cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13711730
title: Carbon accounting for negative emissions technologies

description: Negative emissions technologies (NETs) are an essential part of most scenarios for achieving the Paris Agreement goal of limiting warming to below 2°C and for all scenarios that limit warming to 1.5 °C. The deployment of these technologies requires carbon accounting methods for a range of different purposes, such as determining the effectiveness of specific technologies or incentivising NETs. Although the need for carbon accounting methods is discussed within the literature on NETs, there does not appear to be a clear understanding of the range of different accounting challenges. Based on a systematic literature review this study identifies five distinct accounting issues related to NETs: 1. estimating total system-wide change in emissions/removals; 2. non-permanence; 3. non-equivalence of ‘no overshoot’ and ‘overshoot and removal’; 4. accounting for incentives for NETs; and 5. the temporal distribution of emissions/removals. Solutions to these accounting challenges are proposed, or alternatively, areas for further research and the development of solutions are highlighted. One key recommendation is that carbon accounting methods should follow a ‘reality principle’ to report emissions and removals when and where they actually occur, and an important overall conclusion is that it is essential to use the correct accounting method for its appropriate purpose. For example, consequential methods that take account of total system-wide changes in emissions/removals should be used if the purpose is to inform decisions on the deployment or incentivisation of NETs. Attributional methods, however, should be used if the purpose is to construct static descriptions of possible net zero worlds. <b>Key policy insights</b>Negative emissions technologies (NETs) raise a number of distinct carbon accounting challenges, the importance of which varies across different NETs.Attributional life cycle assessment is not an appropriate method for estimating the system-wide changes caused by the deployment of NETs.Consequential greenhouse gas accounting methods should be used to estimate system-wide changes, and should be used as much as possible for guiding incentives for NETs.Greenhouse gas accounting methods should follow a ‘reality principle’ to report emissions and removals when and where they actually occur. Negative emissions technologies (NETs) raise a number of distinct carbon accounting challenges, the importance of which varies across different NETs. Attributional life cycle assessment is not an appropriate method for estimating the system-wide changes caused by the deployment of NETs. Consequential greenhouse gas accounting methods should be used to estimate system-wide changes, and should be used as much as possible for guiding incentives for NETs. Greenhouse gas accounting methods should follow a ‘reality principle’ to report emissions and removals when and where they actually occur.
DOI: 10.6084/m9.figshare.14447211
title: QSAR models for insecticidal properties of plant essential oils on the housefly (<i>Musca domestica</i> L.)

description: The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult <i>M. domestica</i> housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.
DOI: 10.6084/m9.figshare.14447211.v1
title: QSAR models for insecticidal properties of plant essential oils on the housefly (<i>Musca domestica</i> L.)

description: The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult <i>M. domestica</i> housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.
DOI: 10.6084/m9.figshare.14483310
title: Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

description: Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure–activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.
DOI: 10.6084/m9.figshare.14483310.v1
title: Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

description: Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure–activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.
DOI: 10.6084/m9.figshare.9959789
title: Fast Robust Correlation for High-Dimensional Data

description: The product moment covariance matrix is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other results. Unfortunately, the product moment covariance and the corresponding Pearson correlation are very susceptible to outliers (anomalies) in the data. Several robust estimators of covariance matrices have been developed, but few are suitable for the ultrahigh-dimensional data that are becoming more prevalent nowadays. For that one needs methods whose computation scales well with the dimension, are guaranteed to yield a positive semidefinite matrix, and are sufficiently robust to outliers as well as sufficiently accurate in the statistical sense of low variability. We construct such methods using data transformations. The resulting approach is simple, fast, and widely applicable. We study its robustness by deriving influence functions and breakdown values, and computing the mean squared error on contaminated data. Using these results we select a method that performs well overall. This also allows us to construct a faster version of the DetectDeviatingCells method (Rousseeuw and Van den Bossche 2018) to detect cellwise outliers, which can deal with much higher dimensions. The approach is illustrated on genomic data with 12,600 variables and color video data with 920,000 dimensions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9959789.v2
title: Fast Robust Correlation for High-Dimensional Data

