Search

Summary: Deep neural networks for medical image segmentation often face the problem of insufficient clean labeled data. Although non‐expert annotations are more readily accessible, these low‐quality annotations lead to significant performance degradation of existing neural network methods. In this paper, we focus on robust learning of medical image segmentation with noisy annotations and propose a novel noise‐tolerant framework based on dual‐strategy sample selection, which selects the informative samples to provide effective supervision information. First, we propose the first round of sample selection by designing a novel joint loss, which includes conventional supervised loss and regularization loss. To further select information‐rich samples, we propose confidence‐based pseudo‐label sample selection from a novel perspective as the complement. The dual strategies are used in a collaborative manner and the network is optimized with mined informative samples. We conducted extensive experiments on datasets with both simulated noisy labels and real‐world noisy labels. For instance, on a simulated dataset with 25% noise ratio, our method achieves segmentation Dice value with 90.56% ±$$ \pm $$ 0.03%. Furthermore, increasing the noise ratio to 95%, our method still maintains a high Dice value of 73.85% ±$$ \pm $$ 0.28% compared to other baselines. Extensive results have demonstrated that our method can weaken the effects of noisy labels on medical image segmentation. [ABSTRACT FROM AUTHOR]

AbstractProbability surveys are a major source of population representative data for policy research and program evaluation. However, the data come with the added complications of being observational and selected with unequal probabilities. Propensity score adjustments have become increasingly popular for inferring causal relationships in non-randomized studies, but when using survey data, estimates of the population level causal effect may be biased if the sampling design is not adequately adjusted for. The current practice of using propensity score estimators with complex surveys is somewhat ad-hoc. We propose a potential-outcome super-population framework to streamline the causal analysis. We also develop propensity-score-and-survey weighted estimators and corresponding variance estimators, as well as their asymptotic properties. Our framework clarifies the confusion regarding the use of survey weighted propensity score in practice. The choice actually depends on the available sampling weights. Various estimators are compared in a simulation study, which shows that the proposed estimators perform better than the competing methods in terms of bias and confidence interval coverage when treatment effects are heterogeneous. To address an important public health issue, we evaluate the impact of e-cigarette use on future tobacco use intention in teens, using a large nationally representative survey in the United States. [ABSTRACT FROM AUTHOR]

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39‐bus system and the realistic regional power system of Eastern China show the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring. [ABSTRACT FROM AUTHOR]

Many countries have embarked on decentralization as a way to mitigate separation tendencies. Even though decentralization could satisfy some separation tendencies, the central government might be unable or unwilling to award the localities the level of decentralization they require to stay in the union. This mismatch may give rise to domestic violence and internal conflict. We propose a simple two-stage model that combines the decentralization and domestic terrorism literature in a single structural model, which can be readily tested using existing data in the disciplines. As long as the actual decentralization level meets the unobserved desired value by the localities, there will be a high likelihood of observing a zero incidence of domestic terrorism. Otherwise, the incidents happen regularly with mitigating effects from decentralization. Our model also accounts for the left-censoring of the terrorism data at zero. We examine our model using 5-year interval panel data of more than 60 countries based on the information for 1970–2019. Our results indicate a strong negative and statistically significant relationship between fiscal decentralization and domestic terrorism events. One percent increase in fiscal decentralization is associated with a two to three percent decline in domestic terrorism incidents. The coefficients of the selection model and domestic terrorism model generally conform to the literature. [ABSTRACT FROM AUTHOR]

This study investigates the commitment of farmer members to traditional and hybrid cooperatives, and examines how and why their commitment has changed over time. The empirical basis consists of three surveys of representative samples of Finnish farmers conducted in 2010, 2013 and 2016. Dairy cooperatives in Finland have a traditional organizational form. Animal breeders deliver to hybrid cooperatives that have some external investors, but farmers have the majority of the votes. In both industries, the farmers are committed to their cooperatives and increasingly so over the years. This is particularly true of dairy farmers. However, the members' commitment is based more on their satisfaction with the cooperatives' business activities rather than on any cohesion within the cooperative societies. One explanation for this is that primary agriculture is becoming more consolidated, with fewer but larger farms whose owners are business oriented and professional. [ABSTRACT FROM AUTHOR]

