Motion tomography via occupation kernels

<p style='text-indent:20px;'>The goal of motion tomography is to recover a description of a vector flow field using measurements along the trajectory of a sensing unit. In this paper, we develop a predictor corrector algorithm designed to recover vector flow fields from trajectory data with the use of occupation kernels developed by Rosenfeld et al. [<xref ref-type="bibr" rid="b9">9</xref>,<xref ref-type="bibr" rid="b10">10</xref>]. Specifically, we use the occupation kernels as an adaptive basis; that is, the trajectories defining our occupation kernels are iteratively updated to improve the estimation in the next stage. Initial estimates are established, then under mild assumptions, such as relatively straight trajectories, convergence is proven using the Contraction Mapping Theorem. We then compare the developed method with the established method by Chang et al. [<xref ref-type="bibr" rid="b5">5</xref>] by defining a set of error metrics. We found that for simulated data, where a ground truth is available, our method offers a marked improvement over [<xref ref-type="bibr" rid="b5">5</xref>]. For a real-world example, where ground truth is not available, our results are similar results to the established method.</p>

Review of Different Error Metrics: A Case of Solar Forecasting

Renewable energy systems (RES) are no longer confined to being used as a stand-alone entity in the modern era. These RES, especially solar panels are also used with the grid power systems to supply electricity. However, precise forecasting of solar irradiance is necessary to ensure that the grid operates in a balanced and planned manner. Various solar forecasting models (SFM) are presented in the literature to produce an accurate solar forecast. Nevertheless, each model has gone through the step of evaluation of its accuracy using some error measures. Many error measures are discussed in the literature for deterministic as well as probabilistic solar forecasting. But, each study has its own selected error measure which sometimes landed on a wrong interpretation of results if not selected appropriately. As a result, this paper offers a critical assessment of several common error metrics with the goal of discussing alternative error metrics and establishing a viable set of error metrics for deterministic and probabilistic solar forecasting. Based on highly cited research from the last three years (2019-2021), error measures for both types of forecasting are presented with their basic functionalities, advantages & limitations which equipped the reader to pick the required compatible metrics

Application of Machine Learning Techniques to Estimate Unsoaked California Bearing Ratio in Ekiti Central Senatorial District

This paper investigates the relationship between soil physical properties and the Un-soaked California Bearing Ratio (USCBR) of soil found in Ekiti State Central Senatorial District (ESCSD), which includes Natural Moisture Content (NMC%) Percentage Fines, Specific Gravity (SG) and Consistency Limits (LL%, PL%, & PI %). The database was prepared in the laboratory by conducting tests on ninety-nine (99) soil samples which were obtained in a burrowed pit found in the Central Senatorial District of Ekiti State. An R version 4.0.5 and R studio version 1.2.5033 was used to analyze the Artificial Neural Networks (ANNs) and Least Square Regression (LSR) in order to develop a simplified CBR model. In both models, independent layer containing six nodes (soil physical properties) and the dependent layer containing a single node (i.e. CBR) were taken. The descriptive analysis for training and testing was performed; boxplots of the variables were plotted and; sensitivity analysis was carried out. The capacity of the developed equation was evaluated in terms of error metrics MSE and RMSE. The analysis showed that both ANN and MLR models predicted CBR close to the laboratory value. However, the model without the percentage passing sieve 200 (MIC) is the best, having Akaike Information Criterion and Bayesian Information Criterion values of 614.1707 and 627.5754 respectively, from the error metrics analysis, the results showed that PL and LL are the most influential variable that affects the developed CBR model's output. From the foregoing its concluded that the study has shown a relationship between the CBR value of Ekiti Central Senatorial District soil and its basic soils properties using machine learning techniques, also the developed CBR model will be useful tool to Civil engineers, geotechnical engineers and construction industry within the study area particularly in their preliminary stage of their project.

Gate-Level Static Approximate Adders: A Comparative Analysis

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

Validation of Wind Resource and Energy Production Simulations for Small Wind Turbines in the United States

Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, and MyWindTurbine.com, which are seen to overestimate actual annual capacity factors by 2.5, 4.2, and 11.5 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produce more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

Cross-Domain Topic Classification for Political Texts

We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics.

Comparison of traditional method and triple collocation analysis for evaluation of multiple gridded precipitation products across Germany

Evaluating the accuracy of precipitation products is essential for many applications. The traditional method for evaluation is to calculate error metrics of products with gauge measurements that are considered as ground-truth. The multiplicative triple collocation (MTC) method has been demonstrated powerful in error quantification of precipitation products when ground-truth is not known. This study applied MTC to evaluate five precipitation products in Germany: two raw satellite-based (CMORPH and PERSIANN), one reanalysis (ERA-Interim), one soil moisture-based (SM2RAIN-ASCAT), and one gauge-based (REGNIE) products. Evaluation was performed at the 0.5° -daily spatial-temporal scales. MTC involves a log transformation of data, necessitating dealing with zero values in daily precipitation. Effects of 12 different strategies for dealing with zero value on MTC results were investigated. Seven different triplet combinations were tested to evaluate the stability of MTC. Results showed that different strategies for replacing zero values had considerable effects on MTC-derived error metrics particularly for root mean squared error (RMSE). MTC with different triplet combinations generated different error metrics for individual products. MTC-derived correlation coefficient (CC) was more reliable than RMSE. It is more appropriate to use MTC to compare the relative accuracy of different precipitation products. Based on CC with unknown truth, MTC with different triplet combinations produced the same ranking of products as the traditional method. A comparison of results from MTC and the classic TC with additive error model showed the potential limitation of MTC in arid area or dry time periods with large ratio of zero daily precipitation.

Polygenic score accuracy in ancient samples: quantifying the effects of allelic turnover

Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the correlation between an ancient individual’s polygenic score and true phenotype, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.