cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21084724.v1
title: Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

description: The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on group memberships. To test these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict dynamic node memberships in latent groups as well as the direct formation of edges between dyads. While prior substantive research often assumes the decision to engage in international militarized conflict is independent across states and static over time, we demonstrate that conflict is driven by states’ evolving membership in geopolitical blocs. Our analysis of militarized disputes from 1816 to 2010 identifies two distinct blocs of democratic states, only one of which exhibits unusually low rates of conflict. Changes in monadic covariates like democracy shift states between coalitions, making some states more pacific but others more belligerent. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13342180.v2
title: LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing

description: Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of two-dimensional and three-dimensional images. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13157836.v1
title: Markov Neighborhood Regression for High-Dimensional Inference

description: This paper proposes an innovative method for constructing confidence intervals and assessing <i>p</i>-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient <i>β<sub>i</sub> </i>, the confidence interval and <i>p</i>-value are computed by regressing on a subset of variables selected according to the conditional independence relations between the corresponding variable <i>X<sub>i</sub> </i> and other variables. Since the subset of variables forms a Markov neighborhood of <i>X<sub>i</sub> </i> in the Markov network formed by all the variables X1,X2,…,Xp, the proposed method is coined as Markov neighborhood regression. The proposed method is tested on high-dimensional linear, logistic and Cox regression. The numerical results indicate that the proposed method significantly outperforms the existing ones. Based on the Markov neighborhood regression, a method of learning causal structures for high-dimensional linear models is proposed and applied to identification of drug sensitive genes and cancer driver genes. The idea of using conditional independence relations for dimension reduction is general and potentially can be extended to other high-dimensional or big data problems as well.
DOI: 10.6084/m9.figshare.13342180
title: LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing

description: Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of two-dimensional and three-dimensional images. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13342188
title: Multiscale Quantile Segmentation

description: We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments <i>S</i> at a given error level, which serves as a tuning parameter. For a proper choice of this parameter, this probability tends exponentially fast to one, as sample size increases. We further show that the location and size of segments are estimated at minimax optimal rate (compared to a Gaussian setting) up to a log-factor. Thereby, our approach leads to (asymptotically) uniform confidence bands for the entire quantile regression function in a fully nonparametric setup. The procedure is efficiently implemented using dynamic programming techniques with double heap structures, and software is provided. Simulations and data examples from genetic sequencing and ion channel recordings confirm the robustness of the proposed procedure, which at the same time reliably detects changes in quantiles from arbitrary distributions with precise statistical guarantees. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13342188.v2
title: Multiscale Quantile Segmentation

description: We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments <i>S</i> at a given error level, which serves as a tuning parameter. For a proper choice of this parameter, this probability tends exponentially fast to one, as sample size increases. We further show that the location and size of segments are estimated at minimax optimal rate (compared to a Gaussian setting) up to a log-factor. Thereby, our approach leads to (asymptotically) uniform confidence bands for the entire quantile regression function in a fully nonparametric setup. The procedure is efficiently implemented using dynamic programming techniques with double heap structures, and software is provided. Simulations and data examples from genetic sequencing and ion channel recordings confirm the robustness of the proposed procedure, which at the same time reliably detects changes in quantiles from arbitrary distributions with precise statistical guarantees. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13179905.v1
title: Coupled generation*

description: Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest.
DOI: 10.6084/m9.figshare.13342180.v1
title: LAWS: A Locally Adaptive Weighting and Screening Approach To Spatial Multiple Testing

description: Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally–adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of 2D and 3D images.
DOI: 10.6084/m9.figshare.13342188.v1
title: Multiscale quantile segmentation

description: We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments <i>S</i> at a given error level, which serves as a tuning parameter. For a proper choice of this parameter, this probability tends exponentially fast to one, as sample size increases. We further show that the location and size of segments are estimated at minimax optimal rate (compared to a Gaussian setting) up to a log-factor. Thereby, our approach leads to (asymptotically) uniform confidence bands for the entire quantile regression function in a fully nonparametric setup. The procedure is efficiently implemented using dynamic programming techniques with double heap structures, and software is provided. Simulations and data examples from genetic sequencing and ion channel recordings confirm the robustness of the proposed procedure, which at the same time reliably detects changes in quantiles from arbitrary distributions with precise statistical guarantees.
DOI: 10.6084/m9.figshare.13342224
title: Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets With Many Locations

