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In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the SV can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the skewness and SV in a Bayesian framework. We subsequently demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation study results also show that neglecting skewness leads to inaccurate SV estimates and conditional expected returns. Our empirical applications to daily stock return data also show strong evidence of negative skewness.

A 2 m class robotic drill was sent to the Moon and successfully collected and returned regolith samples in late 2020 by China. It was a typical thick wall spiral drill (TWSD) with a hollow auger containing a complex coring system to retain subsurface regolith samples. Before the robotic drill was launched, a series of laboratory tests were carried out to investigate and predict the possible drilling loads it may encounter in the lunar environment. This work presents how the sampling performance of the TWSD is affected by the regolith compressibility. Experiments and analysis during the drilling and sampling process in a simulated lunar regolith environment were conducted. The compressibility of a typical lunar regolith simulant (LRS) was measured through uni-directional compression tests to study the relationship between its inner regolith stress and bulk density. A theoretical model was established to elucidate the cutting discharge behavior by auger flights based on the aforementioned relationship. Experiments were conducted with the LRS, and the results show that the sampling performance is greatly affected by the flux of the drilled cuttings into the spiral flight channels. This work helped in scheduling reasonable drilling parameters to promote the sampling performance of the robotic drill in the Chinese Chang'E 5 mission.

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.

Graph Mining has gained importance due to its wide range of applications. As graph mining is a form of data mining where data is depicted in the form of graphs. The data when represented in the form of graphs helps to model the real scenario and relationship well. Discovery of interesting pattern from real relationship is of utmost beneficial. Frequent Subgraph Mining (FSM) is a significant area of graph mining. We accompany our approach in the field of FSM by adding a graph sampling function. This sampling function takes the single graph and a sampling percentage as input and produces a sampled graph which is similar in properties with original graph. This sampled graph is then taken as input while implementing the GraMi framework. Our sampling approach successfully mines the subgraphs with lower frequency threshold thus significantly improving the efficiency of the original GraMi framework.

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this paper we explore this issue by first considering the frequentist properties (bias and variance) of the posterior mean in the important case of the normal location model, which consists of a single observation on a univariate Gaussian distribution with unknown mean and known variance. Based on these results, we derive new estimators of the frequentist bias and variance of the WALS estimator in finite samples. We then study the finite-sample performance of the proposed estimators by a Monte Carlo experiment with design derived from a real data application about the effect of abortion on crime rates.

This article proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) t chart for the process mean using auxiliary information (AI) (denoted as the VSI EWMA t-AI chart). The proposed chart allows different sampling intervals to be adopted based on prior process quality. The average time to signal (ATS) and expected average time to signal (EATS) criteria are used to evaluate the performance of the proposed chart. Numerical analyses are conducted and the results show that the proposed VSI EWMA t-AI chart surpasses the EWMA t-AI chart in the detection of all sizes of shifts in the process mean when there are no changes (or estimation error) in the process standard deviation. Additionally, the proposed VSI EWMA t-AI chart is found to be more robust than the VSI EWMA X ¯ -AI chart when there are changes in the process standard deviation.

Non-response is unavoidable in any disaggregate-level survey efforts where information is sought from individuals. There is a strong need to understand the contributing factors of non-response behavior in freight surveys as there is very little information on what sort of establishments respond positively to survey inquiries. To meet this research gap, this paper analyzes the non-response behavior in an establishment-based freight survey (EBFS) conducted in Kerala, India. The analysis involves usage of five prominent classifiers: classification trees, random forests, Naive Bayes classifier, logistic regression and K nearest neighbors. A closer look at the results revealed that the industrial classes handling commodities with high value density (machinery, electronics and electrical equipment, other manufacturing products, textiles) exhibit high non-response probability for freight surveys. This may be explained in terms of the limited logistic information shared by establishments handling high valued products due to less attention given to logistics strategies. Another reason could be that the amount of capital and opportunity costs tied up with these commodities are high, and therefore, their information tend to be more proprietary in nature. The analysis also reveals that the location of establishment plays a modest, yet, noticeable effect on non-response behavior. The establishments from cities located closer to Ports with high per-capita income are less likely to respond to survey requests, possibly due to the higher opportunity cost for time and increased concerns of intrusion into privacy. Finally, the comparison of ROC curves suggests that KNN algorithm is most suitable for modelling non-response behavior. These models are expected to be useful for developing sample weighting schemes and targeted incentive strategies that can improve the state of practice of freight data collection in middle-income countries like India. The study findings are expected to be useful for developing more elaborate and dynamically responsive survey designs for EBFS, in which sample recruitment strategies can be adapted real time during data collection. This study also informs policymakers that the apparent trends in non-response, if not arrested, are likely to weaken the validity of inferences drawn from estimates based on EBFS and undermine, perhaps fatally, the potential of EBFS to guide facility planning and policy interventions.

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice, and thus, it is necessary to establish a useful method to reverse the rockhead profile using site investigation results. As a general method to reflect the spatial distribution of geo-material properties based on field measurements, the conditional random field (CRF) was improved in this paper to simulate rockhead profiles. Besides, in geotechnical engineering practice, measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent. As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty, CRF was implemented with the aid of the Bayesian framework in this study. More importantly, this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work. The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result, the subjectivity in determining prior mean can be minimized. Finally, both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles, while the influence of the latter is less significant than that of the former.

