Efficient Sampling Scheme Research Articles

Influence on Estimation of Mean by Using Quartile Paired Double Ranked Set Sampling

In this paper, we present an efficient quartile paired double ranked set sampling (QPDRSS) scheme to estimate the population mean. QPDRSS provides an unbiased improved population mean estimator when the distribution is symmetric and bears negligible biasness for non-symmetric distribution. The performance of the proposed mean estimator is compared to its competitors in simple random sampling, double ranked set sampling, extreme paired double ranked set sampling and paired double ranked set sampling by manipulating simulation process for various (symmetric and non-symmetric) distributions. A real dataset is also studied to exemplify the achievement of the proposal.

Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method

Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil mapping. cLHS samples are randomly selected in each stratum of environmental variables, thus the produced sample sets can vary significantly at different runs with the same sample size. Although variation of mapping accuracies caused by the randomness of cLHS has been realized and qualitatively mentioned in past studies. However, how the randomness of cLHS could quantitatively influence mapping accuracy has rarely been examined. In this study, we conducted experiments to examine how the sample randomness quantitatively influence soil mapping accuracy with different sample sizes, and analyzed the possible reasons from a pedogenesis perspective. The results showed that the largest range of mapping accuracies of 500 repeats was 39.5% at a sample density of 2.59 point/km2, while the smallest range was 7.3% at the maximum sample size with a sample density of 32.47 point/km2. The sample density for satisfactory prediction accuracies in our study area was at least 10.06 Point/km2. The results showed that both the allocation of sample points to each soil series and the typicality of sample points played important roles in mapping accuracies. But the deep reasons causing the unstable performance of cLHS at small sample sizes were the imbalanced class distribution of soil series and the overlap between soil series in the distribution of environmental covariates. Researchers need to be cautious about the output when applying cLHS with small sampling densities. Some effective approaches to address this issue include increasing the sample size, checking the sample allocations of a cLHS design with the assistance of legacy soil maps, or adding the legacy soil map as a variable during sampling design. When the sampling resources and legacy soil maps are limited for an area, fuzzy k-means clustering sampling could be a potential alternative. This study provides useful references for better understanding the uncertainty of cLHS when the sample density is small and selecting alternative sampling methods accordingly.

EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces

In order to approximate the multiple limit state functions for different failure events, the active learning Kriging model proposed for component reliability analysis has been extended to system reliability analysis. Meanwhile, many efficient sampling strategies have been applied to reduce the high computational burden. However, these strategies meet a challenge in wasting some training points and terminating the training process inappropriately, since they do not directly relate to the estimation error of system failure probability. To address the challenge, this work proposes an estimation error-guided adaptive Kriging method. As Kriging prediction may be inaccurate before being well trained, the predicted system failure probability may deviate from the true result. To quantify this estimation error, the true number of failure points is approximated by adding the number of predicted failure points and the number of wrongly classified points. Since it is impossible to learn the exact number of wrongly classified points, its confidence interval is derived based on the probability of making wrong state classification. Subsequently, the refinement of Kriging is achieved by using the probability to identify new points and using the estimation error to determine the termination, which has been demonstrated by three different cases.

Numerical Simulation of Finite-Temperature Field Theory for Interacting Bosons.

We report a stable and efficient complex Langevin sampling scheme for performing approximation-free numerical simulations directly on the path-integral coherent-states field theory for an assembly of interacting bosons. We apply the method to generate the λ line of critical phase transitions associated with Bose-Einstein condensation in a model ϕ^{4} scalar field theory. The new approach enjoys near-linear scaling in the resolved (d+1) spatial and imaginary-time dimensions and should be particularly efficient for the study of dense systems at low temperature.

Computational framework for population-based evaluation of TKR-implanted patellofemoral joint mechanics.

Differences in patient anatomy are known to influence joint mechanics. Accordingly, intersubject anatomical variation is an important consideration when assessing the design of joint replacement implants. The objective of this study was to develop a computational workflow to perform population-based evaluations of total knee replacement implant mechanics considering variation in patient anatomy and to assess the potential for an efficient sampling strategy to support design phase screening analyses. The approach generated virtual subject anatomies using a statistical shape model of the knee and performed virtual implantation to size and align the implants. A finite-element analysis simulated a deep knee bend activity and predicted patellofemoral (PF) mechanics. The study predicted bounds of performance for kinematics and contact mechanics and investigated relationships between patient factors and outputs. For example, the patella was less flexed throughout the deep knee bend activity for patients with an alta patellar alignment. The results also showed the PF range of motions in AP and ML were generally larger with increasing femoral component size. Comparison of the 10-90% bounds between sampling strategies agreed reasonably, suggesting that Latin Hypercube sampling can be used for initial screening evaluations and followed up by more intensive Monte Carlo simulation for refined designs. The platform demonstrated a functional workflow to consider variation in joint anatomy to support robust implant design.

