cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.18624224.v1
title: Hemisensory loss after ventriculoperitoneal shunt insertion: Case Report

description: We describe a case which developed hemisensory loss after ventriculoperitoneal shunt insertion.
DOI: 10.6084/m9.figshare.13517272
title: Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data

description: We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. In the presence of such label noise, conventional classification fails to obtain a classifier to be generalized to a population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies the Fisher consistency and enjoys the property of robustness. To accommodate various label noise mechanisms that occur in practice, we propose a flexible label noise model in a nonparametric way. We propose an efficient algorithm under the thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.
DOI: 10.6084/m9.figshare.13517272.v2
title: Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data

description: We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. In the presence of such label noise, conventional classification fails to obtain a classifier to be generalized to a population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies the Fisher consistency and enjoys the property of robustness. To accommodate various label noise mechanisms that occur in practice, we propose a flexible label noise model in a nonparametric way. We propose an efficient algorithm under the thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.
DOI: 10.6084/m9.figshare.14248539
title: Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials

description: Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.
DOI: 10.6084/m9.figshare.14248539.v2
title: Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials

description: Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a noncontiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models.
DOI: 10.6084/m9.figshare.14484929.v1
title: Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

description: Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches have emerged (Bayer, 2015; Graves, 2013; Pascanu et al., 2012), they remain poorly understand and irreconcilable with more traditional statistical modeling approaches (Box and Jenkins, 1976; Hamilton, 1994). We present a general class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling non-stationary dynamical systems arising in industrial applications. In particular, we analyze their capacity to characterize the non-linear partial autocorrelation structure of time series and directly capture dynamic effects such as seasonality and trends. Application of exponentially smoothed RNNs to forecasting electricity load, weather data, and stock prices highlight the efficacy of exponential smoothing of the hidden state for multi-step time series forecasting. The results also suggest that popular, but more complicated neural network architectures originally designed for speech processing are likely over-engineered for industrial forecasting and light-weight exponentially smoothed architectures, trained in a fraction of the time, capture the salient features while being superior and more robust than simple RNNs and autoregressive models. Additionally uncertainty quantification of Bayesian exponential smoothed RNNs is shown to provide improved coverage. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.14484930
title: Prediction of Future Failures for Heterogeneous Reliability Field Data

description: This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some combination of small sample sizes, censoring, and truncation. In such cases, we use a Bayesian hierarchical model to model jointly multiple lifetime distributions arising from different subpopulations of similar products. By borrowing information across subpopulations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Three applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.
DOI: 10.6084/m9.figshare.14143656
title: Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments

description: Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This complex forward model is computationally expensive but large-scale OSUEs require evaluation of this model numerous times, which makes it infeasible for comprehensive experiments. To tackle this issue, we develop a statistical emulator to facilitate large-scale OSUEs in the OCO-2 mission. Within each distinct spectral band, the emulator represents radiance output at irregular wavelengths as a linear combination of basis functions and random coefficients. These random coefficients are then modeled with nearest-neighbor Gaussian processes with built-in input dimension reduction via active subspace. The proposed emulator reduces dimensionality in both input space and output space, so that fast computation is achieved within a fully Bayesian inference framework. Validation experiments demonstrate that this emulator outperforms other competing statistical methods and a reduced order model that approximates the full-physics forward model.
DOI: 10.6084/m9.figshare.13517272.v1
title: Individual transition label noise logistic regression in binary classification for incorrectly-labeled data

description: We consider binary classification problem in the case where some observations in training data are incorrectly labeled. In presence of such label noise, conventional classification fails to obtain a classifier to be generalized to population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies Fisher consistency and enjoys robustness property. To accommodate various label noise mechanisms occurred in practice, we propose a flexible label noise model in a nonparametric way. An efficient algorithm is proposed under thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.
DOI: 10.6084/m9.figshare.14484930.v1
title: Prediction of Future Failures for Heterogeneous Reliability Field Data

description: This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some combination of small sample sizes, censoring, and truncation. In such cases, we use a Bayesian hierarchical model to model jointly multiple lifetime distributions arising from different subpopulations of similar products. By borrowing information across subpopulations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Three applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.
DOI: 10.6084/m9.figshare.14248538
title: An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes

description: Robustness analysis is an emerging field in the uncertainty quantification domain. It involves analyzing the response of a computer model—which has inputs whose exact values are unknown—to the perturbation of one or several of its input distributions. Practical robustness analysis methods therefore require a coherent methodology for perturbing distributions; we present here one such rigorous method, based on the Fisher distance on manifolds of probability distributions. Further, we provide a numerical method to calculate perturbed densities in practice which comes from Lagrangian mechanics and involves solving a system of ordinary differential equations. The method introduced for perturbations is then used to compute quantile-related robustness indices. We illustrate these “perturbed-law based” indices on several numerical models. We also apply our methods to an industrial setting: the simulation of a loss of coolant accident in a nuclear reactor, where several dozen of the model’s physical parameters are not known exactly, and where limited knowledge on their distributions is available.
DOI: 10.6084/m9.figshare.14143656.v1
title: Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments

description: Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This complex forward model is computationally expensive but large-scale OSUEs require evaluation of this model numerous times, which makes it infeasible for comprehensive experiments. To tackle this issue, we develop a statistical emulator to facilitate large-scale OSUEs in the OCO-2 mission. Within each distinct spectral band, the emulator represents radiance output at irregular wavelengths as a linear combination of basis functions and random coefficients. These random coefficients are then modeled with nearest-neighbor Gaussian processes with built-in input dimension reduction via active subspace. The proposed emulator reduces dimensionality in both input space and output space, so that fast computation is achieved within a fully Bayesian inference framework. Validation experiments demonstrate that this emulator outperforms other competing statistical methods and a reduced order model that approximates the full-physics forward model.
DOI: 10.6084/m9.figshare.14248538.v1
title: An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes

description: Robustness analysis is an emerging field in the uncertainty quantification domain. It involves analyzing the response of a computer model—which has inputs whose exact values are unknown—to the perturbation of one or several of its input distributions. Practical robustness analysis methods therefore require a coherent methodology for perturbing distributions; we present here one such rigorous method, based on the Fisher distance on manifolds of probability distributions. Further, we provide a numerical method to calculate perturbed densities in practice which comes from Lagrangian mechanics and involves solving a system of ordinary differential equations. The method introduced for perturbations is then used to compute quantile-related robustness indices. We illustrate these “perturbed-law based” indices on several numerical models. We also apply our methods to an industrial setting: the simulation of a loss of coolant accident in a nuclear reactor, where several dozen of the model’s physical parameters are not known exactly, and where limited knowledge on their distributions is available.
DOI: 10.6084/m9.figshare.14248539.v1
title: Accounting for Location Measurement Error in Imaging Data with Application to Atomic Resolution Images of Crystalline Materials

description: Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead, we create a spatial Bayesian hierarchical model that treats the locations as parameters, using the image itself to incorporate positional uncertainty. We lower the computational burden by approximating the likelihood using a non-contiguous block design around the object locations. We use this model to quantify the relationship between the intensity and displacement of hundreds of atom columns in crystal structures directly imaged via scanning transmission electron microscopy (STEM). Atomic displacements are related to important phenomena such as piezoelectricity, a property useful for engineering applications like ultrasound. Quantifying the sign and magnitude of this relationship will help materials scientists more precisely design materials with improved piezoelectricity. A simulation study confirms our method corrects bias in the estimate of the parameter of interest and drastically improves coverage in high noise scenarios compared to non-measurement error models. Supplementary materials are available online.
DOI: 10.17028/rd.lboro.19096844
title: Supplementary Information files for: The association of basic numerical abilities and math achievement: The mediating role of visuospatial and arithmetical abilities

description: Supplementary Information files for: The association of basic numerical abilities and math achievement: The mediating role of visuospatial and arithmetical abilities<br>Basic numerical abilities such as number line estimation have been observed repeatedly to be associated with mathematical achievement. Recently, it was argued that the association between basic numerical abilities and mathematical achievement is fully mediated by visuospatial abilities. However, arithmetical abilities have not yet been considered as influencing this association, even though solution strategies in number line estimation as well as mathematical achievement often involve arithmetical procedures. Therefore, we investigated the mediating role of arithmetical and visuospatial abilities on the association between number line estimation and mathematical achievement in a sample of n = 599 German elementary school students. Results indicated that arithmetical abilities as well as visuospatial abilities mediated the association between number line estimation and mathematical achievement. However, neither visuospatial nor arithmetical abilities fully mediated the association between number line estimation and mathematical achievement when considered in isolation. This substantiates the relevance of the intertwined development of visuospatial and arithemtical abilities as well as basic numerical abilities such as number line estimation (i.e. the combination of domain-specific numerical and domain-general abilities) driving mathematical achievement<br>
DOI: 10.17028/rd.lboro.19096844.v1
title: Supplementary Information files for: The association of basic numerical abilities and math achievement: The mediating role of visuospatial and arithmetical abilities

