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1. GhostRNN: Reducing State Redundancy in RNN with Cheap Operations

Author

Zhou, Hang, Zheng, Xiaoxu, Wang, Yunhe, Mi, Michael Bi, Xiong, Deyi, and Han, Kai

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE). Due to the limitation of power and memory in low-resource devices, efficient RNN models are urgently required for real-world applications. In this paper, we propose an efficient RNN architecture, GhostRNN, which reduces hidden state redundancy with cheap operations. In particular, we observe that partial dimensions of hidden states are similar to the others in trained RNN models, suggesting that redundancy exists in specific RNNs. To reduce the redundancy and hence computational cost, we propose to first generate a few intrinsic states, and then apply cheap operations to produce ghost states based on the intrinsic states. Experiments on KWS and SE tasks demonstrate that the proposed GhostRNN significantly reduces the memory usage (~40%) and computation cost while keeping performance similar.

Published
2024
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2. Vista3D: Unravel the 3D Darkside of a Single Image

Author

Shen, Qiuhong, Yang, Xingyi, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory, Computer Science - Multimedia
Abstract

We embark on the age-old quest: unveiling the hidden dimensions of objects from mere glimpses of their visible parts. To address this, we present Vista3D, a framework that realizes swift and consistent 3D generation within a mere 5 minutes. At the heart of Vista3D lies a two-phase approach: the coarse phase and the fine phase. In the coarse phase, we rapidly generate initial geometry with Gaussian Splatting from a single image. In the fine phase, we extract a Signed Distance Function (SDF) directly from learned Gaussian Splatting, optimizing it with a differentiable isosurface representation. Furthermore, it elevates the quality of generation by using a disentangled representation with two independent implicit functions to capture both visible and obscured aspects of objects. Additionally, it harmonizes gradients from 2D diffusion prior with 3D-aware diffusion priors by angular diffusion prior composition. Through extensive evaluation, we demonstrate that Vista3D effectively sustains a balance between the consistency and diversity of the generated 3D objects. Demos and code will be available at https://github.com/florinshen/Vista3D., Comment: ECCV'2024

Published
2024

3. Isomorphic Pruning for Vision Models

Author

Fang, Gongfan, Ma, Xinyin, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Structured pruning reduces the computational overhead of deep neural networks by removing redundant sub-structures. However, assessing the relative importance of different sub-structures remains a significant challenge, particularly in advanced vision models featuring novel mechanisms and architectures like self-attention, depth-wise convolutions, or residual connections. These heterogeneous substructures usually exhibit diverged parameter scales, weight distributions, and computational topology, introducing considerable difficulty to importance comparison. To overcome this, we present Isomorphic Pruning, a simple approach that demonstrates effectiveness across a range of network architectures such as Vision Transformers and CNNs, and delivers competitive performance across different model sizes. Isomorphic Pruning originates from an observation that, when evaluated under a pre-defined importance criterion, heterogeneous sub-structures demonstrate significant divergence in their importance distribution, as opposed to isomorphic structures that present similar importance patterns. This inspires us to perform isolated ranking and comparison on different types of sub-structures for more reliable pruning. Our empirical results on ImageNet-1K demonstrate that Isomorphic Pruning surpasses several pruning baselines dedicatedly designed for Transformers or CNNs. For instance, we improve the accuracy of DeiT-Tiny from 74.52% to 77.50% by pruning an off-the-shelf DeiT-Base model. And for ConvNext-Tiny, we enhanced performance from 82.06% to 82.18%, while reducing the number of parameters and memory usage. Code is available at \url{https://github.com/VainF/Isomorphic-Pruning}.

Published
2024

4. Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching

Author

Ma, Xinyin, Fang, Gongfan, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion Transformers have recently demonstrated unprecedented generative capabilities for various tasks. The encouraging results, however, come with the cost of slow inference, since each denoising step requires inference on a transformer model with a large scale of parameters. In this study, we make an interesting and somehow surprising observation: the computation of a large proportion of layers in the diffusion transformer, through introducing a caching mechanism, can be readily removed even without updating the model parameters. In the case of U-ViT-H/2, for example, we may remove up to 93.68% of the computation in the cache steps (46.84% for all steps), with less than 0.01 drop in FID. To achieve this, we introduce a novel scheme, named Learning-to-Cache (L2C), that learns to conduct caching in a dynamic manner for diffusion transformers. Specifically, by leveraging the identical structure of layers in transformers and the sequential nature of diffusion, we explore redundant computations between timesteps by treating each layer as the fundamental unit for caching. To address the challenge of the exponential search space in deep models for identifying layers to cache and remove, we propose a novel differentiable optimization objective. An input-invariant yet timestep-variant router is then optimized, which can finally produce a static computation graph. Experimental results show that L2C largely outperforms samplers such as DDIM and DPM-Solver, alongside prior cache-based methods at the same inference speed. Code is available at https://github.com/horseee/learning-to-cache, Comment: Accepted at NeurIPS 2024

Published
2024

5. DreamDrone: Text-to-Image Diffusion Models Are Zero-Shot Perpetual View Generators

Author

Kong, Hanyang, Lian, Dongze, Mi, Michael Bi, Wang, Xinchao, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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6. Vista3D: Unravel the 3D Darkside of a Single Image

Author

Shen, Qiuhong, Yang, Xingyi, Mi, Michael Bi, Wang, Xinchao, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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7. MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation

Author

Hoang, Nhat M., Gong, Kehong, Guo, Chuan, and Mi, Michael Bi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured and annotated (e.g., text) high-quality motion corpus, a resource-intensive endeavor in the real world. This motivates our proposed MotionMix, a simple yet effective weakly-supervised diffusion model that leverages both noisy and unannotated motion sequences. Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining conditional rough motion approximations in the initial $T-T^*$ steps by learning the noisy annotated motions, followed by the unconditional refinement of these preliminary motions during the last $T^*$ steps using unannotated motions. Notably, though learning from two sources of imperfect data, our model does not compromise motion generation quality compared to fully supervised approaches that access gold data. Extensive experiments on several benchmarks demonstrate that our MotionMix, as a versatile framework, consistently achieves state-of-the-art performances on text-to-motion, action-to-motion, and music-to-dance tasks. Project page: https://nhathoang2002.github.io/MotionMix-page/, Comment: Accepted at the 38th Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, Main Conference

Published
2024

8. Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention

Author

Yang, Xin, Yan, Wending, Yuan, Yuan, Mi, Michael Bi, and Tan, Robby T.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Semantic segmentation's performance is often compromised when applied to unlabeled adverse weather conditions. Unsupervised domain adaptation is a potential approach to enhancing the model's adaptability and robustness to adverse weather. However, existing methods encounter difficulties when sequentially adapting the model to multiple unlabeled adverse weather conditions. They struggle to acquire new knowledge while also retaining previously learned knowledge.To address these problems, we propose a semantic segmentation method for multiple adverse weather conditions that incorporates adaptive knowledge acquisition, pseudolabel blending, and weather composition replay. Our adaptive knowledge acquisition enables the model to avoid learning from extreme images that could potentially cause the model to forget. In our approach of blending pseudo-labels, we not only utilize the current model but also integrate the previously learned model into the ongoing learning process. This collaboration between the current teacher and the previous model enhances the robustness of the pseudo-labels for the current target. Our weather composition replay mechanism allows the model to continuously refine its previously learned weather information while simultaneously learning from the new target domain. Our method consistently outperforms the stateof-the-art methods, and obtains the best performance with averaged mIoU (%) of 65.7 and the lowest forgetting (%) of 3.6 against 60.1 and 11.3, on the ACDC datasets for a four-target continual multi-target domain adaptation.

Published
2024
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9. HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping

Author

Zhang, Xin, Xie, Jinheng, Yuan, Yuan, Mi, Michael Bi, and Tan, Robby T.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Unsupervised object discovery and localization aims to detect or segment objects in an image without any supervision. Recent efforts have demonstrated a notable potential to identify salient foreground objects by utilizing self-supervised transformer features. However, their scopes only build upon patch-level features within an image, neglecting region/image-level and cross-image relationships at a broader scale. Moreover, these methods cannot differentiate various semantics from multiple instances. To address these problems, we introduce Hierarchical mErging framework via contrAstive grouPing (HEAP). Specifically, a novel lightweight head with cross-attention mechanism is designed to adaptively group intra-image patches into semantically coherent regions based on correlation among self-supervised features. Further, to ensure the distinguishability among various regions, we introduce a region-level contrastive clustering loss to pull closer similar regions across images. Also, an image-level contrastive loss is present to push foreground and background representations apart, with which foreground objects and background are accordingly discovered. HEAP facilitates efficient hierarchical image decomposition, which contributes to more accurate object discovery while also enabling differentiation among objects of various classes. Extensive experimental results on semantic segmentation retrieval, unsupervised object discovery, and saliency detection tasks demonstrate that HEAP achieves state-of-the-art performance., Comment: Accepted by AAAI24

Published
2023

10. DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators

Author

Kong, Hanyang, Lian, Dongze, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce DreamDrone, a novel zero-shot and training-free pipeline for generating unbounded flythrough scenes from textual prompts. Different from other methods that focus on warping images frame by frame, we advocate explicitly warping the intermediate latent code of the pre-trained text-to-image diffusion model for high-quality image generation and generalization ability. To further enhance the fidelity of the generated images, we also propose a feature-correspondence-guidance diffusion process and a high-pass filtering strategy to promote geometric consistency and high-frequency detail consistency, respectively. Extensive experiments reveal that DreamDrone significantly surpasses existing methods, delivering highly authentic scene generation with exceptional visual quality, without training or fine-tuning on datasets or reconstructing 3D point clouds in advance., Comment: 16 pages, 12 figures, project page: https://hyokong.github.io/dreamdrone-page/

Published
2023

11. Learning Unorthogonalized Matrices for Rotation Estimation

Author

Gu, Kerui, Li, Zhihao, Liu, Shiyong, Liu, Jianzhuang, Xu, Songcen, Yan, Youliang, Mi, Michael Bi, Kawaguchi, Kenji, and Yao, Angela

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Estimating 3D rotations is a common procedure for 3D computer vision. The accuracy depends heavily on the rotation representation. One form of representation -- rotation matrices -- is popular due to its continuity, especially for pose estimation tasks. The learning process usually incorporates orthogonalization to ensure orthonormal matrices. Our work reveals, through gradient analysis, that common orthogonalization procedures based on the Gram-Schmidt process and singular value decomposition will slow down training efficiency. To this end, we advocate removing orthogonalization from the learning process and learning unorthogonalized `Pseudo' Rotation Matrices (PRoM). An optimization analysis shows that PRoM converges faster and to a better solution. By replacing the orthogonalization incorporated representation with our proposed PRoM in various rotation-related tasks, we achieve state-of-the-art results on large-scale benchmarks for human pose estimation.

