sampling methods, we collected paired reach‐wide benthic and edge samples from up to 10 study reaches in nine basins spanning an aridity gradient across the United States. Edge sampling targeted riparian‐adjacent microhabitats not typically sampled, including submerged vegetation, roots, and overhanging banks. We compared observed richness, asymptotic richness, and assemblage dissimilarity between benthic samples alone and different combinations of benthic and edge samples to determine the magnitude of increased diversity and assemblage dissimilarity values with the addition of edge sampling. We also examined how differences in richness and assemblage composition varied across an aridity gradient. The addition of edge sampling significantly increased observed richness (median increase = 29%) and asymptotic richness (median increase = 173%). Similarly, median Bray–Curtis dissimilarity values increased by as much as 0.178 when benthic and edge samples were combined. Differences in richness metrics were generally higher in arid basins, but assemblage dissimilarity either increased or decreased across the aridity gradient depending on how benthic and edge samples were combined. Our results suggest that studies that do not sample stream edges may significantly underestimate reach diversity and misrepresent assemblage compositions, with effects that can vary across climates. We urge researchers to carefully consider sampling methods in field studies spanning climatic zones and the comparability of existing data sets when conducting data synthesis studies. [ABSTRACT FROM AUTHOR]

In this article, we develop a new adaptive event‐triggered asymptotic control scheme for strict‐feedback systems with fast time‐varying parameters. To deal with time‐varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the feedback path and one for the uncertain parameters in the input path. In particular, two sets of tuning functions are introduced to avoid over‐parametrization. Additionally, an event‐triggering mechanism is embedded in this adaptive control framework to reduce the data transmission from the controller to the actuator. We also introduce a soft sign function to handle the perturbations caused by sampling errors to achieve asymptotic stability and avoid the so‐called parameter drift. The stability analysis shows that the closed‐loop system is globally uniformly asymptotically stable and the Zeno behavior can be excluded. Simulation results verify the effectiveness and performance of the proposed adaptive scheme. [ABSTRACT FROM AUTHOR]

In typical machine learning frameworks, model selection is of fundamental importance: commonly, multiple models have to be trained and compared in order to identify the one with the best predictive performances. The aim of this study is to provide a new tool to improve the model selection process, allowing the user to identify the algorithm which significantly outperforms the other candidates. It proposes a robust model selection procedure based on a multi‐aspect permutation test which makes it possible to detect differences in both location and variability between two paired samples of prediction errors. A new extension of the nonparametric combination (NPC) methodology is therefore introduced and is integrated with an appropriate ranking procedure in order to deal with the comparison of C≥2 candidate models. A simulation study is conducted to evaluate the performances of this testing procedure in 2‐sample and C‐sample problems, by generating data from various well‐known distributions and simulating several possible null and alternative scenarios. The adoption of the proposed technique in machine learning model selection problems is then discussed by means of multiple real data applications. These applications confirm what emerges from the simulation study: the introduced NPC‐based approach performs well under several different scenarios and represents a valuable tool for robust machine learning model selection. [ABSTRACT FROM AUTHOR]

Aim: Spatial sampling bias (SSB) is a feature of opportunistically sampled species records. Species distribution models (SDMs) built using these data (i.e. presence‐background models) can produce biased predictions of suitability across geographic space, confounding species occurrence with the distribution of sampling effort. A wide range of SSB correction methods have been developed but simulations suggest effects on predictive performance are highly variable. Here, we aim to identify the SSB correction methods that have the highest likelihood of improving true predictive performance and evaluation strategies that provide a reliable indicator of model performance when independent test data are unavailable. Location: Global, simulation. Time Period: Current, simulation. Methods: A meta‐analysis was used to evaluate the performance of SSB correction methods in studies where there were direct comparisons between corrected and uncorrected SDMs. A simulation model was then developed to test evaluation strategies against a known truth using four common SSB correction methods. Results: Effect sizes from published studies suggest some support for small positive effects of SSB correction on predictive performance when assessed using independent test data, but this was not evident using internal cross‐validation and no single method stood out as consistently effective. Simulations support these findings and show that evaluation using internal test data was generally a poor indicator of the true effect of SSB correction. Methods that adjust models relative to a known driver of SSB produced the largest performance gains, but were also the most inconsistent. Main Conclusions: Correcting SSB in presence‐background SDMs without independent test data to evaluate the effect on model performance requires careful implementation. We recommend clearer documentation of SSB correction effects on SDMs, presenting results from models with and without correction, evaluating effects of different assumptions of SSB implementation on predictions, as well as greater efforts to collect independent test datasets to validate model predictions. [ABSTRACT FROM AUTHOR]