The purpose of this paper is to investigate how family businesses utilise social media tools, to determine what the purposes, benefits and challenges are, and to discover competencies that are important in social networking and cooperation. A multiple case study method was employed based on semi-structured interviews with six wineries located in the Balaton region, Hungary. The interviewees and the wineries they represented were selected among the 55 wineries applying the non-probability sampling purposive technique and the interviews were analysed using a case study method and qualitative content analysis. The results show that Facebook is the most popular social media tool that wineries use to increase brand awareness and reach new potential customers. This study also presents a “Social media roadmap” framework for good practice and proposes practical steps family businesses may take to help them succeed in their marketing activities.

In this paper, we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform. With this system, we aim to achieve the sub-Nyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics. Under the proposed scheme, we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain. Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory. We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect. The reconstruction error for the proposed system is analyzed. We show that, with considerations of noises and mismatches, the total error is bounded. The effectiveness of the proposed system is verified by numerical experiments. It is shown that our proposed system outperforms the other systems state-of-the-art.

Respondent-driven sampling (RDS) is commonly used to sample from key populations without a sampling frame since traditional methods are unable to efficiently survey them. Surveying these populations is often desirable to inform service delivery, assess effectiveness of programs, and determine prevalence of diseases. However, there are concerns about how RDS works in practice due to its many assumptions. To assess some of these assumptions, we develop diagnostics using geographical data and demonstrate their utility by identifying lack of convergence and characterizing RDS reach in surveys conducted among female sex workers and men who have sex with men in Kampala, Uganda.

We revisit the estimation algorithm of Pettenuzzo and Timmermann (2011) and show how to apply the posterior simulation test of Geweke (2004) to locate and correct an error in the original posterior sampling algorithm. The main modification for the new algorithm is the introduction of a Metropolis–Hasting step to draw the precision parameters of the return and predictor equations, taking into account the correlation between the error terms of the two equations. We find that the modification of the original algorithm has very minor effects on the empirical results reported in Pettenuzzo and Timmermann (2011). In particular, for both the dividend yield and Treasury bill predictors the updated algorithm finds that the models best supported by the data, as measured by the SIC, match those reported in the original paper.

Non-destructive methods for estimating carbon storage capacity are becoming increasingly popular as they do not harm the individual trees or the ecosystem. However, currently the destructive method of sampling trees for estimating their carbon storage capacity is widely practiced throughout the world. Therefore, the present study was conducted in a mangrove conservation forest located in a tropical island, Sri Lanka, with the objective of developing allometric equations to predict the stem carbon content of Lumnitzera racemosa and Avicennia marina using non-destructive method of sampling. The allometric model developed for Lumnitzera racemosa from this study, to determine stem carbon content is: Ln C = -3.485+ 1.155 Ln SH + 1.892 Ln DBH, Where, C: Stem carbon content, SH: merchantable stem height, DBH: Diameter at breast height. For A. marina, only diameter at breast height was statistically significant with stem carbon content and the allometric equation was, Ln C = −3.483 + 2.407 Ln DBH. The models were evaluated using p value, R2 value, residual diagram, model bias values and model efficiency values. The models were validated by calculating residual values as the difference between the actual stem carbon content and predicted stem carbon content from the models for Lumnitzera racemosa and A. marina. Further, there was no significant difference between the mean values of the measured stem carbon content and the predicted stem carbon content using the prediction models. The results indicate that the developed allometric equations in the present study are practically applicable in the field to estimate the stem carbon content of Lumnitzera racemosa and A. marina. Further, these estimations can contribute to make more accurate valuations on carbon stocks of sequestered carbon necessary for carbon trading purposes and sustainable management of mangrove forest ecosystems.

In the field of supercritical wing design, various principles and rules have been summarized through theoretical and experimental analyses. Compared with black-box relationships between geometry parameters and performances, quantitative physical laws about pressure distributions and performances are clearer and more beneficial to designers. With the advancement of computational fluid dynamics and computational intelligence, discovering new rules through statistical analysis on computers has become increasingly attractive and affordable. This paper proposes a novel sampling method for the statistical study on pressure distribution features and performances, so that new physical laws can be revealed. It utilizes an adaptive sampling algorithm, of which the criteria are developed based on Kullback–Leibler divergence and Euclidean distance. In this paper, the proposed method is employed to generate airfoil samples to study the relationships between the supercritical pressure distribution features and the drag divergence Mach number as well as the drag creep characteristic. Compared with conventional sampling methods, the proposed method can efficiently distribute samples in the pressure distribution feature space rather than directly sampling airfoil geometry parameters. The corresponding geometry parameters are searched and found under constraints, so that supercritical airfoil samples that are well distributed in the pressure distribution space are obtained. These samples allow statistical studies to obtain more reliable and universal aerodynamic rules that can be applied to supercritical airfoil designs.