Published
2023

12. Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks

Author

Perry, Andrew S., Farber-Eger, Eric, Gonzales, Tomas, Tanaka, Toshiko, Robbins, Jeremy M., Murthy, Venkatesh L., Stolze, Lindsey K., Zhao, Shilin, Huang, Shi, Colangelo, Laura A., Deng, Shuliang, Hou, Lifang, Lloyd-Jones, Donald M., Walker, Keenan A., Ferrucci, Luigi, Watts, Eleanor L., Barber, Jacob L., Rao, Prashant, Mi, Michael Y., Gabriel, Kelley Pettee, Hornikel, Bjoern, Sidney, Stephen, Houstis, Nicholas, Lewis, Gregory D., Liu, Gabrielle Y., Thyagarajan, Bharat, Khan, Sadiya S., Choi, Bina, Washko, George, Kalhan, Ravi, Wareham, Nick, Bouchard, Claude, Sarzynski, Mark A., Gerszten, Robert E., Brage, Soren, Wells, Quinn S., Nayor, Matthew, and Shah, Ravi V.

Published
2024
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13. Priority-Centric Human Motion Generation in Discrete Latent Space

Author

Kong, Hanyang, Gong, Kehong, Lian, Dongze, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Text-to-motion generation is a formidable task, aiming to produce human motions that align with the input text while also adhering to human capabilities and physical laws. While there have been advancements in diffusion models, their application in discrete spaces remains underexplored. Current methods often overlook the varying significance of different motions, treating them uniformly. It is essential to recognize that not all motions hold the same relevance to a particular textual description. Some motions, being more salient and informative, should be given precedence during generation. In response, we introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM), which utilizes a Transformer-based VQ-VAE to derive a concise, discrete motion representation, incorporating a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token within the entire motion sequence. This approach retains the most salient motions during the reverse diffusion process, leading to more semantically rich and varied motions. Additionally, we formulate two strategies to gauge the importance of motion tokens, drawing from both textual and visual indicators. Comprehensive experiments on the HumanML3D and KIT-ML datasets confirm that our model surpasses existing techniques in fidelity and diversity, particularly for intricate textual descriptions., Comment: Accepted by ICCV2023

Published
2023

14. PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

Author

Nie, Ming, Xue, Yujing, Wang, Chunwei, Ye, Chaoqiang, Xu, Hang, Zhu, Xinge, Huang, Qingqiu, Mi, Michael Bi, Wang, Xinchao, and Zhang, Li

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representing point clouds as polar grids has been recognized as an alternative due to (1) its advantage in robust performance under different resolutions and (2) its superiority in streaming-based approaches. However, state-of-the-art polar-based detection methods inevitably suffer from the feature distortion problem because of the non-uniform division of polar representation, resulting in a non-negligible performance gap compared to Cartesian-based approaches. To tackle this issue, we present PARTNER, a novel 3D object detector in the polar coordinate. PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head. Extensive experiments show overwhelming advantages in streaming-based detection and different resolutions. Furthermore, our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on Waymo and ONCE validation set, thus achieving competitive results over the state-of-the-art methods., Comment: ICCV 2023

Published
2023

15. Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis

Author

Wood, Alexis C, Goodarzi, Mark O, Senn, Mackenzie K, Gadgil, Meghana D, Graca, Goncalo, Allison, Matthew A, Tzoulaki, Ioanna, Mi, Michael Y, Greenland, Philip, Ebbels, Timothy, Elliott, Paul, Tracy, Russell P, Herrington, David M, and Rotter, Jerome I

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Prevention, Obesity, Diabetes, Metabolic and endocrine, Humans, Female, Aged, Male, Diabetes Mellitus, Type 2, Risk Factors, Persea, Cross-Sectional Studies, Biomarkers, Insulin, Glucose, Atherosclerosis, biomarker, type 2 diabetes, dysglycemia, metabolomics, personalized nutrition, MUFA, avocado, Animal Production, Food Sciences, Nutrition & Dietetics, Animal production, Food sciences, Nutrition and dietetics
Abstract

BackgroundAvocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes.ObjectivesTo identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia.MethodsBaseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an ∼18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined.ResultsThree highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 ∗ 10-7) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (β = -0.07 +/- 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m2). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (β = -0.22 +/- 0.02, P < 2.0 ∗ 10-16), lower fasting insulin (β = -0.17 +/- 0.02, P < 2.0 ∗ 10-16), and a lower incidence of T2D (hazard ratio: 0.68; 0.63-074, P < 2.0 ∗ 10-16), even when adjusting for BMI.ConclusionsHighly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.