The goal of domain adaptation (DA) is to develop a robust decision model on the source domain effectively generalize to the target domain data. State-of-the-art domain adaptation methods typically focus on finding an optimal inter-domain invariant feature representation or helpful instances from the source domain. In this paper, we propose a kernel extreme learning machine with discriminative transfer features and instance selection (KELM-DTF-IS) for unsupervised domain adaptation tasks, which consists of two steps: discriminative transfer feature extraction and classification with instance selection. At the feature extraction stage, we extend cross domain mean approximation(CDMA) by incorporating a penalty term and develop discriminative cross domain mean approximation (d-CDMA) to optimize the category separability between instances. Subsequently, d-CDMA is integrated into kernel ELM-AutoEncoder(KELM-AE) for extracting inter-domain invariant features. During the classification process, our approach uses CDMA metrics to compute a weights to each source instances based on their impact in reducing distribution differences between domains. Instances with a greater effect receive higher weights and vice versa. These weights are then used to distinguish and select source domain instances before incorporating them into weight KELM for proposing an adaptive classifier. Finally, we apply our approach to conduct classification experiments on publicly available domain adaptation datasets, and the results demonstrate its superiority over KELM and numerous other domain adaptation approaches. [ABSTRACT FROM AUTHOR]

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods. [ABSTRACT FROM AUTHOR]

This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R. [ABSTRACT FROM AUTHOR]

Coring is essential for understanding subsurface rock properties and optimizing reservoir characterization. This study explores the application of the Multi-Resolution Graph-Based Clustering (MRGC) method for efficient sample selection in coring programs. Through a detailed analysis of well logs and core data, focusing on mechanical properties like rock strength and elasticity, geomechanical units are identified using the MRGC method. By aligning selection criteria with program objectives and integrating various data sources, the MRGC method optimizes sample selection, offering a comprehensive insight into subsurface geomechanical properties while minimizing core sample requirements. The systematic and targeted sample selection facilitated by the MRGC method ensures that core samples accurately represent the geomechanical characteristics of different field layers. By incorporating petrophysical logs and geomechanical parameters, a model was developed for formations in Gachsaran, Asmari, and Pabdeh-Gorpi-Gadovan. Ultimately, 11 geomechanical units were distinguished from 12 coring wells based on 4 input parameters using the MRGC method. This method improves sample selection accuracy and efficiency, validates geomechanical unit definitions, and offers valuable insights into subsurface geomechanics. [ABSTRACT FROM AUTHOR]

This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from 'site-specific' unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator is consistent and asymptotically normal. A Monte Carlo experiment illustrates the small sample properties of our estimator. We apply our procedure to the estimation of a female wage offer equation in the United States and the results show the relevance of spatial differencing to account for 'site-specific' unobserved effects. [ABSTRACT FROM AUTHOR]

Summary: We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine‐learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsen sample selection bias. We propose two control function approaches that remove the effects of selection bias before training and find that they reduce mean‐squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re‐election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) suffer from noisy-labeled data because of the risk of overfitting. To avoid the risk, in this paper, we propose a novel DNN training method with sample selection based on adaptive k-set selection, which selects k (< n, where n is the number of training samples) samples with a small noise-risk from the whole n noisy training samples at each epoch. It has the strong advantage of guaranteeing the performance of the selection theoretically. Roughly speaking, a regret, which is defined by the difference between the actual selection and the best selection, of the proposed method is theoretically bounded, even though the best selection is unknown until the end of all epochs. The experimental results on multiple noisy-labeled datasets demonstrate that our sample selection strategy works effectively in the DNN training; in fact, the proposed method achieved the best or the second-best performance among state-of-the-art methods, while requiring a significantly lower computational cost. [ABSTRACT FROM AUTHOR]