Monte Carlo integration is an established method in simulating light transport. However, due to its stochastic nature, it requires copious amounts of samples to eliminate the estimation error, and variance reduction techniques such as importance sampling are used to improve the convergence speed as a remedy. Path guiding is a class of adaptive importance sampling methods devised specifically for light transport simulation, which demonstrates significant improvements over classical sampling techniques. Yet most of the previous path guiding methods only account for the radiance term, omitting the bidirectional scattering distribution function (BSDF) term. This results in suboptimal sample quality for non-diffuse light transport. This paper presents a path guiding method which guides paths according to the full product of the BSDF and radiance terms in light transport equation. Extending a spatio-directional tree based path guiding method, we use a semi-adaptive grid-quadtree hybrid subdividing the directional domain in a spatial subdomain to learn and represent the radiance field. With the help of BSDF lookup tables used to accelerate BSDF evaluations, this grid-quadtree allows us to efficiently construct approximate product distributions and guide paths according to the product of incident radiance and cosine-weighted BSDF at each path vertex. The resulting method is relatively simple to implement and enables more robust sampling on scenes with many glossy surfaces.

Increasingly, marketing and consumer researchers rely on online data collection services. While actively-managed data collection services directly assist with the sampling process, minimally-managed data collection services, such as Amazon’s Mechanical Turk (MTurk), leave researchers solely responsible for recruiting, screening, cleaning, and evaluating responses. The research reported here proposes a 2 x 2 framework based on sampling goal and methodology for screening and evaluating the quality of online samples. By sampling goals, screeners can be categorized as selection, which involves matching the sample with the targeted population; or as accuracy, which involves ensuring that participants are appropriately attentive. By methodology, screeners can be categorized as direct, which screens individual responses; and as statistical, which provides quantitative signals of low quality. Multiple screeners for each of the four categories are compared across three MTurk samples, two actively-managed data collection samples (Qualtrics and Dynata), and a student sample. The results suggest the need for screening in every online sample, particularly for the MTurk samples, with the fewest supplier-provided filters. Recommendations are provided for researchers and journal reviewers that provide greater transparency with respect to sample practices.

Learning from the relevant experiences leads to fast convergence if the experiences provide useful information. We present the new and simple yet efficient technique to find suitable samples of experiences to train the agents in a given state of an environment. We intended to increase the number of states visited and unique sequences that efficiently reduce the number of states the agents have to explore or exploit. Our technique implicitly introduces additional strength to the exploration-exploitation trade-off. It filters the samples of experiences that can benefit more than half the number of agents and then utilizes the experiences to extract useful information for decision making. First, we compute the similarities between the observed state and previous states in the experiences to achieve this filtering. Then, we filter the samples using the hyper-parameter, z , to decide which experiences will be suitable. We found out that agents learn quickly and efficiently since sampled experiences provide useful information that speeds up convergence. In every episode, most agents learn or contribute to improve the total expected future return. We further study our approaches’ generalization ability and present different settings to show significant improvements in diverse experiment environments.

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, S toc P res G reed , is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.

With the development of the Internet of Things technology, the current amount of data generated by the Internet of Things system is increasing, and these data are continuously transmitted to the data center. The data processing and analysis of the traditional Internet of Things system are inefficient and can not handle such a large number of data streams. In addition, the IoT smart device has a resource-limited feature, which can not be ignored when analyzing data. This paper proposes a new architecture ApproxECIoT (Approximate Edge Computing Internet of Things, ApproxECIoT) suitable for real-time data stream processing of the Internet of Things. It implements a self-adjusting stratified sampling algorithm to process real-time data streams. The algorithm adjusts the size of the sample stratums according to the variance of each stratum while maintaining the given memory budget. This is beneficial to improve the accuracy of the calculation results when resources are limited. Finally, the experimental analysis was performed using synthetic datasets and real-world datasets, the results show that ApproxECIoT can still obtain high-accuracy calculation results when using memory resources similar to simple random sampling. In the case of synthetic data streams, when the sampling ratio is 10%, compared with CalculIoT, the accuracy loss of ApproxECIoT is reduced by 89.6%; compared with SRS, the accuracy loss of ApprxoECIoT is reduced by 99.8%. In the case of using the real data stream of the wireless sensor network, the performance of ApproxECIoT is not the best, but as the sampling ratio increases, the accuracy loss of ApproxECIoT decreases more than other frameworks.

In this paper, we investigate the stability analysis for interconnected system, which is composed of multiple subsystems with sampled-data control and state quantization. The controlled interconnected system is modeled as a time-delay system with time-varying delays. By employing the information of sampling instants, a discontinuous Lyapunov functional is constructed. Using this functional, we derive tractable sufficient conditions to ensure the well-posedness and asymptotic stability of the considered system by taking the interconnected structure into consideration. These conditions depend only on the sampling interval, quantizer parameters and matrices parameters of individual subsystem, making them attractive in computation verification for large scale interconnected system. Furthermore, a grid search based optimization algorithm is proposed to reduce the total number of sampled data for the whole system. The effectiveness and applicability of the main results are shown by two examples.

The randomized extended Gauss-Seidel method is a popular representative among the iterative algorithm due to its simplicity for solving the inconsistent and consistent systems of linear equations, which builds the connection between the randomized Kaczmarz and Gauss-Seidel methods. In this work we develop a general version of the randomized extended Gauss-Seidel method, as well as some new iterative schemes. We prove that our algorithm can exponentially converge in expectation to the solutions of the consistent or inconsistent linear systems under two different sampling strategies. Numerical examples show that the proposed algorithm is feasible and effective, where the block method performs significantly better than the corresponding original form.