To improve the pulse width modulation (PWM) harmonic suppression effect of the three‐phase VSI‐fed PMSM system, this paper proposed a novel random switching period (RSP)‐Space Vector Pulse Width Modulation (SVPWM) technique for the three‐phase power converter. First, the proposed technique replaces the 10‐bit linear feedback shift register pseudorandom number generator (LFSR‐PRNG) with a Tiny Mersenne Twister (MT)‐PRNG to quickly generate high‐quality pseudorandom number sequences. Then, to reduce the sampling error caused by the current ripple, this paper analyzes the sampling timing under different modulation techniques. Based on the interrupted update mechanism of the Infineon AURIX‐TC264, the variable frequency sampling of the RSP‐SVPWM technique is implemented to ensure the best sampling of the motor phase current. Finally, the simulation and experimental results show that the proposed technique can effectively disperse PWM harmonic energy. Meanwhile, the RSP‐SVPWM technique based on variable sampling frequency effectively improves the phase current quality of the PMSM system. The proposed technique shows great potential for suppressing PWM harmonics in integrated PMSM systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

For Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and "flow‐dependent" error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors. Plain Language Summary: Paleoclimate data assimilation (PDA) combines information from the climate simulations and proxy data, aiming to provide an optimal estimate of past climates. The relatively low predictive skill of the climate model and the noisy and sparse paleoclimate proxies have imposed great challenges on PDA. Previous studies have shown that offline ensemble assimilation methods are much more computationally efficient than online ensemble assimilation ones, and the offline analog ensemble can also provide "flow‐dependent" prior error covariances. However, the sample prior error covariances estimated from an ensemble with limited members can be contaminated by sampling errors. Hybridization of the "flow‐dependent" prior error covariances with the static ones can remedy the sampling error, and also infuse better representation of large‐scale error correlations from the static ones. Thus a hybrid gain analog offline ensemble Kalman filter (EnKF) is proposed, which retains the advantages of the analog ensemble and further adapts the benefits of static prior error covariances. The hybrid gain analog offline EnKF is superior to the traditional offline EnKF and the offline EnKF with analog ensembles. Key Points: Static B using sufficiently large samples can better capture large‐scale error correlations and mitigate sampling errors than the sampled BHybrid gain analog offline ensemble Kalman filter (HGAOEnKF) uses the ensemble mean prior from the analog ensemble and hybrid gains combining the static B and "flow‐dependent" BHGAOEnKF has advantages over OEnKF and analog offline ensemble Kalman filter with varying sample sizes and presence of model errors, especially for extreme years [ABSTRACT FROM AUTHOR]

The method to estimate contemporary effective population size (Ne) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy. [ABSTRACT FROM AUTHOR]

Meta-analysis is a statistical procedure that combines the results of multiple scientific studies on the same topic. It allows for a more precise estimation of effects and identification of general trends in research. However, there are common errors that researchers make when conducting meta-analyses. These include misinterpreting the I-squared value, not properly examining outliers, using the wrong measures for effect size, not accounting for correlated observations, including studies with small sample sizes, and failing to conduct sensitivity and asymmetry analyses. These errors can lead to incorrect conclusions and have serious consequences in scientific research and clinical practice. It is recommended to work with a biostatistician to ensure the validity and appropriateness of statistical analysis methods used in meta-analyses. [Extracted from the article]

Interest has grown in synthesizing participant level data of a study with relevant external aggregate information. Several efficient and flexible procedures have been developed under the assumption that the internal study and the external sources concern the same population. This homogeneity condition, albeit commonly being imposed, is hard to check due to limitedly available external information in aggregate data forms. Bias may be introduced when the assumption is violated. In this article, we propose a penalized likelihood approach that avoids undesirable bias by simultaneously selecting and synthesizing consistent external aggregate information. The proposed approach provides a general framework which incorporate consistent external information from heterogeneous study populations as long as the conditional distribution of the dependent variable under investigation is same and differences in the independent variable distributions are properly accounted for via a semi‐parametric density ratio model. The proposed approach also properly accounts for the sampling errors in the external information. A two‐step estimator and an optimization algorithm are proposed for computation. We establish the selection and estimation consistency and the asymptotic normality of the two‐step estimator. The proposed approach is illustrated with an analysis of gestational weight gain management studies. [ABSTRACT FROM AUTHOR]