Understanding Key Interactions between Lipid Membranes and Peripheral Membrane Proteins Involved in Cellular Signalling

Lipid-protein interactions play a key role in formation of protein complexes at the membrane surface. This is of particular importance for many systems involved in cellular signalling. This has been shown experimentally, and, in many cases, been verified and complemented by molecular dynamics (MD) simulations. We have previously used MD simulations to study Pleckstrin homology (PH) domains interacting with phosphatidylinositol (PIP)-containing membranes. Using coarse-grained simulations, we could map out binding sites, and investigate the effects of simultaneous binding at multiple sites (Naughton, Kalli and Sansom, 2018, J. Mol. Biol., 430, 372-388). Binding affinity were estimated using free energy calculations, and compared to experimental values. This work has provided an in-depth understanding of protein-lipid binding for the PH family. In the current study, we extend our investigations to other families of membrane-binding proteins. We have investigated the C2 domain from the notch ligand system, C2 from PTEN, and the FYVE domain from EEA1. The C2 domain binds to membranes without PIP, and thus these studies of C2 may characterise new classes of membrane-protein interactions. Our studies have so far led to development of methods for more efficient sampling strategies, meaning that it is now feasible to study a larger range of proteins and protein families, and to extend simulations to larger systems with multiple domains. Key questions to be addressed include: what lipids are important for the membrane-protein interactions in various systems; how do binding free energy landscapes compare across different families of membrane-binding proteins; and what is the synergistic effect of multiple domains interacting with nanoclusters of lipids in the membrane?

Using health facility-based serological surveillance to predict receptive areas at risk of malaria outbreaks in elimination areas

BackgroundIn order to improve malaria burden estimates in low transmission settings, more sensitive tools and efficient sampling strategies are required. This study evaluated the use of serological measures from repeated health facility-based cross-sectional surveys to investigate Plasmodium falciparum and Plasmodium vivax transmission dynamics in an area nearing elimination in Indonesia.MethodsQuarterly surveys were conducted in eight public health facilities in Kulon Progo District, Indonesia, from May 2017 to April 2018. Demographic data were collected from all clinic patients and their companions, with household coordinates collected using participatory mapping methods. In addition to standard microscopy tests, bead-based serological assays were performed on finger-prick bloodspot samples from 9453 people. Seroconversion rates (SCR, i.e. the proportion of people in the population who are expected to seroconvert per year) were estimated by fitting a simple reversible catalytic model to seroprevalence data. Mixed effects logistic regression was used to examine factors associated with malaria exposure, and spatial analysis was performed to identify areas with clustering of high antibody responses.ResultsParasite prevalence by microscopy was extremely low (0.06% (95% confidence interval 0.03–0.14, n = 6) and 0 for P. vivax and P. falciparum, respectively). However, spatial analysis of P. vivax antibody responses identified high-risk areas that were subsequently the site of a P. vivax outbreak in August 2017 (62 cases detected through passive and reactive detection systems). These areas overlapped with P. falciparum high-risk areas and were detected in each survey. General low transmission was confirmed by the SCR estimated from a pool of the four surveys in people aged 15 years old and under (0.020 (95% confidence interval 0.017–0.024) and 0.005 (95% confidence interval 0.003–0.008) for P. vivax and P. falciparum, respectively). The SCR estimates in those over 15 years old were 0.066 (95% confidence interval 0.041–0.105) and 0.032 (95% confidence interval 0.015–0.069) for P. vivax and P. falciparum, respectively.ConclusionsThese findings demonstrate the potential use of health facility-based serological surveillance to better identify and target areas still receptive to malaria in an elimination setting. Further implementation research is needed to enable integration of these methods with existing surveillance systems.