On the Ground • Adaptive land management requires monitoring of resource conditions, which requires choices about where and when to monitor a landscape. • Designing a sampling design for a monitoring program can be broken down in to eight steps: identifying questions, defining objectives, selecting reporting units, deciding data collection methods, defining the sample frame, selecting an appropriate design type, deciding stratification and allocation, and identifying the required sampling effort. • Here, we provide descriptions of each step in the process and identify tools and resources to complete each step.

Soil Organic Carbon (SOC) is a measure of the total carbon content of the soil and is a vital soil health indicator. Over the decades, SOC has been estimated using sampling followed by rigorous laboratory-based testing methods. Spaceborne Microwave/Synthetic Aperture RADAR (SAR) remote sensing has proven to be a versatile tool for various soil study applications. However, there have been very few studies conducted for SOC estimation using SAR remote sensing. This study utilises time-series, C-band remotely sensed SAR data from Sentinel-1 A satellite for SOC estimation and compared the performances of Random Forest (RF) and Ordinary Least Squares (OLS) Regression models over agricultural areas of Rupnagar district of Punjab in India. A set of 96 soil samples were collected from 32 different agricultural field locations in Rupnagar district between November 2019 to January 2020. SAR backscatter of Vertically emitted and Vertically received (VV) and Vertically emitted and Horizontally received (VH) polarisation channels, from Sentinel-1, soil moisture, electrical conductivity, pH, temperature and SOC from the laboratory-based testing methods were used as regression parameters. The RF regression gave a Root Mean Square Error (RMSE) of 0.78 and R2 statistics of 0.887, while the OLS method performed better with an RMSE of 0.53 and an R2 value of 0.907. It was also observed that the backscatter from VV and VH polarisation channels, when used synergistically with field data, have the highest Feature Importance (FI) score in both RF and OLS regression models for SOC estimation.

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

In this study, a maximum volume sampling model is proposed to improve the accuracy and efficiency of reliability computation. An ellipsoid is constructed with the maximum volume approach in a safe domain, and a new maximum volume optimization method is proposed. The sampling model only computes the samples outside the ellipsoid, which considerably enhances computational efficiency. Furthermore, the uniform sampling strategy and Givens transformation are adopted to efficiently solve the maximum volume optimization model. A series system example, a three-dimensional rock slope example, and an arch bridge example are tested to verify the validity of the proposed maximum volume sampling model. The results indicate that the maximum volume sampling model displays high accuracy and efficiency.

This paper aims to solve sample inefficiency in Asynchronous Advantage Actor-Critic (A3C). First, we design a new off-policy actor-critic algorithm, which combines actor-critic with experience replay to improve sample efficiency. Next, we study the sampling method of experience replay for trajectory experience and propose a familiarity-based replay mechanism which uses the number of replay times of experience as the probability weight of sampling. Finally, we use the GAE-V method to correct the bias caused by off-policy learning. We also achieve better performance by adopting a mechanism that combines off-policy learning and on-policy learning to update the network. Our results on Atari and MuJoCo benchmarks show that each of these innovations contributes to improvements in both data efficiency and final performance. Furthermore, our approach keeps a fast coverage speed and the same parallel feature as A3C, and also has better performance on exploration.

Missing data problems are ubiquitous in data collection. In surveys, these problems may arise from unit response, item nonresponse, and panel attrition. Building on the Dominitz and Manski (2017) study of choice between two or more sampling processes that differ in cost and quality, we study minimax-regret sample design in anticipation of missing data, where the collected data will be used for prediction under square loss of the values of functions of two variables. The analysis imposes no assumptions that restrict unobserved outcomes. Findings are reported for prediction of the values of linear and indicator functions using panel data with attrition. We also consider choice between a panel and repeated cross sections.

Leader-following consensus of multi-agent systems is highly influenced by the sampling effects of the agents’ actuators. In this paper, the multi-agent systems involving leader-following strategy is considered for the consensus analysis via sampled-data control using time-dependent Lyapunov–Krasovskii functionals. A directed graph topology is assumed to address the communication network between the agents and each agent obeys a class of stochastic nonlinear dynamics via Bernoulli distribution. A looped Lyapunov Krasovskii functional (LKF) is constructed for the sake of the consensus analysis with suitable controller design to achieve closed-loop dynamics. Using Wirtinger-based integral inequalities to the integral terms of LKF derivatives, less conservative consensus conditions are established in the form of linear matrix inequalities (LMIs). Finally, two illustrative examples are given to show the effectiveness of developed theoretical results.