Published
2023

16. Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction

Author

Yu, Ziwei, Li, Chen, Yang, Linlin, Zheng, Xiaoxu, Mi, Michael Bi, Lee, Gim Hee, and Yao, Angela

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Direct mesh fitting for 3D hand shape reconstruction is highly accurate. However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes. Conversely, parametric models like MANO ensure plausible hand shapes but are not as accurate as the non-parametric methods. In this work, we introduce a novel weakly-supervised hand shape estimation framework that integrates non-parametric mesh fitting with MANO model in an end-to-end fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to yield well-aligned and high-quality 3D meshes, especially in challenging two-hand and hand-object interaction scenarios., Comment: CVPR 2023

Published
2023

17. Enhancing Video Super-Resolution via Implicit Resampling-based Alignment

Author

Xu, Kai, Yu, Ziwei, Wang, Xin, Mi, Michael Bi, and Yao, Angela

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In video super-resolution, it is common to use a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video, but existing works overlook a critical step -- resampling. We show through extensive experiments that for alignment to be effective, the resampling should preserve the reference frequency spectrum while minimizing spatial distortions. However, most existing works simply use a default choice of bilinear interpolation for resampling even though bilinear interpolation has a smoothing effect and hinders super-resolution. From these observations, we propose an implicit resampling-based alignment. The sampling positions are encoded by a sinusoidal positional encoding, while the value is estimated with a coordinate network and a window-based cross-attention. We show that bilinear interpolation inherently attenuates high-frequency information while an MLP-based coordinate network can approximate more frequencies. Experiments on synthetic and real-world datasets show that alignment with our proposed implicit resampling enhances the performance of state-of-the-art frameworks with minimal impact on both compute and parameters.

Published
2023

18. TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration

Author

Gong, Kehong, Lian, Dongze, Chang, Heng, Guo, Chuan, Jiang, Zihang, Zuo, Xinxin, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. However, the lack of paired motion data with both music and text modalities limits the ability to generate dance movements that integrate both. To alleviate this challenge, we propose to utilize a 3D human motion VQ-VAE to project the motions of the two datasets into a latent space consisting of quantized vectors, which effectively mix the motion tokens from the two datasets with different distributions for training. Additionally, we propose a cross-modal transformer to integrate text instructions into motion generation architecture for generating 3D dance movements without degrading the performance of music-conditioned dance generation. To better evaluate the quality of the generated motion, we introduce two novel metrics, namely Motion Prediction Distance (MPD) and Freezing Score (FS), to measure the coherence and freezing percentage of the generated motion. Extensive experiments show that our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities. Code is available at https://garfield-kh.github.io/TM2D/., Comment: Accepted by ICCV2023

Published
2023

19. DepGraph: Towards Any Structural Pruning

Author

Fang, Gongfan, Ma, Xinyin, Song, Mingli, Mi, Michael Bi, and Wang, Xinchao

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new architectures. In this work, we study a highly-challenging yet barely-explored task, any structural pruning, to tackle general structural pruning of arbitrary architecture like CNNs, RNNs, GNNs and Transformers. The most prominent obstacle towards this goal lies in the structural coupling, which not only forces different layers to be pruned simultaneously, but also expects all removed parameters to be consistently unimportant, thereby avoiding structural issues and significant performance degradation after pruning. To address this problem, we propose a general and {fully automatic} method, \emph{Dependency Graph} (DepGraph), to explicitly model the dependency between layers and comprehensively group coupled parameters for pruning. In this work, we extensively evaluate our method on several architectures and tasks, including ResNe(X)t, DenseNet, MobileNet and Vision transformer for images, GAT for graph, DGCNN for 3D point cloud, alongside LSTM for language, and demonstrate that, even with a simple norm-based criterion, the proposed method consistently yields gratifying performances.

Published
2023

20. Bias-Compensated Integral Regression for Human Pose Estimation

Author

Gu, Kerui, Yang, Linlin, Mi, Michael Bi, and Yao, Angela

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In human and hand pose estimation, heatmaps are a crucial intermediate representation for a body or hand keypoint. Two popular methods to decode the heatmap into a final joint coordinate are via an argmax, as done in heatmap detection, or via softmax and expectation, as done in integral regression. Integral regression is learnable end-to-end, but has lower accuracy than detection. This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation. This bias often forces the network to learn degenerately localized heatmaps, obscuring the keypoint's true underlying distribution and leads to lower accuracies. Training-wise, by investigating the gradients of integral regression, we show that the implicit guidance of integral regression to update the heatmap makes it slower to converge than detection. To counter the above two limitations, we propose Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias. BCIR also incorporates a Gaussian prior loss to speed up training and improve prediction accuracy. Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.

Published
2023

21. Improving Deep Regression with Ordinal Entropy

Author

Zhang, Shihao, Yang, Linlin, Mi, Michael Bi, Zheng, Xiaoxu, and Yao, Angela

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression., Comment: Accepted to ICLR 2023. Project page: https://github.com/needylove/OrdinalEntropy

Published
2023

22. Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Author

Yang, Xin, Mi, Michael Bi, Yuan, Yuan, Wang, Xin, and Tan, Robby T.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth and fog transmission map is introduced to strengthen the retention of the depth information in the aligned features. To address false object features potentially generated during the DA process, we propose an encoder-decoder framework to reconstruct the fog-free background image. This reconstruction loss also reinforces the encoder, i.e., our DA backbone, to minimize false object features.Moreover, we involve our target data in training both our DA module and our detection module in a semi-supervised manner, so that our detection module is also exposed to the unlabeled target data, the type of data used in the testing stage. Using these ideas, our method significantly outperforms the state-of-the-art method (47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains the best performance on multiple real-image public datasets. Code is available at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-Scenes, Comment: Accepted by ACCV

Published
2022

23. ONCE-3DLanes: Building Monocular 3D Lane Detection

Author

Yan, Fan, Nie, Ming, Cai, Xinyue, Han, Jianhua, Xu, Hang, Yang, Zhen, Ye, Chaoqiang, Fu, Yanwei, Mi, Michael Bi, and Zhang, Li

