cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/fq8a7qkl
title: AVA: A large-scale database for aesthetic visual analysis

description: AVA: A large-scale database for aesthetic visual analysis. AVA contains more than 250,000 images along with preference score distributions and textual comments given to images by photography enthusiasts.
DOI: 10.57702/4s3ecd7l
title: EPIC: Leveraging Per Image-Token Consistency for Vision-Language Pre-training

description: The proposed EPIC method is a pre-training approach that leverages more text tokens for learning vision-language associations.
DOI: 10.57702/hg1ljlqp
title: Yelp2018, Amazon-book, and Alibaba-iFashion

description: The dataset used in the paper is Yelp2018, Amazon-book, and Alibaba-iFashion. These datasets are used for top-k recommendation tasks.
DOI: 10.57702/57j7fs73
title: ICDAR dataset

description: The ICDAR dataset is a dataset of handwritten digits.
DOI: 10.57702/1kpccduk
title: TiCC dataset

description: The TiCC dataset is a dataset of handwritten digits.
DOI: 10.57702/k6rwvld8
title: Flooding-based DoS Attack Detection Dataset

description: The dataset used in this research paper is a collection of flooding-based DoS attacks and their corresponding labels.
DOI: 10.57702/i768n7kt
title: SQL Injection Attack Detection Dataset

description: The dataset used in this research paper is a collection of SQL injection attacks and their corresponding labels.
DOI: 10.57702/esmyu5md
title: Structured Query Language Injection Attack Detection Dataset

description: The dataset used in this research paper is a collection of structured query language injection attacks and their corresponding labels.
DOI: 10.57702/1ydhdomp
title: Video Deblurring and Denoising

description: The dataset used for video deblurring and video denoising.
DOI: 10.57702/07sdf7l4
title: Berkeley-Adobe Perceptual Patch Similarity (BAPPS) dataset

description: A large-scale, highly varied, perceptual similarity dataset containing 484k human judgments.
DOI: 10.57702/wym3x6vx
title: RAISE: A Raw Images Dataset for Digital Image Forensics

description: A dataset for digital image forensics, containing 224 images.
DOI: 10.57702/118t58ov
title: TaiChiHD

description: Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education.
DOI: 10.57702/axb3hluz
title: MGif

description: Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education.
DOI: 10.57702/mobo0h92
title: Multi-dimensional Human Preference (MHP) dataset

description: The Multi-dimensional Human Preference (MHP) dataset is a large-scale dataset for evaluating text-to-image models from multi-dimensional human preferences.
DOI: 10.57702/s1ct5tyi
title: Human Preference Score (HPS) v2

description: The Human Preference Score (HPS) v2 dataset is a large-scale dataset for evaluating human preferences in text-to-image generation.
DOI: 10.57702/sbydgh3h
title: LISA: Localized Image Stylization with Audio

description: A novel framework for audio-guided local image stylization, named LISA. Audio-visual sound source localizer provides a delicate localization map by leveraging the CLIP embedding space in a weakly supervised manner.
DOI: 10.57702/cudxhw85
title: Vggsound: A large-scale audio-visual dataset

description: A large-scale audio-visual dataset containing audio-visual pairs.
DOI: 10.57702/inubzdxp
title: Celebrities in Frontal-Proﬁle (CFP) dataset

description: The Celebrities in Frontal-Proﬁle (CFP) dataset contains 500 celebrities, each of which has ten frontal and four proﬁle face images.
DOI: 10.57702/zba82tlz
title: IARPA Janus Benchmark A (IJB-A) dataset

description: The IARPA Janus Benchmark A (IJB-A) dataset contains 500 subjects with 5,712 images and 20,414 frames extracted from videos.
DOI: 10.57702/qtfly1rg
title: BABEL-60

description: The dataset used for action recognition and retrieval of walking sequences based on appearance attributes.
DOI: 10.57702/xzbpv1yb
title: D-Flow: Differentiating through Flows for Controlled Generation

description: The dataset used in the paper D-Flow: Differentiating through Flows for Controlled Generation
DOI: 10.57702/jlpfsu64
title: NLVR2