This paper proposes an adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization. In the proposed algorithm, a multi-niche sampling strategy is designed to sample a subpopulation for evolution at each generation. In this strategy, the entire population is firstly divided into multiple niches by employing a certain niching strategy at each generation. A subpopulation is then dynamically sampled from the resulting niches such that supporting a diverse search at the early stage of evolution while an intensive search towards the end of evolution. The above strategy will be further coupled with a neighborhood crossover, which is devised to encourage high potential solutions for exploitation while low potential solutions for exploration, thus appropriately searching the solution space. Additionally, an adaptive local search (ALS) scheme along with an adaptive elimination operation (AEO) have been designed. The ALS aims to appropriately fine-tune promising solutions in the sampled subpopulation while the AEO tends to adaptively eliminate unpromising individuals in the population during evolution. The performance of the proposed algorithm has been evaluated on CEC’2015 benchmark functions and compared with related methods. Experimental results show that our algorithm can achieve a superior performance and outperform related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Surveys of anthropogenic pressures and their effect on river fish communities have rarely been conducted in the eastern Mediterranean countries, in contrast to the rest of Europe. An electrofishing dataset of 1586 samples (818 sites) was analyzed to investigate human-induced pressures on river fish assemblages in Greece. Pressure patterns and pressure-impact analysis towards the national fish bioassessment index (HeFI) and its four constituent metrics are depicted. At each sampling site 11 anthropogenic pressure variables were assessed, and several integrated/grouped pressure indices were computed, including hydrology, morphology, connectivity, and water quality. Almost 60% of samples were classified as impacted. The most common pressure groups affecting Greek rivers were hydrology recorded at 714 (45%) samples, morphology at 692 (44%) and artificial barriers at 382 (24%) samples. A similar pattern persists among the six investigated freshwater ecoregions in Greece. The most impacted ecoregions were Thrace (76% impacted samples), Western Aegean (67% impacted samples) and Macedonia-Thessaly (67% impacted samples). Lowlands and large rivers displayed higher levels of degradation, but water pollution was not recognized as a key stressor in most running waters. Pressure-impact analysis demonstrates that all HeFI component metrics (insectivorous, omnivorous, benthivores, potamodromous species) respond as expected to almost all integrated pressure indices considered, as regards: a) the direction of response, and b) the response to pressure intensity gradients. However, the longitudinal connectivity stressor clearly fails to respond towards metrics. The findings confirm the usefulness of model-based indices, using fish assemblage functional metrics as bioassessment tools. Specific recommendations for refining the tools are provided.

This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved by removing all the redundant variables, and thus the same feasible stabilization range can be obtained at the cost of more economical computational burden. Secondly, in order to further enhance the control efficiency, a dynamic trade-off between conservatism and complexity is established by proposing a new online evaluator of the updated system variation information across two adjacent sampling instants, and thus the so-called resilient stabilization is developed for reducing the conservatism without increasing or even alleviating the complexity. Moreover, all the determined matrices at the farthest sampling instant can be entirely removed without introducing any conservatism due to the proposed substitution technique. Finally, the superiority and effectiveness of our proposed methods are illustrated by a set of simulation comparisons with relevant research results reported in recent literature.

When forecasting with economic time series data, researchers often use a restricted window of observations or downweight past observations in order to mitigate the potential effects of parameter instability. In this paper, we study the problem of selecting a window for point forecasts made at the end of the sample. We develop asymptotic approximations to the sampling properties of window selection methods, and post-window selection point forecasts, where there is local parameter instability of various sorts. We examine risk properties of point forecasts made after cross-validation to select the window, and compare this approach to some alternative methods of selecting the window. We also propose a quasi-Bayesian form of cross-validation that we find to have good risk properties.

Background Sampling frames rarely exist for key populations at highest risk for HIV, such as sex workers, men who have sex with men, people who use drugs, and transgender populations. Without reliable sampling frames, most data collection relies on non-probability sampling approaches including network-based methods (e.g. respondent driven sampling) and venue-based methods (e.g. time-location sampling). Quality of implementation and reporting of these studies is highly variable, making wide-ranging estimates often difficult to compare. Here, a modified quality assessment tool, Global.HIV Quality Assessment Tool for Data Generated through Non-Probability Sampling (GHQAT), was developed to evaluate the quality of HIV epidemiologic evidence generated using non-probability methods. Methods The GHQAT assesses three main domains: study design, study implementation, and indicator-specific criteria(prevalence, incidence, HIV continuum of care, and population size estimates). The study design domain focuses primarily on the specification of the target and study populations. The study implementation domain is concerned with sampling implementation. Each indicator-specific section contains items relevant to that specific indicator. A random subset of 50 studies from a larger systematic review on epidemiologic data related to HIV and key populations was generated and reviewed using the GHQAT by two independent reviewers. Inter-rater reliability was assessed by calculating intraclass correlation coefficients for the scores assigned to study design, study implementation and each of the indicator-specific criteria. Agreement was categorized as poor(0.00-0.50), fair(0.51-0.70), and good(0.71-1.00). The distribution of good, fair, and poor scores for each section was described. Results Overall, agreement between the two independent reviewers was good(ICC >0.7). Agreement was best for the section evaluating the HIV continuum of care(ICC = 0.96). For HIV incidence, perfect agreement was observed, but this is likely due to the small number of studies reviewed that assessed incidence(n = 3). Of the studies reviewed, 2% (n = 1) received a score of "poor" for study design, while 50% (n = 25) received a score of "poor" for study implementation. Conclusions Addressing HIV prevention and treatment needs of key populations is increasingly understood to be central to HIV responses across HIV epidemic settings, though data characterizing specific needs remains highly variable with the least amount of information in the most stigmatizing settings. Here, we present an efficient tool to guide HIV prevention and treatment programs as well as epidemiological data collection by reliably synthesizing the quality of available non-probability based epidemiologic information for key populations. This tool may help shed light on how researchers may improve not only the implementation of, but also the reporting on their studies.