The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems, in which the objective function is stochastic and the constraints are deterministic. The method proposed in this paper is a proximal gradient method that can also be applied to the composite optimization problem min |$f(x) + h(x)$|⁠ , where |$f$| is stochastic and |$h$| is convex (but not necessarily differentiable). Adaptive sampling methods employ a mechanism for gradually improving the quality of the gradient approximation so as to keep computational cost to a minimum. The mechanism commonly employed in unconstrained optimization is no longer reliable in the constrained or composite optimization settings, because it is based on pointwise decisions that cannot correctly predict the quality of the proximal gradient step. The method proposed in this paper measures the result of a complete step to determine if the gradient approximation is accurate enough; otherwise, a more accurate gradient is generated and a new step is computed. Convergence results are established both for strongly convex and general convex |$f$|⁠. Numerical experiments are presented to illustrate the practical behavior of the method. [ABSTRACT FROM AUTHOR]

From the inventory of the health insurer AOK in 2004, we draw a sample of a quarter million people and follow each person's health claims continuously until 2013. Our aim is to estimate the effect of a stroke on the dementia onset probability for Germans born in the first half of the 20th century. People deceased before 2004 are randomly left-truncated, and especially their number is unknown. Filtrations, modelling the missing data, enable circumventing the unknown number of truncated persons by using a conditional likelihood. Dementia onset after 2013 is a fixed right-censoring event. For each observed health history, Jacod's formula yields its conditional likelihood contribution. Asymptotic normality of the estimated intensities is derived, related to a sample size definition including the number of truncated people. The standard error results from the asymptotic normality and is easily computable, despite the unknown sample size. The claims data reveal that after a stroke, with time measured in years, the intensity of dementia onset increases from 0.02 to 0.07. Using the independence of the two estimated intensities, a 95% confidence interval for their difference is [0.053, 0.057]. The effect halves when we extend the analysis to an age-inhomogeneous model, but does not change further when we additionally adjust for multi-morbidity. [ABSTRACT FROM AUTHOR]

We propose a parametric estimation procedure for linear panel data models with sample selection and heterogeneous coefficients that are present in both outcome model and selection model. Our two‐step estimation procedure accounts for endogeneity from the selection process and endogeneity from correlation between the individual unobserved heterogeneity and the observed covariates using control function like methods. Conditional linear projections are used to establish a tractable approach that builds upon the original Heckman correction to sample selection. Monte Carlo simulations illustrate the finite sample properties of our estimator and demonstrate that our proposed estimator outperforms standard estimators. We apply the proposed approach to estimate gender differences in high‐stakes time‐constrained decisions using Elo ratings data from the World Chess Federation. When addressing both sources of endogeneity, we find a much larger gender skill gap and substantial differences across the genders in strategically selecting into time‐constrained matches. [ABSTRACT FROM AUTHOR]

This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact. [ABSTRACT FROM AUTHOR]

This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning and eventually memorize data with noisy labels. This property allows for the separation of clean and noisy samples from a loss distribution. In recent years, semi-supervised learning, which divides training data into a set of labeled clean samples and a set of unlabeled noisy samples, has achieved impressive results. However, this strategy has two significant problems: (1) the accuracy of dividing the data into clean and noisy samples depends strongly on the network's performance, and (2) if the divided data are biased towards the unlabeled samples, there are few labeled samples, causing the network to overfit to the labels and leading to a poor generalization performance. To solve these problems, we propose the curriculum regularization and adaptive semi-supervised learning (CRAS) method. Its key ideas are (1) to train the network with robust regularization techniques as a warm-up before dividing the data, and (2) to control the strength of the regularization using loss weights that adaptively respond to data bias, which varies with each split at each training epoch. We evaluated the performance of CRAS on benchmark image classification datasets, CIFAR-10 and CIFAR-100, and real-world datasets, mini-WebVision and Clothing1M. The findings demonstrate that CRAS excels in handling noisy labels, resulting in a superior generalization and robustness to a range of noise rates, compared with the existing method. [ABSTRACT FROM AUTHOR]

Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured. [ABSTRACT FROM AUTHOR]

Crop mapping using remote sensing data is essential for predicting crop yield and future requirements. However, it can be accomplished by having full ground truth data corresponding to the samples captured by remote sensors. In a practical scenario, generally, we have limited ground-truth data where available crop samples are less than the number of spectral bands of a hyperspectral image. This paper proposes a sample selection method to select samples from a hyperspectral image based on similarity measure and fuzziness in the samples. We use a single-ground truth pixel of each crop for crop-sample selection. Firstly, we select the number of samples for each crop class using Spectral Information Diversion and tangent of Spectral Angle Mapper ( SS tan ) measure from a selected region of the hyperspectral image. Then, we compute fuzziness for each selected crop sample to generate a set of final samples. We have evaluated the proposed method on an Airborne Visible near InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset and two benchmark datasets, i.e., Indian Pines and Salinas. Finally, classification results using three supervised classifiers, namely, support vector machine, k-nearest neighbourhood and random forest, have been shown in terms of overall accuracy and kappa coefficient. It can be demonstrated that the proposed method outperforms some prevailing methods by at least 4.5 % for 5 samples and 11% for 10 samples for the Indian Pines dataset. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Soziologie is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The eighth wave of the Survey of Health, Aging and Retirement in Europe comprises a subsample of respondents who were asked to participate in a measurement of physical activity using thigh-worn accelerometers. This paper describes the process for obtaining consent, identifies determinants of consent, and analyzes the aggregated results of the accelerometer measurements for bias due to sample selection. The overall consent rate in the Survey of Health, Aging and Retirement in Europe accelerometer study was 54%, with variations between countries ranging from 34% to 70%. Multivariate logistic regressions show that various factors are correlated with consent such as respondents' age, self-reported moderate activity, computer literacy, willingness to answer questions, and the interviewers' age. After introducing inverse probability weights, there appears to be only a small and insignificant influence of participant selection and consent. [ABSTRACT FROM AUTHOR]

Örnekler; bir kavrama ait tanımları açıklamak, kavrama ait olan ya da olmayan durumları detaylandırmak veya somutlaştırmak amacıyla, kavramın özel durumlarını oluşturan yapılardır. Bu araştırmada ortaokul matematik öğretmenlerinin; oran-orantı konusunda örnekleri nasıl belirlediklerini, derslerin farklı aşamalarında ne tür örnekleri kullandıklarını tespit etmek ve örnekleri kullanırken yararlandıkları kaynakların neler olduğunu ortaya çıkarmak amaçlanmıştır. Araştırmada tarama yöntemi türlerinden biri olan kesitsel tarama yöntemi kullanılmıştır. Araştırmanın katılımcıları, uygun örnekleme yöntemiyle belirlenen 97 ortaokul matematik öğretmeninden oluşmaktadır. Araştırmanın verileri, öğretmenlere uygulanan açık uçlu sorulardan oluşan anket ve yarı-yapılandırılmış mülakatlar yardımıyla toplanmıştır. Elde edilen veriler betimsel olarak analiz edilmiştir. Öğretmenlerin verdikleri cevaplara göre kategoriler oluşturulmuştur. Araştırma sonucunda öğretmenlerin kullandıkları örnek türlerinin dersin aşamalarına göre farklılık gösterdiği tespit edilmiştir. Bununla birlikte öğretmenlerin günlük hayatla ilişkili örnekleri dersin her aşamasında kullandıkları belirlenmiştir. Öğretmenlerin örnekleri kullanırken yararlandıkları kaynakların; “yardımcı kaynaklar”, “z-kitap”, “ders kitabı”, “internet” ve “EBA” ile “ÖDSGM” olduğu görülmüştür. Bazı öğretmenler ise herhangi bir kaynaktan yararlanmadan kendilerinin örnekler hazırladıklarını ifade etmişlerdir. Öğretmenlerin örnek seçiminde sadece bir kaynağa bağlı kalmadıkları, farklı kaynaklardan yararlandıkları tespit edilmiştir. Bunun yanı sıra öğretmenlerin mesleki kıdemi arttıkça EBA ve internet gibi dijital kaynaklar yerine daha çok ders kitaplarındaki örnekleri tercih ettikleri belirlenmiştir.