Interacting cluster point process model for epidermal nerve fibers

We propose an interacting cluster model for the spatial distribution of epidermal nerve fibers (ENF). The model consists of a spatial process of parent points modeling the base points of nerve fiber bundles. To each base point there is an offspring point process of fibers end points associated. The parent process is, possibly, inhibited by fiber endings that belong to different base bundles. The fibers themselves have random length and spatial orientation. We consider a non-orphan process where we can connect each offspring to a parent. We examine the processes that can be described with our model, how coefficient estimation can be performed under the Bayesian paradigm via Markov chain Monte Carlo methods, and detail approaches for the implementation of efficient MCMC sampling schemes. We study the performance of the estimation procedure through a simulation study. An application to data from skin blister biopsy images of ENFs is presented.

High-performance sampling of generic determinantal point processes.

Determinantal point processes (DPPs) were introduced by Macchi (Macchi 1975 Adv. Appl. Probab. 7, 83-122) as a model for repulsive (fermionic) particle distributions. But their recent popularization is largely due to their usefulness for encouraging diversity in the final stage of a recommender system (Kulesza & Taskar 2012 Found. Trends Mach. Learn. 5, 123-286). The standard sampling scheme for finite DPPs is a spectral decomposition followed by an equivalent of a randomly diagonally pivoted Cholesky factorization of an orthogonal projection, which is only applicable to Hermitian kernels and has an expensive set-up cost. Researchers Launay et al. 2018 (http://arxiv.org/abs/1802.08429); Chen & Zhang 2018 NeurIPS (https://papers.nips.cc/paper/7805-fast-greedy-map-inference-for-determinantal-point-process-to-improve-recommendation-diversity.pdf) have begun to connect DPP sampling to LDLH factorizations as a means of avoiding the initial spectral decomposition, but existing approaches have only outperformed the spectral decomposition approach in special circumstances, where the number of kept modes is a small percentage of the ground set size. This article proves that trivial modifications of LU and LDLH factorizations yield efficient direct sampling schemes for non-Hermitian and Hermitian DPP kernels, respectively. Furthermore, it is experimentally shown that even dynamically scheduled, shared-memory parallelizations of high-performance dense and sparse-direct factorizations can be trivially modified to yield DPP sampling schemes with essentially identical performance. The software developed as part of this research, Catamari (hodgestar.com/catamari) is released under the Mozilla Public License v.2.0. It contains header-only, C++14 plus OpenMP 4.0 implementations of dense and sparse-direct, Hermitian and non-Hermitian DPP samplers. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

Design and Analysis of Elimination Surveys for Neglected Tropical Diseases.

As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit’s elimination status.

Time-dependent reliability analysis through projection outline-based adaptive Kriging

Time-dependent reliability analysis (TRA) has drawn much attention due to its ability in measuring the probability that a system or component keeps safe in the full life cycle. Since it is difficult to efficiently obtain accurate results for TRA problems with expensive simulation demand, many surrogate model-based methods have been proposed to handle this challenge. Moreover, when both random and interval uncertainties are included in these TRA problems simultaneously, the analysis process will be more complicated. In this paper, two methods based on projection outline adaptive Kriging (POK) are proposed to handle TRA and TRA with mixed interval uncertainties (iTRA), respectively. Firstly, POK-TRA method is put forward for the TRA problems with different stochastic processes. Different from current TRA methods, POK-TRA regards the time parameter as a special interval variable, which converts TRA problem into a special hybrid reliability analysis (HRA) problem with one interval variable. Based on the concept of projection outline as well as a correlation condition, an efficient sampling strategy is proposed to refine the Kriging model adaptively. Secondly, POK-TRA is extended to the time-dependent reliability analysis including both random and interval variables (POK-iTRA). By inheriting the processing strategy of time parameter and stochastic processes, the iTRA problem is converted into the HRA problem with multiple interval variables. Finally, four cases are used to show the accuracy and efficiency of the proposed method.

Importance Sampling for Slow-Fast Diffusions Based on Moderate Deviations

We consider systems of slow-fast diffusions with small noise in the slow component. We construct provably logarithmic asymptotically optimal importance schemes for the estimation of rare events based on the moderate deviations principle. Using the subsolution approach we construct schemes and identify conditions under which the schemes will be asymptotically optimal. Moderate deviations--based importance sampling offers a viable alternative to large deviations importance sampling when the events are not too rare. In particular, in many cases of interest one can indeed construct the required change of measure in closed form, a task which is more complicated using the large deviations--based importance sampling, especially when it comes to multiscale dynamically evolving processes. The presence of multiple scales and the fact that we do not make any periodicity assumptions for the coefficients driving the processes complicate the design and the analysis of efficient importance sampling schemes. Simulation studies illustrate the theory.