In-field and non-invasive determination of internal quality and ripeness stages allows for a selective harvest for Feicheng peach. In this study, a portable hyperspectral imager was used for on-site capturing the images of mid-ripe and ripe peaches on trees, and soluble solids content (SSC) and firmness of the fruit were measured as the reference standards. These samples were split into calibration set and validation set by samples set partitioning based on joint X–Y distances (SPXY) algorithm. Multiple linear regression (MLR) models were established using effective wavelengths selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), respectively. The more promising performances were achieved by RF-MLR model with R v 2 of 0.88 and RMSEV of 0.54 for SSC, and CARS-MLR model with R v 2 of 0.81 and RMSEV of 1.17 for firmness. Furthermore, LIBSVM model was employed to discriminate the ripeness of Feicheng peach using effective wavelengths selected by sequential forward selection (SFS) algorithm, with classification accuracy of 91.7% in the validation set. It can be concluded that portable hyperspectral imager can be applied to determine the internal quality and ripeness stages of Feicheng peach in orchard, providing support for the in-field and nondestructive quality inspection and timely harvesting of Feicheng peach.

This study models the energy consumption of electric vehicles (EVs) under uncertain and small data conditions by combining the machine learning method and the idea of controlled experiments. We propose a Machine Learning-Control Variable model, termed the MLCV model, to estimate the trip energy consumption of EVs. Different data augmentation methods, ensemble methods, sampling factors are adopted as the parameters of the proposed method. Through parameter search, the accuracy of the base learner can be further improved. Our method utilizes real driving behaviours that are generated by real drivers and collected in a complex urban environment, making the approach generalizable. The experimental results demonstrate that the proposed MLCV model is superior to existing machine learning models in terms of estimation accuracy.

In this study, the images of Sentinel-2B multispectral sensor are used to map minerals associated with rare earth elements (REE) in an attempt to discover new potential targets for REE deposits in the Schiel Alkaline Complex of South Africa. Spectral Information Divergence (SID) classification was applied to Sentinel-2B images to map areas containing potential monazite and hematite. Generally, the accuracy of SID classification is warranted by selecting appropriate threshold values, which are normally user-defined. In this study, a simple thresholding approach is proposed; this approach requires at least one area, with a confirmed existence of a mineral being sought, to be used as a reference point for selecting optimum threshold value. The aim of this approach is to determine the lowest threshold value at which mineral classification in a reference point starts. The smaller the threshold value, the more likely the classified pixels represent minerals in question in the ground. Following this approach, a value of 0.02 and 0.03 was used as the maximum acceptable threshold value for mapping REE-bearing mineral (monazite) and iron-rich mineral (hematite), respectively. The proposed approach was validated through field sampling and geochemical analysis of samples collected at potential REE deposit targets, including one rock sample (S3) collected in the area void of minerals of interest, based on SID classification. The analytical results show that the samples collected at anomalous mineral clusters have relatively high REE contents that are well beyond the average crustal abundance and S3, thus suggesting the viability of the proposed approach.

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.

Inter-Satellite Links (ISL) are considered a key technology which might improve the accuracy of the Global Navigation Satellite System (GNSS) and navigation satellites orbit determination. This research aims at evaluation of advantages of the ISLs with special consideration of the Galileo, as a contribution to precise orbit determination and satellite clock estimation. The performed analysis will also pave the way to more advanced processing including more sophisticated modifications of the current orbit determination approach and development of the algorithms for the ISL system. The study provides a detailed analysis of various ISL connectivity schemes (organisation of the inter-satellite connections). Selected results are then presented in the context of the precise orbit determination (POD) based on weighted least squares adjustment. As there is no Galileo satellite currently equipped with operational ISLs, the analysis is based on simulated measurements. The most promising connectivity schemes are collated according to their influence on POD performance and compared in terms of their accuracy and reliability. The performance of the estimation process was also tested with respect to the different number of GNSS stations and variable ISL measurement sampling interval. By using ISLs in nominal simulation, the POD is improved by reducing RMS orbit error in along-track by about 8 mm, cross-track components from 2 mm to 7 mm depending on used connectivity scheme, and in radial by about 5 mm. Generally clock estimation errors are smaller with the ISL contribution up to 30%. Furthermore, the ISLs make the constellation more resistant to the reduction of the number of ground stations, however about 30 stations are optimal. For 6 ground stations ISLs help to minimize orbit errors even up to 4 times comparing to a GNSS-only solution. Establishing ISLs more often, with sampling interval on the level of 30 s or less, seems to be the most promising in improving the orbit determination. Using sampling intervals equal to 300 s or 600 s indicates that a very small percentage of ISL observations compared to total number of GNSS measurements is still sufficient to reduce orbit errors in reference to GNSS-only solution from 30% to 40%.