description: The product moment covariance matrix is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other results. Unfortunately, the product moment covariance and the corresponding Pearson correlation are very susceptible to outliers (anomalies) in the data. Several robust estimators of covariance matrices have been developed, but few are suitable for the ultrahigh-dimensional data that are becoming more prevalent nowadays. For that one needs methods whose computation scales well with the dimension, are guaranteed to yield a positive semidefinite matrix, and are sufficiently robust to outliers as well as sufficiently accurate in the statistical sense of low variability. We construct such methods using data transformations. The resulting approach is simple, fast, and widely applicable. We study its robustness by deriving influence functions and breakdown values, and computing the mean squared error on contaminated data. Using these results we select a method that performs well overall. This also allows us to construct a faster version of the DetectDeviatingCells method (Rousseeuw and Van den Bossche 2018) to detect cellwise outliers, which can deal with much higher dimensions. The approach is illustrated on genomic data with 12,600 variables and color video data with 920,000 dimensions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12294404
title: A Component-Position Model, Analysis and Design for Order-of-Addition Experiments

description: An order-of-addition experiment is a kind of experiment in which the response is affected by the addition order of materials or components. In many situations, performing the full design with all possible permutations of components is unaffordable, especially when the number of components is larger than four. We introduce a component-position model for analyzing data from such experiments and study associated problems. We further propose a new type of design, called component orthogonal array, as a fraction of the full design for order-of-addition experiments. It is shown that component orthogonal arrays have the same D-efficiency as the full design under our proposed model. Component orthogonal arrays also perform well under the existing pairwise ordering model. Two drug combination experiments are used to show the effectiveness of the proposed model and designs. Supplementary materials for this paper are available online.
DOI: 10.6084/m9.figshare.12294404.v1
title: A Component-Position Model, Analysis and Design for Order-of-Addition Experiments

description: An order-of-addition experiment is a kind of experiment in which the response is affected by the addition order of materials or components. In many situations, performing the full design with all possible permutations of components is unaffordable, especially when the number of components is larger than four. We introduce a component-position model for analyzing data from such experiments and study associated problems. We further propose a new type of design, called component orthogonal array, as a fraction of the full design for order-of-addition experiments. It is shown that component orthogonal arrays have the same D-efficiency as the full design under our proposed model. Component orthogonal arrays also perform well under the existing pairwise ordering model. Two drug combination experiments are used to show the effectiveness of the proposed model and designs. Supplementary materials for this paper are available online.
DOI: 10.6084/m9.figshare.11605536
title: Sequential Optimization in Locally Important Dimensions

description: Optimizing an expensive, black-box function f(·) is challenging when its input space is high-dimensional. Sequential design frameworks first model f(·) with a surrogate function and then optimize an acquisition function to determine input settings to evaluate next. Optimization of both f(·) and the acquisition function benefit from effective dimension reduction. Global variable selection detects and removes input variables that do not affect f(·) across the input space. Further dimension reduction may be possible if we consider local variable selection around the current optimum estimate. We develop a sequential design algorithm called <i>sequential optimization in locally important dimensions</i> (SOLID) that incorporates global and local variable selection to optimize a continuous, differentiable function. SOLID performs local variable selection by comparing the surrogate’s predictions in a localized region around the estimated optimum with the <i>p</i> alternative predictions made by removing each input variable. The search space of the acquisition function is further restricted to focus only on the variables that are deemed locally active, leading to greater emphasis on refining the surrogate model in locally active dimensions. A simulation study across multiple test functions and an application to the Sarcos robot dataset show that SOLID outperforms conventional approaches. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11899989
title: Adaptive Convex Clustering of Generalized Linear Models With Application in Purchase Likelihood Prediction

description: In the pricing of customized products, it is challenging to accurately predict the purchase likelihood of potential clients for each personalized request. The heterogeneity of customers and their responses to the personalized products leads to very different purchase behavior. Thus, it is often not appropriate to use a single model to analyze all the pricing data. There is a great need to construct distinctive models for different data segments. In this work, we propose an adaptive convex clustering method to perform data segmentation and model fitting simultaneously for generalized linear models. The proposed method segments data points using the fused penalty to account for the similarity in model structures. It ensures that the data points sharing the same model structure are grouped into the same segment. Accordingly, we develop an efficient algorithm for parameter estimation and study its consistency properties in estimation and clustering. The performance of our approach is evaluated by both numerical examples and case studies of real business data.
DOI: 10.6084/m9.figshare.11605536.v1
title: Sequential Optimization in Locally Important Dimensions