A data driven method based on generalized additive modelling (GAM) has been developed for homogenizing daily minimum and maximum temperature (TN, TX) series using parallel measurements and covariates. The method is applied to two coastal and two inland stations in the Netherlands. Between 1950 and 1972, these stations were relocated from cities to airports, accompanied by parallel measurement of at least 5 years at the old and new sites. Separating these parallel measurements in training and test data, the method compares numerous models involving covariates like the wind vector, cloudiness, specific humidity and sea surface temperature, and selects a model for each station. The resulting models offer an improvement compared to models based on temperature and season only: seasonal dependence is largely replaced by dependence on physical quantities. However, quantitatively, the impact is not large in the cases studied. One of the reasons might be that some covariates have only been measured at specific times not coinciding with the occurrences of the temperature minima or maxima. Additional benefits of the method are robustness and estimation of the sampling error variance of the daily homogenized daily temperature values. [ABSTRACT FROM AUTHOR]

In this article, we focus on solving a sequence of linear systems that have identical (or similar) coefficient matrices. For this type of problem, we investigate subspace correction (SC) and deflation methods, which use an auxiliary matrix (subspace) to accelerate the convergence of the iterative method. In practical simulations, these acceleration methods typically work well when the range of the auxiliary matrix contains eigenspaces corresponding to small eigenvalues of the coefficient matrix. We develop a new algebraic auxiliary matrix construction method based on error vector sampling in which eigenvectors with small eigenvalues are efficiently identified in the solution process. We use the generated auxiliary matrix for convergence acceleration in the following solution step. Numerical tests confirm that both SC and deflation methods with the auxiliary matrix can accelerate the solution process of the iterative solver. Furthermore, we examine the applicability of our technique to the estimation of the condition number of the coefficient matrix. We also present the algorithm of the preconditioned conjugate gradient method with condition number estimation. [ABSTRACT FROM AUTHOR]

Data deficiencies among rare or cryptic species preclude assessment of community‐level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures.A recent surge in both public science and government‐funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well‐structured, design‐based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis.Hierarchical modelling, including single‐species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species‐ and community‐level dynamics.We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community‐level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula‐based interfaces and through development of custom code in JAGS, NIMBLE and Stan.Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community‐level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity. [ABSTRACT FROM AUTHOR]

Although it is widely known that observations of aeolian sediment transport are susceptible to large sampling errors, sample designs are frequently used that do not sufficiently reduce the measurement uncertainties inherent in the study of aeolian processes. Here, we examine the influence of sample size (n) and sampling location on uncertainty in models of aeolian sediment transport responses to vegetation cover change. We compare measurements from a stratified random array of 27 horizontal sediment mass flux samplers to vegetative cover data collected at a 1 ha site over a period of nearly 6 years. To assess the sensitivity of modeled relationships between aeolian transport and vegetative cover to sample design, we analyze statistical regressions for all possible combinations of sample size and sampler locations. We show that at least 17 randomly located samplers are needed to consistently capture the sediment mass flux response to vegetative cover change. We found that multiple statistically significant models can describe the sediment flux‐vegetative cover relationship when using smaller sample sizes, demonstrating the risks of inferring sediment transport response from an underpowered sample design. Across vegetative functional groups, we found that woody cover generally influenced aeolian sediment transport rates more than herbaceous cover, while model uncertainty at large sample sizes (n > 17) showed the limitation of using vegetative cover as an indicator of aeolian sediment transport. Our results suggest an evaluation of sampling practices in aeolian sediment transport studies may be needed to avoid inferential errors that are likely pervasive in this field of study. Plain Language Summary: Studies of aeolian sediment transport are prone to sampling errors, largely due to the many factors that control the transport process. However, researchers often look to simplify sample designs and monitoring methods as monitoring is time consuming and expensive. To that end, vegetative cover is an easily collected and interpreted environmental indicator that affects the rates of aeolian sediment transport. However, little consideration has been given to how many samples are needed to confidently model the relationship between changes in vegetative cover and sediment transport. We investigate how changes in sample size, modeling approaches, and vegetative cover may affect inferences made about aeolian sediment transport. We show that using traditional approaches in aeolian research (using small sample sizes and relatively simple environmental data) could risk false conclusions being drawn about aeolian transport processes, even with models that appear to be accurate. If sampling does not adequately describe the sediment transport "population" response, variability in field measurements can be so large that it undermines our understanding of how and why aeolian sediment transport rates change over time. These findings may require a re‐evaluation of typical sediment transport sampling practices. Key Points: Heterogeneity in soil, vegetation, and wind cause aeolian sediment flux to have large spatiotemporal variabilityModels describing change in aeolian sediment fluxes with ground cover are highly sensitive to sampling errorLarge sample sizes and a variety of environmental indicators are needed to produce consistent models that can inform wind erosion management [ABSTRACT FROM AUTHOR]