description: The dataset used in the paper is a set of sequential vision-and-language tasks, where each task consists of an image and a text input.
DOI: 10.57702/iy4pt64t
title: COVIDx dataset

description: The COVIDx dataset is a combination of many publicly available datasets for COVID-19 image classification.
DOI: 10.57702/ifth9ypu
title: COVID-Net dataset

description: The COVID-Net dataset is a dataset of COVID-19 images for image classification.
DOI: 10.57702/so8nko7p
title: CNN-based transfer learning–Bilstm network dataset

description: The CNN-based transfer learning–Bilstm network dataset is a dataset of COVID-19 images for image classification.
DOI: 10.57702/spph2gcr
title: Infinite Class Mixup

description: Mixup is a widely adopted strategy for training deep networks, where additional samples are augmented by interpolating inputs and labels of training pairs. Mixup has shown to improve classification performance, network calibration, and out-of-distribution generalisation.
DOI: 10.57702/4kw8n2jo
title: i-mix: A domain-agnostic strategy for contrastive representation learning

description: A simple framework for contrastive learning of visual representations.
DOI: 10.57702/alu9frqk
title: Tune it or don’t use it: Benchmarking data-efficient image classification

description: Tune it or don’t use it: Benchmarking data-efficient image classification.
DOI: 10.57702/nw20xe5b
title: Reg-mixup: Mixup as a regularizer can surprisingly improve accuracy and out distribution robustness

description: Mixup as a regularizer can surprisingly improve accuracy and out distribution robustness.
DOI: 10.57702/zue83m7v
title: Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation

description: Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise the generation of 3D models, e.g., NeRF. However, score distillation is prone to suffer the view inconsistency problem, and implicit NeRF modeling can also lead to an arbitrary shape, thus leading to less realistic and uncontrollable 3D generation. In this work, we propose a flexible framework of Points-to-3D to bridge the gap between sparse yet freely available 3D points and realistic shape-controllable 3D generation by distilling the knowledge from both 2D and 3D diffusion models.
DOI: 10.57702/8krequzi
title: UCF QNRF

description: The UCF QNRF dataset is a crowd counting dataset containing 334 images.
DOI: 10.57702/gnm0np91
title: FIT: Far-reaching Interleaved Transformers

description: We present FIT: a transformer-based architecture with efficient self-attention and adaptive computation.
DOI: 10.57702/fszchq3g
title: FIT-AR: Far-reaching Interleaved Transformers for Autoregressive Modeling

description: We introduce FIT-AR, a variant of FIT that incorporates causal masks and shifting in cross-attention.
DOI: 10.57702/k0dhymdi
title: Motion Correction in Optical Coherence Tomography Volumes

description: The dataset used in this paper is a collection of 102 scan pairs containing both healthy and a wide range of pathologic cases. The dataset is used to evaluate the proposed method for motion correction in optical coherence tomography (OCT) volumes.
DOI: 10.57702/l5k4xu8i
title: CoraFull

description: CoraFull is an extension of the prevalent dataset Cora from the entire citation network.
DOI: 10.6084/m9.figshare.13252283
title: On Constraining Projections of Future Climate Using Observations and Simulations From Multiple Climate Models

description: <b>Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships on projections of future warming in the Arctic at the end of the 21st century. We propose a hierarchical Bayesian framework based on a coexchangeable representation of the relationship between climate models and the Earth system. We show how emergent constraints fit into the coexchangeable representation, and extend it to account for internal variability simulated by the models and natural variability in the Earth system. Our analysis shows that projected warming in some regions of the Arctic may be more than 2</b>°<b>C lower and our uncertainty reduced by up to 30% when constrained by historical observations. A detailed theoretical comparison with existing multi-model projection frameworks is also provided. In particular, we show that projections may be biased if we do not account for internal variability in climate model predictions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</b>
DOI: 10.6084/m9.figshare.13252283.v2
title: On Constraining Projections of Future Climate Using Observations and Simulations From Multiple Climate Models

