Component-scaled signal reconstruction for enhanced noise filtration

This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

Robust historical evapotranspiration trends across climate regimes

Evapotranspiration (ET) links the hydrological, energy, and carbon cycle on the land surface. Quantifying ET and its spatiotemporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observationally-based gridded ET datasets, and while many have been developed using physically-based, empirically-based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive a new version of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatiotemporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal reach (1980–2018). After successful evaluation of the efficacy of these uncertainty estimates out-of-sample, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions, these do not correlate with behavioural ET clusters. Each cluster, and the vast majority of the Earth's surface, show clear robust increases in ET over the recent historical period.

A Hybrid Adaptive Transaction Injection Protocol and Its Optimization for Verification-Based Decentralized System

Latency is a critical issue that impacts the performance of decentralized systems. Recently we designed various protocols to regulate the injection rate of unverified transactions into the system to improve system performance. Each of the protocols is designed to address issues related to some particular network traffic syndrome. In this work, we first provide the review of our prior protocols. We then provide a hybrid scheme that combines our transaction injection protocols and provides an optimal linear combination of the protocols based on the syndromes in the network. The goal is to speed up the verification process of systems that rely on only one single basic protocol. The underlying basic protocols are Periodic Injection of Transaction via Evaluation Corridor (PITEC), Probabilistic Injection of Transactions (PIT), and Adaptive Semi-synchronous Transaction Injection (ASTI).

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

Calm Multi-Baryon Operators

There are many outstanding problems in nuclear physics which require input and guidance from lattice QCD calculations of few baryons systems. However, these calculations suffer from an exponentially bad signal-to-noise problem which has prevented a controlled extrapolation to the physical point. The variational method has been applied very successfully to two-meson systems, allowing for the extraction of the two-meson states very early in Euclidean time through the use of improved single hadron operators. The sheer numerical cost of using the same techniques in two-baryon systems has so far been prohibitive. We present an alternate strategy which offers some of the same advantages as the variational method while being significantly less numerically expensive. We first use the Matrix Prony method to form an optimal linear combination of single baryon interpolating fields generated from the same source and different sink interpolating fields. Very early in Euclidean time this optimal linear combination is numerically free of excited state contamination, so we coin it a calm baryon. This calm baryon operator is then used in the construction of the two-baryon correlation functions.To test this method, we perform calculations on the WM/JLab iso-clover gauge configurations at the SU(3) flavor symmetric point with mπ~ 800 MeV — the same configurations we have previously used for the calculation of two-nucleon correlation functions. We observe the calm baryon significantly removes the excited state contamination from the two-nucleon correlation function to as early a time as the single-nucleon is improved, provided non-local (displaced nucleon) sources are used. For the local two-nucleon correlation function (where both nucleons are created from the same space-time location) there is still improvement, but there is significant excited state contamination in the region the single calm baryon displays no excited state contamination.

The Total Score With Maximal Reliability and Maximal Criterion Validity: An Illustration Using a Career Satisfaction Measure

The maximal reliability of a congeneric measure is achieved by weighting item scores to form the optimal linear combination as the total score; it is never lower than the composite reliability of the measure when measurement errors are uncorrelated. The statistical method that renders maximal reliability would also lead to maximal criterion validity. Using a career satisfaction measure as an example, the present article calculated the maximal reliability and maximal criterion validity and compared them with the composite reliability and the scale criterion validity, respectively. The improvement of reliability and validity indicated that the optimal linear combination is preferred when forming a total score of a measure. The Mplus codes for analyzing maximal reliability, maximal criterion validity, and related parameters are provided.

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, as well as being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data, and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in three common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.