Size control of particulate material is key in a mineral processing plant, allowing the downstream process equipment to separate the mineralogical components. Despite being extensively applied in millimetric particle size classification, recent advances in high frequency screens have allowed their application in grinding circuits, leading to production increases and better classification efficiency for the micrometric range. The development of models to simulate these systems is essential to allow estimating possible technological benefits in the plant performance. This study presents a review of models applied to screening processing, focusing on the wet classification of fine particles, and considering their applicability and limitations. The models review is combined with an assessment of their fitness to a high frequency screening trials database. Among the available alternatives, the model developed by Batterham et al. [1] presented the best fit to the dataset, but it has limited simulation capability. Mwale [2], in turn, was considered to offer the best compromise between fit to data and simulation readiness, since it does not demand a difficult sampling campaign and individualizes the process parameters from the model constants. However, a technology gap is still present, which would involve scale-up procedures from small-scale trials to industrial plants, since all the test work was developed using a real scale device.

The pollution of heavy metals is considered as one of the main problems of using wastewater for irrigation purposes. Frequent experimental measurements, time, and cost are essential for evaluating the pollution of heavy metals in a large area. Thus, using satellite images and establishing a relationship between the images and concentration of heavy metals can be regarded as a solution for estimating the polluted area. In this study, the image of the Sentinel-2 satellite was used to evaluate the heavy metals pollution of the area irrigated with wastewater in south of Tehran. For this aim, 30 soil-surface samples irrigated with raw wastewater were collected in the area. After preparing the samples, the concentration of Pb, Cu, and Ni was determined using atomic absorption spectroscopy. Then, the relationship between the heavy metal concentration, reflection on the bands or rating of the bands at the corresponding sampling points was determined by applying the stepwise regression method. In addition, the developed models were applied to the satellite image for zoning the heavy metal concentrations in the studied area. Finally, the accuracy of the developed models was examined by Root-Mean-Square Error (RMSE) and Pearson correlation coefficients. The results indicated that the amounts of RMSE for the equations of Pb, Cu, and Ni were 1.90, 2.54, and 1.59 respectively, while the amounts of R were 0.81, 0.75, and 0.73 for these metals representing a promising match between the predicted and measured results of the models.

Learning from label proportions (LLP), in which the training data is divided into different bags and only the proportions of samples belonging to certain categories in each bag are known, has attracted widespread interest in many application scenarios. However, the existing researches ignore the importance of part instances in which the labels can be easily obtained. In this paper, we propose a novel method called active-pSVM, which incorporates active learning into the large margin framework to solve LLP problem. In detail, we first label the most valuable instances that are selected by QUIRE or uncertainty sampling query strategy. Then, the obtained labeled instances and the given unlabeled data are simultaneously used to construct the prediction model under the large margin framework. Extensive experiments on benchmark datasets show that the proposed active-pSVM outperforms the state-of-the-art approaches for solving LLP problem, i.e. InvCal, alter-∝SVM and LLP-NPSVM.

Rangelands provide numerous ecosystem services, including forage for livestock grazing. Effective grazing management requires measuring forage availability, which influences the level of grazing that can be sustained while maintaining healthy ecological conditions. However, spatiotemporal variability makes such determinations of forage quantity difficult, potentially hindering optimal management. These determinations are especially difficult across large, remote areas. To address this, we developed an approach using data from a one-time sampling of vegetation throughout the Uintah and Ouray Reservation in northeast Utah. By associating these data in a random forest model with environmental and climatic covariates that vary annually, we produced yearly predictions of forage availability on a pixel-by-pixel basis for the Reservation and surroundings from 1984 to 2018. This method addresses and quantifies the spatiotemporal variability of available forage. The model confirms that forage availability is highly variable throughout the area. On average, forage availability in Reservation management units declined as much as 32% below median availability in some years and increased 33% above median availability in others. Moreover, some management units experienced large increases in favorable years but less significant declines in unfavorable years, while the opposite was true in others. In comparison to determining a single “typical” forage availability of management units, recognizing this inherent variability and how it differs between units provides a fuller picture of the range of possible forage availability. This can improve grazing management by revealing how forage quantities vary from year to year and may help avoid forage overutilization during unfavorable years such as drought. The model can continue to be used into the future to monitor vegetation trends, though with ongoing climate and vegetation changes periodic recalibration may be necessary. In addition, the modeling method may be applicable to other similar study systems.

Matrix completion is about recovering a matrix from its partial revealed entries, and it can often be achieved by exploiting the inherent simplicity or low dimensional structure of the target matrix. For instance, a typical notion of matrix simplicity is low rank. In this paper we study matrix completion based on another low dimensional structure, namely the low rank Hankel structure in the Fourier domain. It is shown that matrices with this structure can be exactly recovered by solving a convex optimization program provided the sampling complexity is nearly optimal. Empirical results are also presented to justify the effectiveness of the convex method.

Low-frequency radar tomography is an important subsurface imaging method for future planetary missions to solar system small bodies. We derive angular and radial sampling criteria for monostatic and bistatic radar tomography algorithms that are based on monochromatic free-space backprojection and spherical apertures. We use the vector Born approximation to highlight the degeneracy of monochromatic bistatic source/receiver direction pair measurements in k-space. Analytical expressions are then derived for the scalar point target response of different spherical sampling geometries. We also derive the angular sampling step and total number of sampling points required to fully reconstruct the point target response for monostatic, bistatic, and non-degenerate k-space spherical apertures. These are evaluated for object sizes and radar operating frequencies expected in small body tomography. We also analyze and derive expressions for the coherence loss of spherical apertures due to random errors in a sensor’s radial position, which provides requirements on the a posteriori ephemeris knowledge. Finally, we derive a vector backprojection algorithm suitable for focusing quad-pol scattering matrix (S-matrix) data that is tested using full-wave S-matrix simulations of dielectric point targets. This work is intended to aid radar instrument performance analysis and inform the design and architecture of future instruments and missions.