description: Supplementary Information files for: The association of basic numerical abilities and math achievement: The mediating role of visuospatial and arithmetical abilities<br>Basic numerical abilities such as number line estimation have been observed repeatedly to be associated with mathematical achievement. Recently, it was argued that the association between basic numerical abilities and mathematical achievement is fully mediated by visuospatial abilities. However, arithmetical abilities have not yet been considered as influencing this association, even though solution strategies in number line estimation as well as mathematical achievement often involve arithmetical procedures. Therefore, we investigated the mediating role of arithmetical and visuospatial abilities on the association between number line estimation and mathematical achievement in a sample of n = 599 German elementary school students. Results indicated that arithmetical abilities as well as visuospatial abilities mediated the association between number line estimation and mathematical achievement. However, neither visuospatial nor arithmetical abilities fully mediated the association between number line estimation and mathematical achievement when considered in isolation. This substantiates the relevance of the intertwined development of visuospatial and arithemtical abilities as well as basic numerical abilities such as number line estimation (i.e. the combination of domain-specific numerical and domain-general abilities) driving mathematical achievement<br>
DOI: 10.5281/zenodo.5930925
title: Collection of primary literature pdfs from the Goldberg reviews

description: These are the archive copies of all currently crawled pdf files from the Goldberg reviews.
DOI: 10.6084/m9.figshare.19096850
title: Data for repository.xlsx

description: Corporate governance and Sustainability<br>
DOI: 10.6084/m9.figshare.19096850.v1
title: Data for repository.xlsx

description: Corporate governance and Sustainability<br>
DOI: 10.5281/zenodo.5930116
title: Dataset related to article "Dose-dense ABVD as first-line therapy in early-stage unfavorable Hodgkin lymphoma: results of a prospective, multicenter double-step phase II study by Fondazione Italiana Linfomi"

description: <br> This record contains raw data related to article “Dose-dense ABVD as first-line therapy in early-stage unfavorable Hodgkin lymphoma: results of a prospective, multicenter double-step phase II study by Fondazione Italiana Linfomi" We investigated the feasibility and activity of an intensifed dose-dense ABVD (dd-ABVD) regimen in patients with earlystage unfavorable Hodgkin lymphoma (HL). This prospective, multicenter, phase II study enrolled 96 patients with newly diagnosed, unfavorable stage I or II classical HL. The patients received four cycles of dd-ABVD followed by radiotherapy. Interim PET (PET-2) was mandatory after two courses. Primary endpoints were the evaluation of dd-ABVD feasibility and activity (incidence of PET-2 negativity). The feasibility endpoint was achieved with 48/52 (92.3%) patients receiving&gt;85% of the programmed dose. The mean dose intensity in the overall patient population (n=96) was 93.7%, and the median duration of dd-ABVD was 85 days (range, 14–115) versus an expected duration of 84 days. PET-2 was available for 92/96 (95.8%) patients, of whom 79 were PET-2 negative (85.9%). In total, 90 (93.8%) patients showed complete response at the end of treatment. With a follow-up of 80.9 months (3.3–103.2), the median progression-free survival (PFS) and overall survival (OS) were not reached. At 84 months, PFS and OS rates were 88.4% and 95.7%, respectively. No evidence for a diference in PFS or OS was observed for PET-2-negative and PET-2-positive patients. Infections were documented in 8.3% and febrile neutropenia in 6.2% of cases. Four patients died: one had alveolitis at cycle 3, one death was unrelated to treatment, and two died from a secondary cancer. dd-ABVD is feasible and demonstrates activity in early-stage unfavorable HL. The predictive role of PET-2 positivity in early-stage unfavorable HL remains controversial. The study was registered in the EudraCT (reference number, 2011–003,191-36) and the ClinicalTrials.gov (reference number, NCT02247869) databases.