We propose a parametric sampling strategy for reduction of large scale PDE systems with multidimensional input parametric spaces by leveraging models of different fidelity. The design of this methodology allows a user to adaptively sample points ad hoc from a discrete training set with no prior requirement of error estimators. It is achieved by exploiting low‐fidelity models throughout the parametric space to sample points using an efficient sampling strategy, and at the sampled parametric points, high‐fidelity models are evaluated to recover the reduced basis functions. The low‐fidelity models are then adapted with the reduced order models built by projection onto the subspace spanned by the recovered basis functions. The process continues until the low‐fidelity model can represent the high‐fidelity model adequately for all the parameters in the parametric space. Since the proposed methodology leverages the use of low‐fidelity models to assimilate the solution database, it significantly reduces the computational cost in the offline stage. The highlight of this article is to present the construction of the initial low‐fidelity model, and a sampling strategy based on the discrete empirical interpolation method. We test this approach on a 2D steady‐state heat conduction problem for two different input parameters and make a qualitative comparison with the classical greedy reduced basis method and with random selection of points. Further, we test the efficacy of the proposed method on a 9‐dimensional parametric non‐coercive elliptic problem and analyze the computational performance based on different tuning of greedy selection of points. [ABSTRACT FROM AUTHOR]

Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error‐prone tests. This results in naïve estimators of prevalence (ie, proportion of observed infected individuals in the sample) that can be very far from the true proportion of infected. In this work, we present a method of prevalence estimation that reduces both the effect of bias due to testing errors and oversampling of symptomatic individuals, eliminating it altogether in some scenarios. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. This results in easily implementable algorithms, for which code is provided, that produce better prevalence estimates than other methods (in terms of reducing and/or removing bias), as demonstrated by formal results, simulations, and on COVID‐19 data from the Israeli Ministry of Health. [ABSTRACT FROM AUTHOR]

Rigorous evaluation of surrogate endpoints is performed in a trial‐level analysis in which the strength of the association between treatment effects on the clinical and surrogate endpoints is quantified across a collection of previously conducted trials. To reduce bias in measures of the performance of the surrogate, the statistical model must account for the sampling error in each trial's estimated treatment effects and their potential correlation. Unfortunately, these within‐study correlations can be difficult to obtain, especially for meta‐analysis of published trial results where individual patient data is not available. As such, these terms are frequently partially or completely missing in the analysis. We show that improper handling of these missing terms can meaningfully alter the perceived quality of the surrogate and we introduce novel strategies to handle the missingness. [ABSTRACT FROM AUTHOR]

When the indicators of the HT ht do not have predefined metrics, we can rewrite equation (8) as 9 HT ht where the HT ht are arbitrary positive numbers. [Extracted from the article]

Aim: Communities contain more individuals of small species and fewer individuals of large species. According to the 'metabolic theory of ecology', the relationship of log mean abundance with log mean body size across communities should exhibit a slope of −3/4 that is invariant across environmental conditions. Here, we investigate whether this slope is indeed invariant or changes systematically across gradients in temperature, resource availability and predation pressure. Location: 1048 lakes across the USA. Time Period: 2012. Major Taxa Studied: Phytoplankton. Results: We found that the size–abundance relationship across all sampled phytoplankton communities was significantly lower than −3/4 and near −1 overall. More importantly, we found strong evidence that the environment affects the slope: it varies between −0.33 and −0.93 across interacting gradients of temperature, resource (phosphorus) supply and zooplankton predation pressure. Therefore, phytoplankton communities have orders of magnitude more small or large cells depending on environmental conditions across geographical locations. Conclusion: Our results emphasise the importance of the environmental factors' effect on macroecological patterns that arise through physiological and ecological processes. An investigation of the mechanisms underlying the link between individual energetics constrain and macroecological patterns would allow to predict how global warming and changes in nutrients will alter large‐scale ecological patterns in the future. [ABSTRACT FROM AUTHOR]