Consuming too much fat, sugar, and salt is associated with adverse health outcomes. Food reformulation is one possible strategy to enhance the food environment by improving the nutritional quality of commercial products. However, food reformulation faces many hindrances. One way to alleviate some of these hindrances is to embrace a multicriteria approach that is based on a market inventory. In this objective, additional sensory screening and water content analyses allow going beyond nutrition and composition information on the packaging. However, due to feasibility reasons for later in-depth analyses, it is necessary to work with several reduced and manageable products. To the best of the authors' knowledge, in the literature, there is no sample selection approach taking into account multiple criteria as a base for future food reformulation. The overall aim of this paper is to propose a method to select the best representative products from the market base, for future reformulation by going beyond nutrition and composition information on the packaging. This approach considered therefore nutrition, composition, economic, water content, and sensory information with the example of the cookies market. The first step is the creation of an extensive cookie database including sensory and water content information. In total 178 cookies among the French market were identified, then focus was placed on 62 chocolate chip cookies only. Sensory screening and water content analyses of all 62 products were conducted. The second step is to make an informed subset selection, thanks to a cluster analysis based on 11 nutrition, composition, and water content variables. A representative subset of 18 cookies could be derived from the obtained clusters. The representativity was evaluated with statistical uni- and multivariate analyses. Results showed a broad variety of chocolate chips cookies with a large nutritional, compositional, water content, and sensory differences. These results highlighted the first paths for future reformulation in this product category and showed the importance to include physical product information beyond the information on the packaging. This complete database on the selected cookies constituted a solid base for identifying future reformulation levers, in order to improve the nutritional quality and health.

Estimating true treatment effects in the presence of selection bias is a common problem in program evaluation, but it is uncommon to quantify the share of a total apparent effect that is “true”. We identify multiple methods for doing so, in the context of isolating the true causal influence of the built environment on travel behavior in the presence of residential self-selection (RSS). Among the different approaches for dealing with self-selection, we use three: statistical control (SC) modeling, propensity score-based techniques (PS), and sample selection (SS) modeling. Our objective is to identify multiple ways to estimate the proportion of the total apparent effect of the built environment on travel behavior that is due to the built environment itself, which we call the “built environment proportion”, or BEP. We present and evaluate three categories of methods for estimating this measure (each native to one of the approaches): variance explained, modular effects, and treatment effects. A BEP formula associated with a given method can be applied to its native approach or cross-applied to other approaches. We identify and enumerate 47 potential BEPs. The methods presented here can be applied in other contexts involving treatment evaluation in the presence of selection bias.

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods.

Cropland is the foundation of agriculture and the cornerstone of ensuring national food security. Ongoing advancement of China’s urbanization and social economy results in a tendency toward ‘non-agriculturalization’ of cropland in pursuit of economic benefits, which greatly threatens food security. Therefore, long-term monitoring of cropland is urgently needed. Based on the Google Earth Engine (GEE) platform, this paper uses the Landsat series and Sentinel-2 multisource images from 1990–2020 to dynamically monitor cropland in Yunnan Province. First, a multiresolution collaborative sample selection method was proposed and sample labels with resolutions better than 30 m from 1990 to 2015 were obtained. An ‘impervious surface-spectrum-texture-geometry’ (ISTG) feature system was constructed and the Jeffries-Matusita (JM) distance method was used for feature optimization. Finally, the proposed collaborative classification system method of land classification and binary classification was used to extract cropland in Yunnan Province. The results show that: (1) The accuracy of cropland extraction was highly reliable, with an overall accuracy (OA) wall-to-wall ranging from 93.15–98.14% and a kappa coefficient (Kappa) ranging from 86.29–96.26%. (2) The change trend of the cropland area in Yunnan Province from 1990 to 2020 exhibited certain fluctuations, but overall decreased annually. (3) The province’s average annual cropland area is 567.71 × 103 hectares. The proposed method has great potential in large-scale cropland extraction.

