Underestimation Problem Research Articles

Belarus in the Eurasian Migration System: The Challenges of the Last Decade and Their Consequences

This article presents the main recent developments in migration trends to and from Belarus. It studies these trends via the migration systems theory lenses, according to which Belarus belongs to the Eurasian migration system. The most significant migration flows are directed towards Russia, due to the existence of the Union State. However, over the last decade, Belarusian statistics have shown a gradual transformation in the direction of these migration flows. After the recession in Russia in 2015, the number of emigrants from Belarus to EU countries increased. The most significant changes have occurred in the migration dynamics between Belarus and Poland and Lithuania. The existence of the Pole’s Card makes it more difficult to measure the number of Belarusian immigrants in Poland, therefore, I provide a comparative analysis of Belarusian and Polish statistics in order to show a more realistic picture of the number and structure of Belarusian emigrants and the problem of underestimation in the sending country. Particular attention is paid to the consequences of the political situation in Belarus after 2020; this has become an additional push factor for emigrants and may also lead to a further reduction of Belarusian migratory links within the Eurasian migration system. Thus, the statistics for 2021 show a significant increase in the number of Belarusian emigrants to the EU, while emigration to Russia has remained at the same level.

Improvement of a Near-Real-Time Precipitation Estimation Algorithm Using Deep Learning

Near-real time estimation of precipitation from geostationary satellites plays a vital role in natural disaster mitigation due to timely monitoring, high spatial-temporal resolution and large coverage, yet this research remains a large challenge. In this research, a novel Deep Learning-based algorithm entitled Precipitation Estimation using a Multi-Scale network (DLPE-MS) is proposed to estimate precipitation during summer over eastern Continental United States (CONUS) of America. When inputting bispectral satellite information (10.3 μm and 6.2 μm), this algorithm provides near-real time rainfall rates at hourly and 0.04°×0.04° resolution. In order to emphasize the information of a precipitation region at different scales using satellites’ data, we design a multi-scale framework based on convolutional neural networks (CNNs). In addition, a loss function named Balanced Weights Mean Square Error (BWMSE) is proposed to settle the problem of underestimation caused by a shortage in heavy rainy samples. Compared with the Mean Square Error (MSE), the BWMSE has more balance parameters for different objects when training, which is able to mitigate the deviation between the prediction and ground truth in tailed categories (heavy rainy). Results show that this algorithm achieves the highest Probability of Detection (POD) and Correlation Coefficient (CC) with the value of 95.5% and 0.5. The statistical results of the precipitation cases also show that the DLPE-MS can significantly improve the estimated values in tailed categories than other products and methods. After testing, this algorithm is able to estimate the precipitation for the study area within 0.19 seconds.

A Reweighted Overlapping Group Shrinkage Method for Bearing Fault Diagnosis

Sparse regularization has received widespread attention in bearing fault diagnosis as it overcomes the limitation of the artificially constructed dictionary. However, the amplitude underestimation caused by different penalty functions reduces the precision of extracted fault features. To handle this difficulty, a Reweighted Overlapping Group Shrinkage (R-OGS) method for bearing fault diagnosis is proposed in this paper. The group property of the fault signal and the amplitude underestimation problem are considered simultaneously. A novel group weighting coefficient based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the group is designed to mitigate the amplitude compression caused by the group penalty. Although a non-convex penalty function is used to improve the performance, the cost function can be convex with appropriate parameter selection. The convexity condition is given and the solution algorithm is derived based on the Majorization-Minimization (MM) algorithm. The setting strategy of the group size and the regularization parameter is given after in-depth analysis. Numerical simulation shows that the R-OGS method has stronger amplitude retention of fault feature signal and noise suppression capability than previous Overlapping Group Shrinkage (OGS). The bearing fault experiment signals verify that the proposed R-OGS method has superior performance in fault feature extraction compared to the Moreau-enhanced Total Variation Denoising (MTVD) and the Fast Kurtogram (FSK).

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

Estimation of Time-Dependent Delay Models at Actuated Traffic Signals in Duhok City

Duhok City, developed rapidly in recent years, leading to an increase in the traffic volume on its streets which are not sufficient for this increase resulting in traffic congestion. Therefore, this paper studied the delay as an important measure of effectiveness at traffic signals. The delay was measured and predicted (for saturated and under saturated conditions) to help in solving some underestimation problems by using the transportation system management (TSM) techniques. Five signalized intersections on Barzani major street in Duhok city was chosen. Vehicle delay was measured in these intersections directly from field, then it was calculated using the time-dependent equations recommended by three different official manuals, Highway Capacity Manual (HCM2000), Canadian Capacity Guide (ITE1995) and Australian Capacity Guide (ARRB1995). After that comparisons between the results of each theoretical method and the field result was made. Finally, the regression analysis technique was used to establish different relationships between the delay data obtained from the field and each of the theoretical methods to find the best applicable model to predict the vehicle delay at signalized intersections in Duhok City. It was proved that the logarithmic relationship between field and theoretical results obtained from the Canadian Capacity Guide is the best

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China

In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.

Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data choices for crop yield estimation. In most cases, spectral information derived from multispectral or hyperspectral images is used alone for yield estimation, while variables that contain spatial information such as textural measures were not considered. As UAV can acquire images with spatial resolution at centimeter-level, which contain rich spatial information of observed objects, the test of textural measures in improving yield estimation becomes possible. In this study, UAV-based hyperspectral images of rice in the east of China were acquired in 2017, 2018 and 2019 three consecutive year during rice growing seasons. Eight gray level co-occurrence matrix (GLCM) based texture measures were derived, and all two band combinations of these textural measures as well as spectral reflectance in three types (normalized type, differential type and simple ratio type) were calculated to developing textural and spectral index. Grain yield estimation models were established using spectral indices alone and spectral indices combining textural measures, respectively. The results show that models based on spectral indices combining textural measures all performed better than these using spectral indices alone, with coefficient of determination R2 of the best model greater than 0.8, RMSE (Root Mean Square Error) of 0.421 Mg ha−1 and MAPE (Mean Absolute Percentage Error) of 4.66% for calibration results, and 0.521 Mg ha−1 and 6.63% for validation results. Meanwhile, spectral indices combining textural measures can adjust the problems of overestimation and underestimation in models developed with spectral indices alone. This kind of improvement is especially obvious when grain yield is below 5.0 Mg ha−1 or above 8.0 Mg ha−1. Besides, the estimation accuracy of the model based on spectral index combining textural measure at the booting stage was equivalent to the model based on spectral indices at the booting and heading stages, indicating that textural measures may be helpful in monitoring grain yield at an earlier stage.

Baseline correction method based on improved adaptive iteratively reweighted penalized least squares for the x-ray fluorescence spectrum.

To solve the problem of baseline drift in the detection of soil samples by x-ray fluorescence spectrum, an improved adaptive iterative weighted penalized least squares (IairPLS) method is proposed to estimate the baseline of x-ray fluorescence spectrum signals. We improved the original exponential weight function to solve the problem of baseline underestimation caused by adaptive iterative weighted penalized least squares. The improved function effectively reduces the risk of baseline underestimation and speeds up the weighting process, achieving good results. In this paper, the MC simulation spectrum and soil real analysis spectrum are used to verify the performance of the algorithm. Finally, the algorithm is compared with previous penalized least squares methods (asymmetric least squares, adaptive iterative reweighted penalized least squares, and multiple constrained reweighted penalized least squares), with the results showing that the proposed method has the least root-mean-square error after baseline correction for optimal smoothing parameters λ and the best relative error of baseline estimation accuracy. Meanwhile, the IairPLS method can effectively improve the quantitative analysis ability of the x-ray fluorescence spectrum. The proposed method can be successfully applied to the actual x-ray fluorescence spectrum, which provides a powerful basis for quantitative analysis.

“One Out–All Out” Principle in the Water Framework Directive 2000—A New Approach with Fuzzy Method on an Example of Greek Lakes

The “One Out–All Out” (OOAO) principle imposed by the WFD selects the worst ecological status assessed by different biological quality elements (BQEs). Since it is a precautionary rule that can lead to problems of underestimation of the overall status, its amendment has been a matter of debate for WFD 20+. The use of fuzzy methods that express the functional relationships between variables in ecology and management has been gaining more ground recently. Here is attempted the inclusion of a fuzzy regression among the frequently monitored BQE (phytoplankton) and the outcome of OOAO application in six Greek lakes. The latter was determined by the comparison of four BQE indices in order to assess the extent to which BQEs might underpin the optimal/actual qualitative classification of a waterbody. This approach encompasses the uncertainty and the possibility to broaden the acceptable final EQR based on the character and status of each lake. We concluded that the fuzzy OOAO is an approach that seems to allow a better understanding of the WFD implementation and case-specific evaluation, including the uncertainty in classification as an asset. Moreover, it offers a deeper understanding through self-learning processes based on the existing datasets.

Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model

In order to examine the risk spillovers among multiple stock markets efficiently, we develop a vine-copula-GARCH-MIDAS model to estimate the multivariate joint distribution, and then derive CoVaR-type risk measures. Our empirical results on international stock markets show that the vine-copula-GARCH-MIDAS model is promising and is superior to several popular models. One the one hand, it exploits macroeconomic fundamentals to improve the accuracy of CoVaR measure. On the other hand, it is able to measure risk spillovers in a “multiple-to-one” pattern and solve the problem of underestimation in conventional “one-to-one” methods. What’s more, we find that there are significant risk spillovers from multiple developed stock markets including the US, Japan, and Britain, to China, which is necessary for regulators to be concerned with multiple markets simultaneously instead of a single market.