We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical p$$ p $$‐value. A recalibration is proposed to obtain exact overall Type‐I error control if the effect is null in both studies and additional bounds on the partial and conditional Type‐I error rate, which represent the case where only one study has a null effect. The approach avoids the double dichotomization for significance of the two‐trials rule and has larger project power to detect existing effects over both studies in combination. It can also be used for power calculations and requires a smaller replication sample size than the two‐trials rule for already convincing original studies. We illustrate the performance of the proposed methodology in an application to data from the Experimental Economics Replication Project. [ABSTRACT FROM AUTHOR]

We propose a demand estimation method that allows for a large number of zero‐ sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market‐level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand. [ABSTRACT FROM AUTHOR]

Flow‐dependent background error covariances estimated from short‐term ensemble forecasts suffer from sampling errors due to limited ensemble sizes. Covariance localization is often used to mitigate the sampling errors, especially for high dimensional geophysical applications. Most applied localization methods, empirical or adaptive ones, multiply the Kalman gain or background error covariances by a distance‐dependent parameter, which is a simple linear filtering model. Here two localization methods based on convolutional neural networks (CNNs) learning from paired data sets are proposed. The CNN‐based localization function (CLF) aims to minimize the sampling error of the estimated Kalman gain, and the CNN‐based empirical localization function (CELF) aims to minimize the posterior error of state variables. These two CNN‐based localization methods can provide localization functions that are nonlinear, spatially and temporally adaptive, and non‐symmetric with respect to displacement, without requiring any prior assumptions for the localization functions. Results using the Lorenz05 model show that CLF and CELF can better capture the structures of the Kalman gain than the best Gaspari and Cohn (GC) localization function and the adaptive reference localization method. For both perfect‐ and imperfect‐model experiments, CLF produces smaller errors of the Kalman gain, prior and posterior than the best GC and reference localization, especially for spatially averaged observations. Without model error, CELF has smaller prior and posterior errors than the best GC and reference localization for spatially averaged observations, while with model error, CELF has smaller prior and posterior errors than the best GC and reference localization for single‐point observations. Plain Language Summary: Ensemble Kalman filters have been widely used for high‐dimensional geophysical applications, with the advantages to provide flow‐dependent background error covariances based on short‐term ensemble forecasts. Due to the massive computational costs for advancing ensemble simulations, limited ensemble members are commonly adopted, which results in sample‐estimated background error covariances contaminated by spurious noisy correlations. To remedy the sampling error, covariance localization that tapers the observation impact on state variables with distance, is commonly used. There are pre‐defined localization functions with tuning parameters and adaptive localization functions that are often based on correlation statistics. Here two purely data‐driven localization methods based on convolutional neural networks are proposed. These newly proposed localization functions are spatially and temporally adaptive, non‐symmetric with respect to displacement, with better captured structures of the Kalman gain than the empirical and adaptive localization methods. When they are applied in cycling assimilation, the localization methods based on the convolutional neural networks can produce improved analyses and forecasts. Key Points: Two CNN‐based localization methods are proposed to minimize sampling errors of estimated Kalman gain or posterior errors of state variablesCNN‐based localizations are adaptive in space and time, non‐symmetric with respect to displacement, and able to fit nonlinear functionsCNN‐based localizations effectively represent the Kalman gain, and lead to improved analyses and forecasts in cycling assimilations [ABSTRACT FROM AUTHOR]