Efficient Particle MCMC with GMM Likelihood Representation

Particle Markov Chain Monte Carlo (PMCMC) is a widely used method to handle estimation problem in the context of nonlinear structural dynamic models whose likelihood function is analytically intractable. PMCMC can be constructed upon a GMM likelihood representation when one does not want to rely on the structural form of the measurement equation (Gallant et al 2016). It only requires to compute moment conditions available from the structural model. However, particle filter with GMM may suffer from high degeneracy of particle weights which severely affects the accuracy of Monte Carlo approximations and in turn Markov Chain Monte Carlo estimates. This work is concerned with revising particle GMM algorithm as proposed in Gallant et al in order to reduce the depletion problem. Estimation results of stochastic volatility models show that the efficient block sampling strategy as proposed in Doucet et al (2006) can outperform particle GMM and in turn deliver more reliable MCMC estimates. Auxiliary particle filter (Doucet et al, 2011) is also proposed as an alternative strategy to the block sampling approach. However, in the intended experiments it does not seem to be very effective. Thus some of the assumptions needed to estimate structural nonlinear state space models can be weakened and requiring only available moment conditions without affecting dramatically the conclusions.

GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.

MotivationMany methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters.ResultsThe proposed method, GPseudoClust, is a novel approach that jointly infers pseudotemporal ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with non-parametric Bayesian clustering methods, efficient Markov Chain Monte Carlo sampling and novel subsampling strategies which aid computation. We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings.Availability and implementationAn implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/GPseudoClust.Supplementary information Supplementary data are available at Bioinformatics online.

Bayesian inference approach to inverse problems in a financial mathematical model

This paper investigates an inverse problem of option pricing in the extended Black–Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of an MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

A modified sampling plan by variables with an adjustable mechanism for lot sentencing

Acceptance sampling has been used as a method of determining if submitted product lots meet required quality standards. Recently, several sampling plans with different strategies have been developed to tackle various types of situations and requirements, and to increase their performance and efficiency. In particular, repetitive group sampling plan (RGSP) has been proposed and proven to be an efficient sampling scheme. However, it has its drawbacks that occur when the sampling is repeated until the lot can be successfully determined as either accepted or rejected; in theory, the number of sampling times might be infinite. Therefore, this paper proposes a modified version of variables RGSP (VRGSP) with an adjustable mechanism for lot sentencing (Adj-VRGSP), i.e., by setting a maximum allowable sampling time. The operating characteristic (OC) and average sample number (ASN) functions of the proposed plan are derived along with the step-by-step operating procedure. The determination of plan parameters is formulated as an optimization model with nonlinear constraints related to quality and risk requirements. Additionally, the performance of the proposed plan is examined, discussed, and compared with that of the conventional sampling plans. An application is also presented as an example illustrating the effectiveness of the proposed plan.

Evaluating Efficiency of Sampling Schemes in Tropical Natural Forests: Review and Simulation Experience from Kenya

Forest measurements, especially in natural forests are cumbersome and complex. 100% enumeration is costly and inefficient. This study sought to find out reliable, efficient and cost-effective sampling schemes for use in tropical rain forest (TRF), moist montane forest (MMF) and dry woodland forest (DWF) in Kenya. Forty-eight sampling schemes (each combining sampling intensity (5, 10, 20, 30%), plot size (25, 50, 100, 400 m2) and sampling technique (simple random sampling, systematic sampling along North-South and along East-West orientations) were generated for testing estimates of forest attributes such as regeneration through simulation using R-software. Sampling error and effort were used to measure efficiency of each sampling scheme in relation to actual values. Though forest sites differed in biophysical characteristics, cost of sampling increased with decreasing plot size regardless of the forest type and attribute. Accuracy of inventory increased with decreasing plot size. Plot sizes that captured inherent variability were 5mx5m for regeneration and trees ha-1 across forest types but varied between forest types for basal area. Different sampling schemes were ranked for relative efficiency through simulation techniques, using regeneration as an example. In many instances systematic sampling-based sampling schemes were most effective. Sub-sampling in one-hectare forest unit gave reliable results in TRF (e.g. SSV-5mx5m-30%) and DWF (e.g. SSV-10mx10m-30%) but not in MMF (5mx5m-100%). One-hectare-complete-inventory method was found inevitable for regeneration assessment in montane forest.

Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform

The change of fraction vegetation cover (FVC) is the key ecological index for vegetation dynamics of dryland ecosystem. However, it is difficult to directly map woody vegetation and herbaceous vegetation in the dryland from the satellite images due to the mixture of their distribution at small scale. Emerging UAV remote sensing provides a good opportunity to capture and quantify the distribution of the sparse vegetation in the drylands ecosystem. In this study, we proposed a new method to classify woody vegetation and herbaceous vegetation and calculate their FVC based on the high-resolution orthomosaic generated from UAV images by the machine learning algorithm of classification and regression tree (CART). This proposed method was validated and evaluated by visual interpretation, the detailed ground measurement dataset of 4832 trees and 18,798 shrubs and three popular machine learning algorithms of Support Vector Machine(SVM), Random Forest(RF), Gradient Boosting Decision Tree(GBDT). The overall assessments showed good overall accuracy (0.78), average accuracy (0.76), and the Kappa coefficient (0.64). The FVC of woody vegetation calculated from orthomosaic agreed well with that estimated from ground measurements. Both group of FVC have a stable linear relationship over different spatial scales. The proposed method showed higher efficiency of 166%, 111% and 290% than SVM, RF, GBDT respectively. A new optimized model was developed to reduce the workload of vegetation investigation and to design more efficient sampling strategies. The proposed method was incorporated into an interactive web-based software “UAV- High Resolution imagery Analysis Platform” (UAV-HiRAP, http://www.uav-hirap.org). Our study demonstrates that UAV-HiRAP combined with UAV platform can be a powerful tool to classify woody vegetation and herbaceous vegetation and calculate their FVC for sparse vegetation in the drylands. The new optimization model will inspire researchers to design more effective sampling strategies for future field investigation.

Fast top-k similarity search in large dynamic attributed networks

In this paper, we study the problem of retrieving top-k nodes that are similar to a given query node in large dynamic attributed networks. To tackle this problem, we propose a fast Attribute augmented Single-source Path similarity algorithm (ASP). Our ASP constructs an attribute augmented network that integrates both node structure and attribute similarities through similarity scores computed by an efficient single-source path sampling scheme. It also contains simple and effective updating schemes to maintain similarity scores for dynamic edge insertions and deletions. We provide an upper bound of the sampling size of ASP for obtaining an ϵ-approximation estimation of similarity scores with probability at least 1−δ. We theoretically prove that the sampling size and computational complexity of ASP are significantly lower than that of deploying the currently known most effective method Panther. Implementation results from extensive experiments in both synthetic networks and real-world social networks show that our ASP achieves better convergence performance, runs faster than the baselines, and effectively captures both structure and attribute similarities of the nodes. Specifically, we show that our algorithm can return top-k similar nodes for any query node in the real-world network at least 44x faster than the state-of-the-art methods, and update similarity scores in about 1 second for a batch of network modifications in a network of size 1,000,000.

Efficient determination of axon number in the optic nerve: A stereological approach

Quantifying the number of axons in the optic nerve is of interest in many research questions. Here, we show that a stereological method allows simple, efficient, precise and unbiased determination of the total axon number in the murine optic nerve. Axons in semi-thin optic nerve cross sections from untreated eyes (n = 21) and eyes subjected to retinal damage by intravitreous NMDA injections (n = 32) or PBS controls (n = 5) were manually identified, counted and digitally labeled by hand. A stereological procedure was empirically tested with systematic combinations of different sampling methods (simple random sampling without replacement, systematic uniform random sampling, stratified random sampling) and sampling parameters. Extensive numerical Monte Carlo experiments were performed to evaluate their large-sample properties. Our results demonstrate reliable determination of total axon number and superior performance compared to other methods at a small fraction of the time required for a full manual count. We specify suitable sampling parameters for the adoption of an efficient stereological sampling scheme, give empirical estimates of the additionally introduced sampling variance to facilitate experimental planning, and offer AxonCounter, an easy-to-use plugin implementing these stereological methods for the multi-platform image processing application NIH ImageJ.