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe driving. However, existing 3D lane detection datasets are either unpublished or synthesized from a simulated environment, severely hampering the development of this field. In this paper, we take steps towards addressing these issues. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations in 211K road scenes. In addition, we present an extrinsic-free, anchor-free method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV). To facilitate future research on 3D lane detection, we benchmark the dataset and provide a novel evaluation metric, performing extensive experiments of both existing approaches and our proposed method. The aim of our work is to revive the interest of 3D lane detection in a real-world scenario. We believe our work can lead to the expected and unexpected innovations in both academia and industry., Comment: CVPR 2022. Project page at https://once-3dlanes.github.io

Published
2022

24. PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision

Author

Gong, Kehong, Li, Bingbing, Zhang, Jianfeng, Wang, Tao, Huang, Jing, Mi, Michael Bi, Feng, Jiashi, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses. In this paper, we propose a novel self-supervised approach that allows us to explicitly generate 2D-3D pose pairs for augmenting supervision, through a self-enhancing dual-loop learning framework. This is made possible via introducing a reinforcement-learning-based imitator, which is learned jointly with a pose estimator alongside a pose hallucinator; the three components form two loops during the training process, complementing and strengthening one another. Specifically, the pose estimator transforms an input 2D pose sequence to a low-fidelity 3D output, which is then enhanced by the imitator that enforces physical constraints. The refined 3D poses are subsequently fed to the hallucinator for producing even more diverse data, which are, in turn, strengthened by the imitator and further utilized to train the pose estimator. Such a co-evolution scheme, in practice, enables training a pose estimator on self-generated motion data without relying on any given 3D data. Extensive experiments across various benchmarks demonstrate that our approach yields encouraging results significantly outperforming the state of the art and, in some cases, even on par with results of fully-supervised methods. Notably, it achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset evaluation setup, improving upon the previous best self-supervised methods by 8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet, Comment: CVPR 2022 Oral Paper, code available: https://github.com/Garfield-kh/PoseTriplet

Published
2022

25. Point2Seq: Detecting 3D Objects as Sequences

Author

Xue, Yujing, Mao, Jiageng, Niu, Minzhe, Xu, Hang, Mi, Michael Bi, Zhang, Wei, Wang, Xiaogang, and Wang, Xinchao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. In contrast to previous methods that normally {predict attributes of 3D objects all at once}, we expressively model the interdependencies between attributes of 3D objects, which in turn enables a better detection accuracy. Specifically, we view each 3D object as a sequence of words and reformulate the 3D object detection task as decoding words from 3D scenes in an auto-regressive manner. We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words. The predicted words eventually constitute a set of sequences that completely describe the 3D objects in the scene, and all the predicted sequences are then automatically assigned to the respective ground truths through similarity-based sequence matching. Our approach is conceptually intuitive and can be readily plugged upon most existing 3D-detection backbones without adding too much computational overhead; the sequential decoding paradigm we proposed, on the other hand, can better exploit information from complex 3D scenes with the aid of preceding predicted words. Without bells and whistles, our method significantly outperforms previous anchor- and center-based 3D object detection frameworks, yielding the new state of the art on the challenging ONCE dataset as well as the Waymo Open Dataset. Code is available at \url{https://github.com/ocNflag/point2seq}., Comment: To appear in CVPR2022

Published
2022

27. Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Author

Yang, Xin, Mi, Michael Bi, Yuan, Yuan, Wang, Xin, Tan, Robby T., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Gall, Juergen, editor, Chin, Tat-Jun, editor, Sato, Imari, editor, and Chellappa, Rama, editor

Published
2023
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29. Mixed meal tolerance testing highlights in diabetes altered branched-chain ketoacid metabolism and pathways associated with all-cause mortality

Author

Mi, Michael Y., Whitlock, Mark, Shi, Xu, Farrell, Laurie A., Bhambhani, Victoria M., Quadir, Juweria, Blatnik, Matthew, Wald, Kyle P., Tierney, Brendan, Kim, Albert, Loudon, Peter, Chen, Zsu-Zsu, Correa, Adolfo, Gao, Yan, Carson, April P., Bertoni, Alain G., Roth Flach, Rachel J., and Gerszten, Robert E.

Published
2023
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35. Hepatic Steatosis and Fibrosis, Cardiorespiratory Fitness, and Metabolic Mediators in the Community.

Author

Florea, Victor V., Gajjar, Priya, Huang, Shi, Tang, Jingxian, Zhao, Shilin, Davenport, Megan, Mi, Michael Y., Haff, Madeleine, Zhang, Xiaoyu, Miller, Patricia E., Vasan, Ramachandran S., Liu, Ching‐Ti, Lewis, Gregory D., Shah, Ravi V., Long, Michelle T., and Nayor, Matthew

Subjects
HEPATIC fibrosis, AMINO acid derivatives, FATTY liver, EXERCISE tests, CARDIOPULMONARY fitness
Abstract