High‐frequency (HF) square‐wave voltage injection position sensorless control method for interior permanent magnet synchronous motor (IPMSM) is widely utilised in zero and low speed range due to its good dynamic performance and easy implementation. However, this method relies on the sampling accuracy of current sensors for rotor position estimation. To overcome this restriction, an HF square‐wave voltage injection position sensorless control method for IPMSM using a single current sensor (SCS) is proposed. Firstly, the impact of current sampling errors on HF square‐wave voltage injection position sensorless control is analysed, and it is concluded that the scaling errors of current sensors will cause the estimated position to oscillate at twice the fundamental frequency. Based on this conclusion, the phase currents reconstruction technology with SCS is adopted to avoid the impact of scaling errors on rotor position estimation. To reconstruct the phase currents containing HF component, a PWM cycle is divided into two parts, sampling stage and injection stage. By this way, the impact of HF square‐wave voltage injection on current reconstruction can be avoided. Then, the rotor position estimation is realised. The experiments are performed on a 20‐kW IPMSM platform and the results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single samples, referred to as "superstations," before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the individual sites. This bias may be large or small compared to the sampling error, for realistic record lengths, depending on the distribution of the quantity analyzed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability. Plain Language Summary: We report a previously unknown bias in a common method for estimating how often extremely rare events such as extreme wind bursts or rain events will occur, when return periods are longer than the available data record. The method analyzed is one where an investigator combines data from nearby locations to reduce sampling error. We find by looking at new, high‐resolution data that variations in behavior across sites can sometimes produce biases much larger than the sampling error. The implication is that some observed extreme events are even less likely to have occurred than previously thought, assuming the underlying distribution hasn't changed over the period of observation. Key Points: Grouping data of nearby locations into one larger sample or "superstation" can induce biases at long recurrence intervalsThe superstation fit tends to the highest levels suggested by any of the pooled locationsThe bias may be large or small compared to random uncertainty, depending on the distribution of the extreme event analyzed [ABSTRACT FROM AUTHOR]

Endangered species, despite often living at low population densities, may undergo unexpected density‐dependent feedbacks in the case of successful recovery or marked reduction in range. Because density‐dependence dynamics can increase risk of extinction, these effects can hamper conservation efforts. In this study, we analyze the dynamics of the largest population of the tamaraw Bubalus mindorensis, a critically endangered ungulate species endemic to Mindoro island, Philippines. The population is located within a <3000 ha area in Mounts Iglit‐Baco Natural Park, with limited expansion possibilities. We took advantage of a 22 year time series of tamaraw counts to estimate annual population growth rate and possible density‐dependence, accounting for sampling errors in the counts. The tamaraw population has been increasing at an average rate of +5% per year, as would be expected given its protected status by law. Population growth showed strong spatial structuring, with a population growth close to +10% in the core area of protection, and a reduction of abundance of −5% at the periphery of its range, inside the protected area. This range constriction is concerning because our best population dynamics model suggests significant negative density‐dependence (Bayes factor = 0.9). The contraction of tamaraw range is likely caused by anthropogenic pressures forcing the species to live at relatively high densities in the core zone where protection is most effective, creating source‐sink dynamics. Our study highlights the fact that, despite the continuous population growth over the last two decades, the long‐term viability of the Mounts Iglit‐Baco Natural Park tamaraw population remains uncertain. [ABSTRACT FROM AUTHOR]

GPS‐based tracking is widely used for studying wild social animals. Much like traditional observational methods, using GPS devices requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across more distinct social groups may not be sufficient to infer group‐ or subgroup‐level behaviours, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations.Here, we provide quantitative recommendations when designing GPS‐based tracking studies of animal societies. We focus on the trade‐offs between three fundamental axes of sampling effort: (1) sampling coverage—the number and allocation of GPS devices among individuals in one or more social groups; (2) sampling duration—the total amount of time over which devices collect data and (3) sampling frequency—the temporal resolution at which GPS devices record data.We first test GPS tags under field conditions to quantify how these aspects of sampling design can affect both GPS accuracy (error in absolute positional estimates) and GPS precision (error in the estimate relative position of two individuals), demonstrating that GPS error can have profound effects when inferring distances between individuals. We then use data from whole‐group tracked vulturine guineafowl Acryllium vulturinum to demonstrate how the trade‐off between sampling frequency and sampling duration can impact inferences of social interactions and to quantify how sampling coverage can affect common measures of social behaviour in animal groups, identifying which types of measures are more or less robust to lower coverage of individuals. Finally, we use data‐informed simulations to extend insights across groups of different sizes and cohesiveness.Based on our results, we are able to offer a range of recommendations on GPS sampling strategies to address research questions across social organizational scales and social systems—from group movement to social network structure and collective decision‐making.Our study provides practical advice for empiricists to navigate their decision‐making processes when designing GPS‐based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions. [ABSTRACT FROM AUTHOR]