description: <b>Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships on projections of future warming in the Arctic at the end of the 21st century. We propose a hierarchical Bayesian framework based on a coexchangeable representation of the relationship between climate models and the Earth system. We show how emergent constraints fit into the coexchangeable representation, and extend it to account for internal variability simulated by the models and natural variability in the Earth system. Our analysis shows that projected warming in some regions of the Arctic may be more than 2</b>°<b>C lower and our uncertainty reduced by up to 30% when constrained by historical observations. A detailed theoretical comparison with existing multi-model projection frameworks is also provided. In particular, we show that projections may be biased if we do not account for internal variability in climate model predictions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</b>
DOI: 10.6084/m9.figshare.11873844
title: Inference on a New Class of Sample Average Treatment Effects

description: We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher’s objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both accuracy and coverage can occur by the change of estimand, even asymptotically. Inference on SATE, even using a conservative confidence interval, provides incorrect coverage of SATT. We demonstrate the applicability of the new theoretical results using an empirical application with hundreds of online experiments with an average sample size of approximately 100 million observations per experiment. An R package, estCI, that implements all the proposed estimation procedures is available. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11873844.v3
title: Inference on a New Class of Sample Average Treatment Effects

description: We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher’s objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both accuracy and coverage can occur by the change of estimand, even asymptotically. Inference on SATE, even using a conservative confidence interval, provides incorrect coverage of SATT. We demonstrate the applicability of the new theoretical results using an empirical application with hundreds of online experiments with an average sample size of approximately 100 million observations per experiment. An R package, estCI, that implements all the proposed estimation procedures is available. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13252283.v1
title: On constraining projections of future climate using observations and simulations from multiple climate models

description: Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships on projections of future warming in the Arctic at the end of the 21st century. We propose a hierarchical Bayesian framework based on a coexchangeable representation of the relationship between climate models and the Earth system. We show how emergent constraints fit into the coexchangeable representation, and extend it to account for internal variability simulated by the models and natural variability in the Earth system. Our analysis shows that projected warming in some regions of the Arctic may be more than 2 °C lower and our uncertainty reduced by up to 30 % when constrained by historical observations. A detailed theoretical comparison with existing multi-model projection frameworks is also provided. In particular, we show that projections may be biased if we do not account for internal variability in climate model predictions.
DOI: 10.6084/m9.figshare.11508309
title: Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models

description: High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this article, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings. A test statistic for testing the global null hypothesis is constructed using a generalized low-dimensional projection for bias correction and its asymptotic null distribution is derived. A lower bound for the global testing is established, which shows that the proposed test is asymptotically minimax optimal over some sparsity range. For testing the individual coefficients simultaneously, multiple testing procedures are proposed and shown to control the false discovery rate and falsely discovered variables asymptotically. Simulation studies are carried out to examine the numerical performance of the proposed tests and their superiority over existing methods. The testing procedures are also illustrated by analyzing a dataset of a metabolomics study that investigates the association between fecal metabolites and pediatric Crohn’s disease and the effects of treatment on such associations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11873844.v2
title: Inference on a New Class of Sample Average Treatment Effects

description: We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher’s objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both accuracy and coverage can occur by the change of estimand, even asymptotically. Inference on SATE, even using a conservative confidence interval, provides incorrect coverage of SATT. We demonstrate the applicability of the new theoretical results using an empirical application with hundreds of online experiments with an average sample size of approximately 100 million observations per experiment. An R package, estCI, that implements all the proposed estimation procedures is available. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12404486
title: Bayesian Structure Learning in Multilayered Genomic Networks

description: Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multilayered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multilevel genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12404486.v2
title: Bayesian Structure Learning in Multilayered Genomic Networks

description: Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multilayered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multilevel genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.57702/8bk8c75i
title: MS-UNet: A Novel Transformer-Based U-Shaped Medical Image Segmentation Network

description: MS-UNet: A novel Transformer-based U-shaped medical image segmentation network called MS-UNet has been proposed. MS-UNet addresses the challenge of capturing semantic information between the encoder and decoder components by introducing a multi-scale nested decoder structure.
DOI: 10.6084/m9.figshare.14173410.v2
title: The Effects of Stringent and Mild Interventions for Coronavirus Pandemic

description: The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic’s spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (<i>p</i>-value: &lt;0.001) and mild interventions (<i>p</i>-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, then epidemic would be under control effectively and early. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.