Background and Aims: Individuals with steatotic liver disease (SLD) are at high cardiovascular disease (CVD) risk, but approaches to characterise and mitigate this risk are limited. By investigating relations, and shared metabolic pathways, of hepatic steatosis/fibrosis and cardiorespiratory fitness (CRF), we sought to identify new avenues for CVD risk reduction in SLD. Methods: In Framingham Heart Study (FHS) participants (N = 2722, age 54 ± 9 years, 53% women), vibration‐controlled transient elastography (VCTE) was performed between 2016–2019 to assess hepatic steatosis (continuous attenuation parameter [CAP]) and fibrosis (liver fibrosis measure [LSM]). Concurrently, participants underwent maximum effort cardiopulmonary exercise testing (CPET), and metabolomic profiling (201 circulating metabolites) was performed in a subsample (N = 1268). Results: Mean BMI was 28.0 ± 5.3, 27% had hepatic steatosis, 7.6% had fibrosis, and peak oxygen uptake (VO2) was 26.2 ± 6.8 mL/kg/min in men and 20.7 ± 6.0 mL/kg/min in women (95% predicted overall). In linear models adjusted for cardiometabolic risk factors, greater CAP and LSM were associated with lower peak VO2 (p ≤ 0.002 for all), and the CAP association remained significant after BMI adjustment (p < 0.0001). We observed shared metabolic architecture of CAP, LSM, and peak VO2, with metabolites mediating up to 35% (for CAP) and 74% (for LSM) of the association with peak VO2. Metabolite mediators included amino acids and derivatives implicated in cardiometabolic risk and both protective and deleterious lipid species. Conclusions: Hepatic steatosis and fibrosis are associated with CRF impairment in the community, and these relations are partly mediated by pathways of altered lipid metabolism and general cardiometabolic risk. [ABSTRACT FROM AUTHOR]

Published
2025
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42. Abstract 13308: Complementary Investigations of Plasma Proteomics Identify Novel Protein Associations With Incident Diabetes and Glucose Metabolism

Author

Mi, Michael Y, Cronje, Helene T, Austin, Thomas R, Biggs, Mary L, Siscovick, David S, Lemaitre, Rozenn N, Psaty, Bruce M, Tracy, Russell P, Djousse, Luc, Kizer, Jorge R, IX, Joachim H, Sotoodehnia, Nona, Rao, Prashant, Robbins, Jeremy M, Barber, Jacob L, Sarzynski, Mark A, Clish, Clary, Bouchard, Claude, Mukamal, Ken J, Jensen, Majken K, and Gerszten, Robert E

Published
2022
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45. Nonstandard Errors