Active learning (AL) for object detection (OD) aims to reduce labeling costs by selecting the most valuable samples that enhance the detection network from the unlabeled pool. Due to the complexity of OD compared with image classification , more consideration should be given when designing the selection strategies. Previous works have studied aggregating information of multiple outputs (especially the location information) and aggregating information of batch boxes, all of which indicate improved performances. However, the evaluation index—mean average precision (mAP) has not been considered seriously, although improving it is the goal of AL. Moreover, the background class is far more than other classes (15:1 or more) in each batch of samples, leading to a class imbalance problem . Therefore, AL strategies for OD, which take mAP and class imbalance in batch into consideration, may perform better. In this paper, WBetGS is proposed, which not only considers aggregating information of multiple outputs and batch boxes but also aims to mAP improvement and to address the class imbalance in batch. A weighted algorithm is introduced to promote the mAP more effectively. Besides, WBetGS eliminates the impact of class imbalance between background and object categories by extracting class-balanced information. Moreover, a diversity and uncertainty based sampling algorithm is introduced for batch mode active learning in object detection. The experimental results demonstrate that our method performs better than basic methods, saving up 100% of the labeling efforts while reaching the same performance in an actual industrial application.

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.

Free-form surfaces have been widely used in aerospace, automotive and other fields. Due to its complex geometry, free-form surface inspection is generally conducted by touch-trigger or measuring probe-based Coordinate Measurement Machines or On-machine Measurement. Sampling strategy plays a decisive role in improving both measurement accuracy and efficiency, which is determined by sample size and distribution of sample points. However, it is difficult to simultaneously take the surface curvature, sampling density and approximation error into account, considering the complexity of surface geometry. In this paper, triangle mesh simplification is innovatively adopted in sampling planning to achieve multi-geometric constraints. As triangle mesh has outstanding advantages in representing the surface features, strong stability and is easy to modify its structure, free-form surface is converted to a dense triangle mesh. Triangle mesh simplification is implemented by iteratively contracting triangle edges. An improved quadric error metric is established to decide contraction order and optimal target vertices under discrete curvature constraint. Sampling density is controlled by limiting the triangle edge length. Detailed adaptive sampling algorithm under multi-geometric constraints is then developed. Both simulation and experiment are conducted to validate feasibility and robustness of the proposed method. The results are compared with uniform sampling and existing adaptive sampling strategy to show that the proposed method can prominently reduce sampling error when sample size is small.

In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km2 with most locations having abundances between 20,000 to 50,000 particles/km2. The average abundance in Lake Superior was ~30,000 particles/km2 which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.

This paper focuses on the recognition problem of dynamical patterns consisting of sampling sequences. Specifically, based on the concept of structural stability, a novel similarity measure for dynamical patterns is first given. Then, a specific realization is provided, which consists of: (1) an approximation scheme for computation of partial derivative information by utilizing the knowledge learned through deterministic learning; (2) a similarity comparison scheme using the recognition errors generated from the discrete-time dynamical estimators; and (3) performance analysis of the recognition scheme with general recognition conditions. Compared with the existing methods, in which misrecognition may occur when the differences of dynamics between adjacent training patterns are very small, the proposed method is more appealing in the sense that, the partial derivatives of dynamics are introduced to complement the similarity measures, such that the recognition performance is much improved. Simulation studies are conducted to verify the proposed method in a relatively large data set.

Soil erodibility (K) is a key factor of soil erosion, and its appropriate quantification and interpolation are vitally important for soil and water conservation. The traditional point-represent-polygon approaches and common kriging method for the estimation of K, however, do not sufficiently represent the original data. The objectives of this study were to simulate the spatial distribution of K using a sequential Gaussian algorithm and analyze the uncertainty in evaluating the risk of soil erodibility in southeastern China. We determined 101 sampling points in the area and collected disturbed soil samples from the 0–20 cm layer at each point. Soil properties were determined, and K was calculated using five common models: the EPIC (Erosion/Productivity Impact Calculator), approximate nomograph, Torri, Shirazi, and Wang models. Among the chosen models, the EPIC model performed the best at estimating K(KEPIC), which ranged from 0.019 to 0.060 t ha h (ha MJ mm)−1, with a mean of 0.043 t ha h (ha MJ mm)−1. The KEPIC was moderately spatially variable and had a limited spatial structure, increasing from south to north in our study area, and all spatial simulations using the cooperative kriging (CK) interpolation and the sequential Gaussian simulation (SGS) with 10, 25, 50, 100, 200, and 500 realizations had acceptable accuracies. The CK interpolation narrowed the range, and the SGS maintained the original characteristics of the calculated data. The proportions of the risk area were 38.0% and 10.1%, when the risk probability for K was 60% and 80%, respectively, and high risk areas were mostly located in the north. The results provide scientific guidance for managing the risk of soil erodibility in southeastern China.