Ship present‐weather reports from 1950 to 2019 are used to assess trends in the reporting of precipitation occurrence over the global oceans. Annual reported precipitation frequency shows statistically significant positive trends of up to ∼15% per decade throughout most ocean areas equatorward of 45°. However, latitudes poleward of 45° are dominated by negative trends, some areas of which meet the 95% confidence threshold. Nine smaller regions were subjectively selected for further investigation, revealing that the observed trends, both positive and negative, are often but not always nearly linear, with the amplitude of interannual fluctuations usually being much larger than that expected from random sampling error alone. The annual time series reveal that four comparatively dry areas are associated with the largest overall positive trends, ranging from 8.3% to 12.8% (relative) per decade. Trends were also computed separately for each season, revealing remarkable overall consistency in trends across seasons. Plain Language Summary: Starting with 70 years (1950–2019) of archived ship weather reports from around the world's oceans, we tabulated the frequencies of reports of precipitation as a fraction of the total weather reports on a 1° latitude/longitude grid for each season and year. We then composited these to coarser resolution where necessary to have an adequate sample size. Finally, we computed trends in reported precipitation frequency over the seven‐decade period and tested the trends for statistical significance at the 95% confidence level. We found that statistically significant trends in apparent precipitation frequency affect large areas of the oceans, with positive trends dominating in the warmer and especially drier areas and negative trends dominating at higher latitudes. Key Points: Significant long‐term trends are found in ship reports of precipitation occurrenceTrends are mostly positive equatorward of 45° and negative at higher latitudesReporting biases that could explain the trends cannot be ruled out but have not been identified [ABSTRACT FROM AUTHOR]

The highly accurate state estimation of nonlinear systems is needed in many different application fields. In many existing nonlinear filters, the uncompensated nonlinear approximation error and widely used numerical sampling methods both seriously affect the estimation accuracy and numerical stability. In this paper, based on the variational Bayesian framework, a novel iterative nonlinear filter consisting of forward modeling and inverse estimation is proposed. In the forward modeling stage, a linear Gaussian regression process with distributed variational parameters (VPs) is designed to fit the whole nonlinear measurement likelihood. The distributed VPs consider both first and second moments information, so as to compensate for approximation error from the whole likelihood perspective, instead of only the nonlinear function approximation, which can further improve the accuracy of Bayesian posterior update. In the linear Gaussian regression process, the original nonlinear measurement function (NMF) is just viewed as a prior and no longer directly participates in the modeling of the measurement likelihood. Hence, in the inverse estimation stage, when maximizing the evidence lower bound to update the posteriors of the state and VPs, we can obtain closed‐form solutions without numerical sampling methods to approximate the integral of NMF required in many existing methods, which can guarantee numerical stability. Moreover, to avoid the random setting of hyperparameters and to block the propagation of the accumulated error, we propose two rules, estimation and lower bound consistencies, to conduct the hyperparameters' initialization at the beginning of iteration at each sampling time and also derive an optimization method to update hyperparameters during iteration. Finally, the performance of our proposed method is demonstrated in the nonlinear orbital estimation problems. [ABSTRACT FROM AUTHOR]

Early diagnosis of allograft rejection helps to improve the immune‐related management of transplant recipients. The clinically‐used core needle biopsy method is invasive and subject to sampling error. In vivo fluorescence imaging for monitoring immune‐related processes has the advantages of non‐invasiveness, fast feedback and high sensitivity. Herein, we report a responsive second near‐infrared (NIR‐II) fluorescent nanosensor (ErGZ) to detect early allograft rejection. ErGZ allows ratiometric in vivo fluorescence sensing of granzyme B, which is overexpressed in recipients' T cells during the onset of rejection. The sensor demonstrates efficacious detection of allograft rejection with high sensitivity and specificity, which accomplishes non‐invasive diagnosis of rejection in skin and deep buried islets transplant mice models 2 d and 5 d earlier than biopsy, by in vivo fluorescence imaging and urinary detection, respectively, providing a valuable approach for therapeutical management. [ABSTRACT FROM AUTHOR]