Author

MENKVELD, ALBERT J., primary, DREBER, ANNA, additional, HOLZMEISTER, FELIX, additional, HUBER, JUERGEN, additional, JOHANNESSON, MAGNUS, additional, KIRCHLER, MICHAEL, additional, NEUSÜß, SEBASTIAN, additional, RAZEN, MICHAEL, additional, WEITZEL, UTZ, additional, ABAD‐DÍAZ, DAVID, additional, ABUDY, MENACHEM (MENI), additional, ADRIAN, TOBIAS, additional, AIT‐SAHALIA, YACINE, additional, AKMANSOY, OLIVIER, additional, ALCOCK, JAMIE T., additional, ALEXEEV, VITALI, additional, ALOOSH, ARASH, additional, AMATO, LIVIA, additional, AMAYA, DIEGO, additional, ANGEL, JAMES J., additional, AVETIKIAN, ALEJANDRO T., additional, BACH, AMADEUS, additional, BAIDOO, EDWIN, additional, BAKALLI, GAETAN, additional, BAO, LI, additional, BARBON, ANDREA, additional, BASHCHENKO, OKSANA, additional, BINDRA, PARAMPREET C., additional, BJØNNES, GEIR H., additional, BLACK, JEFFREY R., additional, BLACK, BERNARD S., additional, BOGOEV, DIMITAR, additional, CORREA, SANTIAGO BOHORQUEZ, additional, BONDARENKO, OLEG, additional, BOS, CHARLES S., additional, BOSCH‐ROSA, CIRIL, additional, BOURI, ELIE, additional, BROWNLEES, CHRISTIAN, additional, CALAMIA, ANNA, additional, CAO, VIET NGA, additional, CAPELLE‐BLANCARD, GUNTHER, additional, ROMERO, LAURA M. CAPERA, additional, CAPORIN, MASSIMILIANO, additional, CARRION, ALLEN, additional, CASKURLU, TOLGA, additional, CHAKRABARTY, BIDISHA, additional, CHEN, JIAN, additional, CHERNOV, MIKHAIL, additional, CHEUNG, WILLIAM, additional, CHINCARINI, LUDWIG B., additional, CHORDIA, TARUN, additional, CHOW, SHEUNG‐CHI, additional, CLAPHAM, BENJAMIN, additional, COLLIARD, JEAN‐EDOUARD, additional, COMERTON‐FORDE, CAROLE, additional, CURRAN, EDWARD, additional, DAO, THONG, additional, DARE, WALE, additional, DAVIES, RYAN J., additional, BLASIS, RICCARDO DE, additional, NARD, GIANLUCA F. DE, additional, DECLERCK, FANY, additional, DEEV, OLEG, additional, DEGRYSE, HANS, additional, DEKU, SOLOMON Y., additional, DESAGRE, CHRISTOPHE, additional, DIJK, MATHIJS A. VAN, additional, DIM, CHUKWUMA, additional, DIMPFL, THOMAS, additional, DONG, YUN JIANG, additional, DRUMMOND, PHILIP A., additional, DUDDA, TOM, additional, DUEVSKI, TEODOR, additional, DUMITRESCU, ARIADNA, additional, DYAKOV, TEODOR, additional, DYHRBERG, ANNE HAUBO, additional, DZIELIŃSKI, MICHAŁ, additional, EKSI, ASLI, additional, KALAK, IZIDIN EL, additional, ELLEN, SASKIA TER, additional, EUGSTER, NICOLAS, additional, EVANS, MARTIN D. D., additional, FARRELL, MICHAEL, additional, FELEZ‐VINAS, ESTER, additional, FERRARA, GERARDO, additional, FERROUHI, EL MEHDI, additional, FLORI, ANDREA, additional, FLUHARTY‐JAIDEE, JONATHAN T., additional, FOLEY, SEAN D. V., additional, FONG, KINGSLEY Y. L., additional, FOUCAULT, THIERRY, additional, FRANUS, TATIANA, additional, FRANZONI, FRANCESCO, additional, FRIJNS, BART, additional, FRÖMMEL, MICHAEL, additional, FU, SERVANNA M., additional, FÜLLBRUNN, SASCHA C., additional, GAN, BAOQING, additional, GAO, GE, additional, GEHRIG, THOMAS P., additional, GEMAYEL, ROLAND, additional, GERRITSEN, DIRK, additional, GIL‐BAZO, JAVIER, additional, GILDER, DUDLEY, additional, GLOSTEN, LAWRENCE R., additional, GOMEZ, THOMAS, additional, GORBENKO, ARSENY, additional, GRAMMIG, JOACHIM, additional, GRÉGOIRE, VINCENT, additional, GÜÇBILMEZ, UFUK, additional, HAGSTRÖMER, BJÖRN, additional, HAMBUCKERS, JULIEN, additional, HAPNES, ERIK, additional, HARRIS, JEFFREY H., additional, HARRIS, LAWRENCE, additional, HARTMANN, SIMON, additional, HASSE, JEAN‐BAPTISTE, additional, HAUTSCH, NIKOLAUS, additional, HE, XUE‐ZHONG (TONY), additional, HEATH, DAVIDSON, additional, HEDIGER, SIMON, additional, HENDERSHOTT, TERRENCE, additional, HIBBERT, ANN MARIE, additional, HJALMARSSON, ERIK, additional, HOELSCHER, SETH A., additional, HOFFMANN, PETER, additional, HOLDEN, CRAIG W., additional, HORENSTEIN, ALEX R., additional, HUANG, WENQIAN, additional, HUANG, DA, additional, HURLIN, CHRISTOPHE, additional, ILCZUK, KONRAD, additional, IVASHCHENKO, ALEXEY, additional, IYER, SUBRAMANIAN R., additional, JAHANSHAHLOO, HOSSEIN, additional, JALKH, NAJI, additional, JONES, CHARLES M., additional, JURKATIS, SIMON, additional, JYLHÄ, PETRI, additional, KAECK, ANDREAS T., additional, KAISER, GABRIEL, additional, KARAM, ARZÉ, additional, KARMAZIENE, EGLE, additional, KASSNER, BERNHARD, additional, KAUSTIA, MARKKU, additional, KAZAK, EKATERINA, additional, KEARNEY, FEARGHAL, additional, KERVEL, VINCENT VAN, additional, KHAN, SAAD A., additional, KHOMYN, MARTA K., additional, KLEIN, TONY, additional, KLEIN, OLGA, additional, KLOS, ALEXANDER, additional, KOETTER, MICHAEL, additional, KOLOKOLOV, ALEKSEY, additional, KORAJCZYK, ROBERT A., additional, KOZHAN, ROMAN, additional, KRAHNEN, JAN P., additional, KUHLE, PAUL, additional, KWAN, AMY, additional, LAJAUNIE, QUENTIN, additional, LAM, F. Y. ERIC C., additional, LAMBERT, MARIE, additional, LANGLOIS, HUGUES, additional, LAUSEN, JENS, additional, LAUTER, TOBIAS, additional, LEIPPOLD, MARKUS, additional, LEVIN, VLADIMIR, additional, LI, YIJIE, additional, LI, HUI, additional, LIEW, CHEE