AbstractFinancial inclusion is one of the development priorities of the Ethiopian Government. However, factors, which might affect it are not explored adequately with appropriate methods. To examine the relationship between financial inclusion and its demand-side determinants, we applied a simple probit model and two probit models with sample selection to the 2018/19 Ethiopia Socioeconomic Survey (ESS4). Our simple probit model estimation results show that male, older, non-Muslim, married, more educated, richest, mobile users or people who are aware of how to open a bank account are more likely to have a formal account than their counterparts. However, being from a rural area or from a larger household size reduces the likelihood of having an account. Results from the probit selection model show that rural residents, employed, richer people, or people who are out of labor force are more likely to use their account to save money than their counterparts. Furthermore, the third model shows that being non-Muslim, married, widowed, or poorer reduces the likelihood of account use than their counterparts. Thus, policies should target the most vulnerable groups of the society. Especially, rural households, women, the Muslim population, the poor, less educated people, and the young should be the focus of financial inclusion endeavors of the Government. Parallel to these policy measures, awareness of opening bank account and financial education programs should be promoted.

In the paper, we first explore a novel problem of training the robust Graph Neural Networks (GNNs) against noisy graphs and noisy labels. To the problem, we propose a general Self-supervised Robust Graph Neural Network framework that consists of three modules: graph structure learning, sample selection, and self-supervised learning. Specifically, we first employ a graph structure learning approach to obtain an optimal graph structure. Next, using this structure, we use a clustering algorithm to generate pseudo-labels that represent the clusters. We then design a sample selection strategy based on these pseudo-labels to select nodes with clean labels. Additionally, we introduce a self-supervised learning technique where low-level layer parameters are shared with GNNs to predict pseudo-labels. We jointly train the graph structure learning module, the GNNs model, and the self-supervised model. Finally, we conduct extensive experiments on four real-world datasets, demonstrating the superiority of our methods compared with state-of-the-art methods for semi-supervised node classification under noisy graphs and noisy labels. [ABSTRACT FROM AUTHOR]

This is the first empirical study to examine determinants of both the probability and the volume of foreign direct divestment (FDD) between countries, applying sample selection methods for panel data. The data cover 137 host countries and 169 source countries, from 2004 to 2012. Empirical evidence shows that market size and GDP growth of both host and source countries, as well as a bilateral investment treaty, discourage divestment. By contrast, sharing a common currency has a positive impact on divestment. Distance negatively affects the divestment amount but does not show any statistically significant impact on the probability of divestment. Neither do political stability, colonial relationship, common language, or common religion play an important role in FDD. This study opens up an area for future research in terms of both theory and empirics since FDD is still a neglected area in international economics. [ABSTRACT FROM AUTHOR]