Emigration from Belarus: Problems of Underestimation

The article covers matters of improving measurement on emigration from Belarus. The author views the current role of external migration as a major component of formation of the population in the Republic of Belarus. The effectiveness of the country socio-economic development programs depends, according to the author, on adequate measures for evaluating the phenomenon in question. The article gives a detailed description of national migration statistics, which, as in many countries, underestimates the number of emigrants, and considers the possibilities of the so-called «mirror» statistics (statistics from countries where Belarusian residents emigrate to) in obtaining real information on the size of emigration from Belarus.After the introduction, the author analyses some of the most important publications on the subject under discussion, concludes the current state of current statistics and population censuses. The article contains the rationale for choosing foreign countries whose data are used as «mirror» statistics necessary for a more realistic estimation of emigration processes and comments on the geography of migration flows from Belarus. The author assesses the gap in data from «mirror» statistics and the official Belarusian statistics and gives an opinion on the real trends in migration flows of Belarusian citizens, along with the authors own estimates of the country’s population growth, which differ markedly from official statistics.In the opinion of the author, formulated in the final part of the article, the actual amount of the underestimation is considerably higher than can be assumed using «mirror» statistics, because, for example, there is a group of Belarusian citizens in Poland and Russia, that is not reflected in official statistics on migrants. The article therefore proposes to establish a system of information exchange between Belarus and the main countries of emigration, especially Russia and Poland, with which it is difficult to assess the extent of emigration based solely on official data, and to establish regular surveys on emigration.

Determining the number of canonical correlation pairs for high-dimensional vectors

For two random vectors whose dimensions are both proportional to the sample size, we in this paper propose two ridge ratio criteria to determine the number of canonical correlation pairs. The criteria are, respectively, based on eigenvalue difference-based and centered eigenvalue-based ridge ratios. Unlike existing methods, the criteria make the ratio at the index we want to identify stick out to show a visualized “valley-cliff” pattern and thus can adequately avoid the local optimal solutions that often occur in the eigenvalues multiplicity cases. The numerical studies also suggest its advantage over existing scree plot-based method that is not a visualization method and more seriously underestimates the number of pairs than the proposed ones and the AIC and $$C_p$$ criteria that often extremely over-estimate the number, and the BIC criterion that has very serious underestimation problem. A real data set is analyzed for illustration.

A generalized minimax-concave penalty based compressive beamforming method for acoustic source identification

Acoustic source identification using compressive beamforming has been extensively investigated and applied in numerous fields. In this paper, a generalized minimax-concave (GMC) penalty based compressive beamforming method is proposed. The method solves acoustic inverse problems using the GMC penalty, by which the capacity of sound field reconstruction in low-frequency and low-signal-to-noise ratio environments can be enhanced. In addition, the problem of systematic underestimation of source strength can be relieved with the benefit of penalty homogeneity, and a balance can be reached between the sparsity-induced capacity and computing complexity. Moreover, the convergence of the proposed method and parameter influences were studied through numerical simulations. The results show that the method is robust within a relatively wide range of parameters, and the computational efficiency is within the limits of acceptability. Furthermore, the performance of the proposed method was compared with two other methods using simulated and experimental data. The results indicate that the proposed method performs better than existing algorithms under low-frequency and strong-noise conditions, and the spatial resolution in acoustic maps and the capacity of source strength estimation can be improved.

Multi-GMTI fusion for Doppler blind zone suppression using PHD fusion

For ground moving target indication (GMTI) sensor tracking, the existence of the Doppler blind zone (DBZ) seriously deteriorates tracking performance. In order to minimize the adverse effects of the DBZ factor, this paper puts forward the idea of using sensor fusion technique to suppress the DBZ masking problem. First, we derive the probability hypothesis density (PHD) fusion under the generalized covariance intersection (GCI) framework and its Gaussian mixture (GM) implementation for fusing local PHDs from the local trackers. However, we find that there is the problem of cardinality underestimation (CUE) in the original PHD fusion, which is exacerbated when targets are masked by the DBZ. After analyzing this problem in detail, we propose an improved PHD fusion algorithm through operations such as scale coefficient correction, GM component partition, and fused label correction. Finally, the feasibility and effectiveness of the proposed fusion are verified through numerical examples, and it is proved that it alleviates the CUE problem and is significantly better than local trackers.

Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection.

The Variational AutoEncoder (VAE) has become one of the most popular models for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative VAE model, Quantile-Regression VAE (QR-VAE), that avoids this variance shrinkage problem by estimating conditional quantiles for the given input image. Using the estimated quantiles, we compute the conditional mean and variance for input images under the Gaussian model. We then compute reconstruction probability using this model as a principled approach to outlier or anomaly detection. We also show how our approach can be used for heterogeneous thresholding of images for detecting lesions in brain images.