description: Optimizing an expensive, black-box function f(·) is challenging when its input space is high-dimensional. Sequential design frameworks first model f(·) with a surrogate function and then optimize an acquisition function to determine input settings to evaluate next. Optimization of both f(·) and the acquisition function benefit from effective dimension reduction. Global variable selection detects and removes input variables that do not affect f(·) across the input space. Further dimension reduction may be possible if we consider local variable selection around the current optimum estimate. We develop a sequential design algorithm called <i>sequential optimization in locally important dimensions</i> (SOLID) that incorporates global and local variable selection to optimize a continuous, differentiable function. SOLID performs local variable selection by comparing the surrogate’s predictions in a localized region around the estimated optimum with the <i>p</i> alternative predictions made by removing each input variable. The search space of the acquisition function is further restricted to focus only on the variables that are deemed locally active, leading to greater emphasis on refining the surrogate model in locally active dimensions. A simulation study across multiple test functions and an application to the Sarcos robot dataset show that SOLID outperforms conventional approaches. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12258488
title: The Reconstruction Approach: From Interpolation to Regression

description: <b>This article introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least squares. Some popular methods including kernel ridge regression can be viewed as its special cases. It is shown that the reconstruction idea not only provides different angles to look into existing methods, but also produces new effective experimental design and estimation methods for nonparametric models. In particular, for some methods of complexity</b>O(n3)<b>, where <i>n</i> is the sample size, this approach provides effective surrogates with much less computational burden. This point makes it very suitable for large datasets. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.11899989.v2
title: Adaptive Convex Clustering of Generalized Linear Models With Application in Purchase Likelihood Prediction

description: In the pricing of customized products, it is challenging to accurately predict the purchase likelihood of potential clients for each personalized request. The heterogeneity of customers and their responses to the personalized products leads to very different purchase behavior. Thus, it is often not appropriate to use a single model to analyze all the pricing data. There is a great need to construct distinctive models for different data segments. In this work, we propose an adaptive convex clustering method to perform data segmentation and model fitting simultaneously for generalized linear models. The proposed method segments data points using the fused penalty to account for the similarity in model structures. It ensures that the data points sharing the same model structure are grouped into the same segment. Accordingly, we develop an efficient algorithm for parameter estimation and study its consistency properties in estimation and clustering. The performance of our approach is evaluated by both numerical examples and case studies of real business data.
DOI: 10.6084/m9.figshare.12258488.v1
title: The Reconstruction Approach: From Interpolation to Regression

description: <b>This article introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least squares. Some popular methods including kernel ridge regression can be viewed as its special cases. It is shown that the reconstruction idea not only provides different angles to look into existing methods, but also produces new effective experimental design and estimation methods for nonparametric models. In particular, for some methods of complexity</b>O(n3)<b>, where <i>n</i> is the sample size, this approach provides effective surrogates with much less computational burden. This point makes it very suitable for large datasets. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.9959789.v1
title: Fast robust correlation for high dimensional data

description: The product moment covariance matrix is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other results. Unfortunately the product moment covariance and the corresponding Pearson correlation are very susceptible to outliers (anomalies) in the data. Several robust estimators of covariance matrices have been developed, but few are suitable for the ultrahigh dimensional data that are becoming more prevalent nowadays. For that one needs methods whose computation scales well with the dimension, are guaranteed to yield a positive semidefinite matrix, and are sufficiently robust to outliers as well as sufficiently accurate in the statistical sense of low variability. We construct such methods using data transformations. The resulting approach is simple, fast and widely applicable. We study its robustness by deriving influence functions and breakdown values, and computing the mean squared error on contaminated data. Using these results we select a method that performs well overall. This also allows us to construct a faster version of the DetectDeviatingCells method (Rousseeuw and Van den Bossche, 2018) to detect cellwise outliers, that can deal with much higher dimensions. The approach is illustrated on genomic data with 12,600 variables and color video data with 920,000 dimensions.
DOI: 10.6084/m9.figshare.11899989.v1
title: Adaptive Convex Clustering of Generalized Linear Models with Application in Purchase Likelihood Prediction

description: In the pricing of customized products, it is challenging to accurately predict the purchase likelihood of potential clients for each personalized request. The heterogeneity of customers and their responses to the personalized products leads to very different purchase behavior. Thus, it is often not appropriate to use a single model to analyze all the pricing data. There is a great need to construct distinctive models for different data segments. In this work, we propose an adaptive convex clustering method to perform data segmentation and model fitting simultaneously for generalized linear models (GLM). The proposed method segments data points using the fused penalty to account for the similarity in model structures. It ensures that the data points sharing the same model structure are grouped into the same segment. Accordingly, we develop an efficient algorithm for parameter estimation and study its consistency properties in estimation and clustering. The performance of our approach is evaluated by both numerical examples and case studies of real business data. Supplementary materials that include all the technical details, proofs, and Python codes used in the article are available online.