Copyright of Anadolu University Journal of Education Faculty (AUJEF) is the property of Anadolu Universitesi Egitim Fakultesi Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the sociodemographic characteristics associated with cigarette smoking and quitting decisions allows policy decision-making to attempt to curtail smoking. Individual smoking and quitting decisions are examined using an endogenous sample selection model. Using data for 4,110 individuals from the 2011 Malaysian Global Adult Tobacco Survey, a probit model is estimated for quitting, subject to sample selection for smokers. Marginal effects of exposure variables are calculated for the probability of smoking and probability of quitting conditional on smoking. Ethnic Malays/Bumiputera, urbanites, ignorance of the hazards associated with secondhand smoke, lack of smoking rules at home, and lack of religious restriction contribute to smoking likelihoods. Older age, being a female, ethnic Chinese, married, at home smoking prohibition, and knowledge of secondhand smoke risks are associated with higher probability of quitting conditional upon smoking. Working in non-government occupations, individuals of other ethnic background, and smoking consent at home are associated with lower quitting probability conditional upon smoking. Health awareness and educational programs should be directed at specific population groups with the above sociodemographic characteristics to discourage smoking initiation and promote quitting among smokers. [ABSTRACT FROM AUTHOR]

For the past forty years, Ethiopia has been promoting sustainable land management activities to enhance agricultural productivity. This study was intended to identify the factors determining farmers’ adoption and continued use of soil bund measures in the Handosha watershed, Omo-Gibe river basin. A multistage sampling technique was used to select 340 households using the Heckman sample selection model. A total of 235 (69.12%) households adopted soil bunds, but only 89 (37.87%) of them were sustainably practicing soil bunds on their farm plots. Most adopters widely practiced soil bunds (49.42%), followed by stone bund (15.9%), and Fanyajuu (10%). The empirical results of the Heckman sample selection model showed that the farming experience, land tenure security, and perception of profitability of conservation measures were significantly positively affected the adoption of soil bund. Whereas, farm plot size and participation in off farm activities significantly negatively influenced the adoption of soil bund. Sustainable use of soil bund measures were significantly positively influenced by land tenure security, family size, and frequency of extension contact, whereas the distance between farm plots and home, and farm plot size were negatively affected. As a result, a design of agro-ecological-based soil and water conservation (SWC) measures was essential in reducing farmland vulnerability to soil erosion and food insecurity. It has been concluded that conservation practices should not only focus on the implementation and biophysical factors but also consider the socioeconomic interests of the farmers to improve the sustainable use of conservation technologies.

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domain-specific functionalities. We present the python library scikit-matter that targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows.

Cyberbullying detection has attracted many researchers to detect negative comments deployed on communication platforms as cyberbullying can take many forms: verbal, implicit, explicit, or even nonverbal. The successful growth of social media in recent years has opened new perspectives on the detection of cyberbullying, although related research still encounters several challenges, such as data imbalance and expression implicitness. In this paper, we propose an automated cyberbullying detection framework designed to produce satisfactory results, especially when imbalanced short text and different dialects exist in the Arabic text data. In the proposed framework a new method to solve the imbalance problem is suggested, where the modified simulated annealing optimization algorithm is used to find the optimal set of samples from the majority class to balance the training set. This method has been evaluated using traditional machine learning algorithms including support vector machine, and deep learning algorithms including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). To generate a framework that can detect Arabic written cyberbullying on communication platforms, the accuracy, recall, specificity, sensitivity and mean squared error are used as the main performance indicators. The results indicate that the proposed framework can improve the performance of the tested algorithms, and Bi-LSTM outperforms other methods for cyberbullying classification.

This paper empirically analyses the relationship between urban form and Urban Heat Island (UHI) in a dataset of 523 European cities that matches remotely sensed land-use and surface temperature data. A UHI anomaly is defined as an uninterrupted streak of days where the temperature differential measured at 12.00 AM between the city core and its surroundings is higher than a given threshold. From this definition, three UHI indicators are obtained: mean intensity, mean duration of the event and occurrence rate. We study the influence of urban morphology on the UHI indictors with a Heckman model. A sample selection bias is detected for mean intensity and mean duration. The estimation results also show that some urban morphological features have a mitigating effect, while some others play a role at the adaptation level. [ABSTRACT FROM AUTHOR]