Locality-Aware Crowd Counting

Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias. Our proposed method is locality-aware in two aspects. First, we introduce a locality-aware data partition (LADP) approach to group the training data into different bins via locality-sensitive hashing. As a result, a more balanced data batch is then constructed by LADP. To further reduce the training bias and enhance the collaboration with LADP, a new data augmentation method called locality-aware data augmentation (LADA) is proposed where the image patches are adaptively augmented based on the loss. The proposed method is independent of the backbone network architectures, and thus could be smoothly integrated with most existing deep crowd counting approaches in an end-to-end paradigm to boost their performance. We also demonstrate the versatility of the proposed method by applying it for adversarial defense. Extensive experiments verify the superiority of the proposed method over the state of the arts.

Transportation infrastructure cost underestimation: investigating the front-end debate

The underestimation of the final cost of transportation infrastructure projects has attracted significant scholarly attention and media coverage. While strategic misrepresentation and optimism may have gained recent notoriety for explaining the underestimation problem, traditional schools of thought insist the root sources lie principally within the domains of technical issues and poor conceptual project shaping at the front-end. Given this ongoing dialectical debate, this study investigates the front-end estimating practices of three highway agencies in Nigeria to establish the validity of these dichotomous theories. The findings show that although political motives strongly underlie the project appraisal practices of the highway agencies, there was not much evidence to support strategic misrepresentation/optimism bias. Rather, the study reveals that apart from the deployment of simplistic methods of conceptual costing, there is a discernible lack of control gateways at the front-end of project shaping and decision planning stages, which has fostered dysfunctional project practices.

COPE

Prefetching helps in reducing the memory access latency in multi-banked NUCA architecture, where the Last Level Cache (LLC) is shared. In such systems, an application running on core generates significant traffic on the shared resources, the underlying network and LLC. While prefetching helps to increase application performance, but an inaccurate prefetcher can cause harm by generating unwanted traffic that additionally increases network and LLC contention. Increased network contention results in untimely prefetching of cache blocks, thereby reducing the effectiveness of a prefetcher. Prefetch accuracy is extensively used to reduce unwanted prefetches that can mitigate the prefetcher caused contention. However, the conventional prefetch accuracy parameter has major limitations in NUCA architectures. The article exposes that prefetch accuracy can create two major false-positive cases of prefetching, Under-estimation and Over-estimation problems, and false feedback loop that can mislead a prefetcher in generating more unwanted traffic. We propose a novel technique, Coordinated Prefetching for Efficient (COPE), which addresses these issues by redefining prefetch accuracy for such architectures and identifies additional parameters that can avoid generating unwanted prefetch requests. Experiment conducted using PARSEC benchmark on a 64-core system shows that COPE achieve 3% reduction in L1 cache miss rate, 12.64% improvement in IPC, 23.2% reduction in average packet latency and 18.56% reduction in dynamic power consumption of the underlying network.

Искусство быть человеком: необходимость антропологических практик

The article is devoted to the need to study anthropological practices in modern socio-humanitarian science. The problem of underestimation of the anthropological side of the study of practices is revealed. The main reason for this underestimation is the influence of Marxism on the whole philosophy of the 19th and 20th centhuries. The loss of the authority in science by Marxism and positivism made it possible to make a methodological turn from the world of science to the world of life in the 1950s and 1960s, and in the 1960s – 70s to return to the initial ethical understanding of “practice” as an act, activity aimed at the benefit of a man. In modern science, interest has shifted from the social sciences to the humanities. The Russian philosopher A. Yu. Ashkerov predicts the transformation of social philosophy into social anthropology, the main methodology of which will be existential comparative studies. It is also proved in the article that anthropological phenomena today are the quintessence, the summed entity, more precisely the community, where all possible non-identical to each other beginnings and forces of being exist “inseparably and without merging”. Anthropological is initial, but it is able to realize itself only through individually psychological and social, which is the essence of the form of objectification. However, in order to return to oneself, the anthropological must become non-objectified, and reach the level of self-determination. In cyclicality, in the activity of objectification – reobjectification, a “fabric of history”, a circle of human being, where a person is able to grow up in creativity, is formed.

Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error.

Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time-to-event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time-to-event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Although RC can perform well in many settings with covariate measurement error, it is biased for nonlinear regression models, such as the Cox model. Thus, we additionally propose raking estimators which are consistent estimators of the parameter defined by the population estimating equation. Raking can improve upon RC in certain settings with failure-time data, require no explicit modeling of the error structure, and can be utilized under outcome-dependent sampling designs. We discuss features of the underlying estimation problem that affect the degree of improvement the raking estimator has over the RC approach. Detailed simulation studies are presented to examine the performance of the proposed estimators under varying levels of signal, error, and censoring. The methodology is illustrated on observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.