description: <i>Abstract–</i>Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by modifying an established class of models based on scale mixtures Gaussian processes. We show that the desired extremal dependence properties from the original models are preserved under the modification, and demonstrate that the corresponding Bayesian hierarchical model does not involve the expensive computation of the multivariate Gaussian distribution function. We fit our model to exceedances of a high threshold, and perform coverage analyses and cross-model checks to validate its ability to capture different types of tail characteristics. We use a standard adaptive Metropolis algorithm for model fitting, and further accelerate the computation via parallelization and Rcpp. Lastly, we apply the model to a dataset of a fire threat index on the Great Plains region of the United States, which is vulnerable to massively destructive wildfires. We find that the joint tail of the fire threat index exhibits a decaying dependence structure that cannot be captured by limiting extreme value models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13342224.v2
title: Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets With Many Locations

description: <i>Abstract–</i>Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by modifying an established class of models based on scale mixtures Gaussian processes. We show that the desired extremal dependence properties from the original models are preserved under the modification, and demonstrate that the corresponding Bayesian hierarchical model does not involve the expensive computation of the multivariate Gaussian distribution function. We fit our model to exceedances of a high threshold, and perform coverage analyses and cross-model checks to validate its ability to capture different types of tail characteristics. We use a standard adaptive Metropolis algorithm for model fitting, and further accelerate the computation via parallelization and Rcpp. Lastly, we apply the model to a dataset of a fire threat index on the Great Plains region of the United States, which is vulnerable to massively destructive wildfires. We find that the joint tail of the fire threat index exhibits a decaying dependence structure that cannot be captured by limiting extreme value models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13342224.v1
title: Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets with Many Locations

description: Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by modifying an established class of models based on scale mixtures Gaussian processes. We show that the desired extremal dependence properties from the original models are preserved under the modification, and demonstrate that the corresponding Bayesian hierarchical model does not involve the expensive computation of the multivariate Gaussian distribution function. We fit our model to exceedances of a high threshold, and perform coverage analyses and cross-model checks to validate its ability to capture different types of tail characteristics. We use a standard adaptive Metropolis algorithm for model fitting, and further accelerate the computation via parallelization and Rcpp. Lastly, we apply the model to a dataset of a fire threat index on the Great Plains region of the US, which is vulnerable to massively destructive wildfires. We find that the joint tail of the fire threat index exhibits a decaying dependence structure that cannot be captured by limiting extreme value models.
DOI: 10.6084/m9.figshare.23257871
title: Source code and data produced for the StORF-Reporter genome annotator.

description: The three files uploaded are:<br> StORF-Reporter.zip - The Github repository for v1.0.0 of StORF-Reporter which includes all source code, test files and genomic data for the 6 model organisms listed in the study.<br> All_URs.fasta.zip - Extracted unannotated regions (URs) for the 5,109 genome used in this study. <br> All_Ensembl_UR_StORFs.fasta.gz - Extracted StORFs for the 5,109 genome used in this study.
DOI: 10.6084/m9.figshare.23257871.v1
title: Source code and data produced for the StORF-Reporter genome annotator.

description: The three files uploaded are:<br> StORF-Reporter.zip - The Github repository for v1.0.0 of StORF-Reporter which includes all source code, test files and genomic data for the 6 model organisms listed in the study.<br> All_URs.fasta.zip - Extracted unannotated regions (URs) for the 5,109 genome used in this study. <br> All_Ensembl_UR_StORFs.fasta.gz - Extracted StORFs for the 5,109 genome used in this study.
DOI: 10.6082/uchicago.6217
title: Data of "Dynamin Related Protein 1 (Drp1) is a critical regulator of mitochondrial calcium homeostasis during myocardial ischemia/reperfusion injury"

description: dataset
DOI: 10.5281/zenodo.7988013
title: Tapping to drumbeats in an online experiment changes our perception of time and expressiveness - Dataset

description: The dataset is a supplement to the study <em>Tapping to drumbeats in an online experiment changes our perception of time and expressiveness</em> on Psychological Research.
DOI: 10.5281/zenodo.7988012
title: Tapping to drumbeats in an online experiment changes our perception of time and expressiveness - Dataset