The building damage caused by natural disasters seriously threatens human security. Applying deep learning algorithms to identify collapsed buildings from remote sensing images is crucial for rapid post-disaster emergency response. However, the diversity of buildings, limited training dataset size, and lack of ground-truth samples after sudden disasters can significantly reduce the generalization of a pre-trained model for building damage identification when applied directly to non-preset locations. To address this challenge, a self-incremental learning framework (i.e., SELF) is proposed in this paper, which can quickly improve the generalization ability of the pre-trained model in disaster areas by self-training an incremental model using automatically selected samples from post-disaster images. The effectiveness of the proposed method is verified on the 2010 Yushu earthquake, 2023 Turkey earthquake, and other disaster types. The experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of collapsed building identification, with an average increase of more than 6.4% in the Kappa coefficient. Furthermore, the entire process of the self-incremental learning method, including sample selection, incremental learning, and collapsed building identification, can be completed within 6 h after obtaining the post-disaster images. Therefore, the proposed method is effective for emergency response to natural disasters, which can quickly improve the application effect of the deep learning model to provide more accurate building damage results. [ABSTRACT FROM AUTHOR]

SARS‐CoV‐2 vaccines give rise to positive externalities on population health, society and the economy in addition to protecting the health of vaccinated individuals. Hence, the social value of such a vaccine exceeds its market value. This paper estimates the willingness to pay (WTP) for a hypothetical SARS‐CoV‐2 vaccine (or shadow prices), in four countries, namely the United States (US), the United Kingdom, Spain and Italy during the first wave of the pandemic when COVID‐19 vaccines were in development but not yet approved. WTP estimates are elicited using a payment card method to avoid "yea saying" biases, and we study the effect of protest responses, sample selection bias, as well as the influence of trust in government and risk exposure when estimating the WTP. Our estimates suggest evidence of an average value of a hypothetical vaccine of 100–200 US dollars once adjusted for purchasing power parity. Estimates are robust to a number of checks. [ABSTRACT FROM AUTHOR]

As an effective semi-supervised learning algorithm, the co-training method trains two classifiers on two views independently. The unlabeled sample selection strategy in the self-labeled process is crucial for co-training. However, most of the existing strategies strongly depend on parameter settings and require re-calculating the confidence of unlabeled samples in each iteration. Inspired by the concept of natural neighbors introduced recently, a co-training method based on parameter-free and single-step unlabeled data selection strategy with natural neighbors (CT-NaN) is proposed in this paper. In CT-NaN, the confidence value of unlabeled samples is calculated in a parameter-free manner by analyzing the training data based on natural neighbors before the iteration of co-training, and it requires to be calculated only once in the whole process of co-training. Besides, CT-NaN is able to mitigate the negative effect of outliers because the training stops automatically when only outliers remain. Four groups of experiments with 22 data sets are conducted, and the results verify the effectiveness of CT-NaN when compared with 8 state-of-the-art co-training methods. [ABSTRACT FROM AUTHOR]

Pipelines are the most economical and sensible way to transport oil and gas. Long-distance oil and gas pipelines consist of many steel pipes or pipe fittings joined by welded girth welds, so girth welds are an essential part of pipelines. Owing to the limitations of welding conditions and the complexity of controlling weld quality in the field, some defects are inevitably present in girth welds and adjacent weld areas. These defects can lead to pipeline safety problems; therefore, it is necessary to perform failure risk assessment of pipeline girth welds. In this study, an artificial neural network model was proposed to predict the failure risk of pipeline girth welds with defects. Firstly, many pipeline girth weld failure cases, pipeline excavation, and inspection data were collected and analyzed to determine the main factors influencing girth weld failure. Secondly, a spatial orthogonal optimization method was used to select training samples for the artificial neural network model to ensure that the training sample set could cover the feature space with a minimum number of samples. Thirdly, a prediction model based on BP neural networks was established to predict the failure risk levels. The training dataset/testing dataset was 602/4215, and the prediction accuracy for all risks of girth welds achieved an acceptable level. This study can provide a valuable reference for pipeline operators to prevent pipeline accidents. [ABSTRACT FROM AUTHOR]