description: The dataset is a supplement to the study <em>Tapping to drumbeats in an online experiment changes our perception of time and expressiveness</em> on Psychological Research.
DOI: 10.5281/zenodo.7988015
title: Accident rate per year in plumbing activity in construction sector in two companies in Colombia

description: Dataset of 100 occupational accidents applied to the plumbing activity in two companies with 300 workers of the construction sector, were analyzed. The data were obtained of 2005 to 2022 year. The dataset is a accident database labor administered by Occupational Health and Safety coordinators and it use was approved by the two companies. The kind of occupational accident can be seen in Sheet2(2).
DOI: 10.5281/zenodo.7988014
title: Accident rate per year in plumbing activity in construction sector in two companies in Colombia

description: Dataset of 100 occupational accidents applied to the plumbing activity in two companies with 300 workers of the construction sector, were analyzed. The data were obtained of 2005 to 2022 year. The dataset is a accident database labor administered by Occupational Health and Safety coordinators and it use was approved by the two companies. The kind of occupational accident can be seen in Sheet2(2).
DOI: 10.18434/mds2-2995
title: Magnetic resonance imaging data and analysis software for "Towards a Barrier-Free Anthropomorphic Brain Phantom for Quantitative Magnetic Resonance Imaging: Design, First Construction Attempt, and Challenges"
DOI: 10.5517/ccdc.csd.cc2dk3vy
title: CCDC 2220990: Experimental Crystal Structure Determination

description: Related Article: Zhi-Fei Hu, Zhi-Lei Chai, Qian Zhou, Le-Chuan Feng, Wen-Kui Dong, Yu-Jie Ding|2023|J.Mol.Struct.|1287|135709|doi:10.1016/j.molstruc.2023.135709
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6082/uchicago.6218
title: Dynamin Related Protein 1 (Drp1) is a critical regulator of mitochondrial calcium homeostasis during myocardial ischemia/reperfusion injury

description: Dynamin related protein 1 (Drp1) is a cytosolic GTPase protein that when activated translocates to the mitochondria, meditating mitochondrial fission and increasing reactive oxygen species (ROS) in cardiomyocytes. Drp1 is thus a promising therapeutic target for reducing cardiac ischemia/reperfusion (IR) injury. Lack of specificity of Drp1 small molecule inhibitors and the reliance on the use of Drp1 haploinsufficient hearts in genetic approaches have left the role of Drp1 in IR in question. We address these concerns using two approaches: a) Mice with short-term (3-week) conditional, cardiomyocyte specific Drp1 knock out (KO) and b) a novel, highy specific Drp1 GTPase inhibitor, Drpitor1a. Short-term Drp1 KO mice did not exhibit decreased exercise capacity or heart contractility, but isolated cardiac mitochondria demonstrated increased mitochondrial complex 1 activity, respiratory coupling, calcium retention capacity compared to controls. When exposed to IR injury on a Langendorff persusion system, Drp1 KO hearts had preserved contractility, decreased ROS, enchanced mitochondrial calcium capacity, increased resistance to mitochondrial permeability transition pore (MPTP) opening, and decreased cardiac ryanodine receptor (RYR2) gene expression. Phamacological inhibition of Drp1 with Drpitor1a following ischemia but prior to reperfusion was similarly protective of cardiac function and mitochondrial calcium homeostasis. In contrast to the benefits of short-term Drp1 inhibition, prolonged Drp1 ablation (6weeks) resulted in cardiomyopathy. We conclude that Drp1 is a key regulator of myocardial mitochondrial calcium handling and ROS generation following IR injury. Short-term inhibiton of Drp1 is a promising strategy for limiting early myocardial IR injury which is relevant for acute myocardial infarction, cardiac arrest, and heart transplantation.
DOI: 10.5281/zenodo.7987921
title: Out-of-distribution detection algorithms for robust insect classification dataset and models

description: This folder contains trained models and datasets for reproducing the results in the paper on out-of-distribution detection algorithms for robust insect classification. Specifically, it contains the following folders: OODInsect (out-of-distribution data) MSP, MAH, and EBM trained models, each wrapped around the three classifiers of ResNet50, RegNet32, and VGG11, and different combinations of ID and OOD test data for reproducing RQ1, RQ2, and RQ3. ID3 (in-distribution test data)