YOONG, additional, LINDNER, THOMAS, additional, LINTON, OLIVER, additional, LIU, JIACHENG, additional, LIU, ANQI, additional, LLORENTE, GUILLERMO, additional, LOF, MATTHIJS, additional, LOHR, ARIEL, additional, LONGSTAFF, FRANCIS, additional, LOPEZ‐LIRA, ALEJANDRO, additional, MANKAD, SHAWN, additional, MANO, NICOLA, additional, MARCHAL, ALEXIS, additional, MARTINEAU, CHARLES, additional, MAZZOLA, FRANCESCO, additional, MELOSO, DEBRAH, additional, MI, MICHAEL G., additional, MIHET, ROXANA, additional, MOHAN, VIJAY, additional, MOINAS, SOPHIE, additional, MOORE, DAVID, additional, MU, LIANGYI, additional, MURAVYEV, DMITRIY, additional, MURPHY, DERMOT, additional, NESZVEDA, GABOR, additional, NEUMEIER, CHRISTIAN, additional, NIELSSON, ULF, additional, NIMALENDRAN, MAHENDRARAJAH, additional, NOLTE, SVEN, additional, NORDEN, LARS L., additional, O'NEILL, PETER, additional, OBAID, KHALED, additional, ØDEGAARD, BERNT A., additional, ÖSTBERG, PER, additional, PAGNOTTA, EMILIANO, additional, PAINTER, MARCUS, additional, PALAN, STEFAN, additional, PALIT, IMON J., additional, PARK, ANDREAS, additional, PASCUAL, ROBERTO, additional, PASQUARIELLO, PAOLO, additional, PASTOR, LUBOS, additional, PATEL, VINAY, additional, PATTON, ANDREW J., additional, PEARSON, NEIL D., additional, PELIZZON, LORIANA, additional, PELLI, MICHELE, additional, PELSTER, MATTHIAS, additional, PÉRIGNON, CHRISTOPHE, additional, PFIFFER, CAMERON, additional, PHILIP, RICHARD, additional, PLÍHAL, TOMÁŠ, additional, PRAKASH, PUNEET, additional, PRESS, OLIVER‐ALEXANDER, additional, PRODROMOU, TINA, additional, PROKOPCZUK, MARCEL, additional, PUTNINS, TALIS, additional, QIAN, YA, additional, RAIZADA, GAURAV, additional, RAKOWSKI, DAVID, additional, RANALDO, ANGELO, additional, REGIS, LUCA, additional, REITZ, STEFAN, additional, RENAULT, THOMAS, additional, RENJIE, REX W., additional, RENO, ROBERTO, additional, RIDDIOUGH, STEVEN J., additional, RINNE, KALLE, additional, RINTAMÄKI, PAUL, additional, RIORDAN, RYAN, additional, RITTMANNSBERGER, THOMAS, additional, LONGARELA, IÑAKI RODRÍGUEZ, additional, ROESCH, DOMINIK, additional, ROGNONE, LAVINIA, additional, ROSEMAN, BRIAN, additional, ROŞU, IOANID, additional, ROY, SAURABH, additional, RUDOLF, NICOLAS, additional, RUSH, STEPHEN R., additional, RZAYEV, KHALADDIN, additional, RZEŹNIK, ALEKSANDRA A., additional, SANFORD, ANTHONY, additional, SANKARAN, HARIKUMAR, additional, SARKAR, ASANI, additional, SARNO, LUCIO, additional, SCAILLET, OLIVIER, additional, SCHARNOWSKI, STEFAN, additional, SCHENK‐HOPPÉ, KLAUS R., additional, SCHERTLER, ANDREA, additional, SCHNEIDER, MICHAEL, additional, SCHROEDER, FLORIAN, additional, SCHÜRHOFF, NORMAN, additional, SCHUSTER, PHILIPP, additional, SCHWARZ, MARCO A., additional, SEASHOLES, MARK S., additional, SEEGER, NORMAN J., additional, SHACHAR, OR, additional, SHKILKO, ANDRIY, additional, SHUI, JESSICA, additional, SIKIC, MARIO, additional, SIMION, GIORGIA, additional, SMALES, LEE A., additional, SÖDERLIND, PAUL, additional, SOJLI, ELVIRA, additional, SOKOLOV, KONSTANTIN, additional, SÖNKSEN, JANTJE, additional, SPOKEVICIUTE, LAIMA, additional, STEFANOVA, DENITSA, additional, SUBRAHMANYAM, MARTI G., additional, SZASZI, BARNABAS, additional, TALAVERA, OLEKSANDR, additional, TANG, YUEHUA, additional, TAYLOR, NICK, additional, THAM, WING WAH, additional, THEISSEN, ERIK, additional, THIMME, JULIAN, additional, TONKS, IAN, additional, TRAN, HAI, additional, TRAPIN, LUCA, additional, TROLLE, ANDERS B., additional, VADUVA, M. ANDREEA, additional, VALENTE, GIORGIO, additional, NESS, ROBERT A. VAN, additional, VASQUEZ, AURELIO, additional, VEROUSIS, THANOS, additional, VERWIJMEREN, PATRICK, additional, VILHELMSSON, ANDERS, additional, VILKOV, GRIGORY, additional, VLADIMIROV, VLADIMIR, additional, VOGEL, SEBASTIAN, additional, VOIGT, STEFAN, additional, WAGNER, WOLF, additional, WALTHER, THOMAS, additional, WEISS, PATRICK, additional, WEL, MICHEL VAN DER, additional, WERNER, INGRID M., additional, WESTERHOLM, P. JOAKIM, additional, WESTHEIDE, CHRISTIAN, additional, WIKA, HANS C., additional, WIPPLINGER, EVERT, additional, WOLF, MICHAEL, additional, WOLFF, CHRISTIAN C. P., additional, WOLK, LEONARD, additional, WONG, WING‐KEUNG, additional, WRAMPELMEYER, JAN, additional, WU, ZHEN‐XING, additional, XIA, SHUO, additional, XIU, DACHENG, additional, XU, KE, additional, XU, CAIHONG, additional, YADAV, PRADEEP K., additional, YAGÜE, JOSÉ, additional, YAN, CHENG, additional, YANG, ANTTI, additional, YOO, WOONGSUN, additional, YU, WENJIA, additional, YU, YIHE, additional, YU, SHIHAO, additional, YUESHEN, BART Z., additional, YUFEROVA, DARYA, additional, ZAMOJSKI, MARCIN, additional, ZAREEI, ABALFAZL, additional, ZEISBERGER, STEFAN M., additional, ZHANG, LU, additional, ZHANG, S. SARAH, additional, ZHANG, XIAOYU, additional, ZHAO, LU, additional, ZHONG, ZHUO, additional, ZHOU, Z. IVY, additional, ZHOU, CHEN, additional, ZHU, XINGYU S., additional, ZOICAN, MARIUS, additional, and ZWINKELS, REMCO, additional

Published
2024
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46. Non-Standard Errors

Author

Menkveld, Albert J., Abudy, Menachem (Meni), Grammig, Joachim, Gregoire, Vincent, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, Adrian, Tobias, He, Xue-Zhong 'Tony', Heath, Davidson, Hediger, Simon, Hendershott, Terrence J., Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth, Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Ait-Sahalia, Yacine, Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylha, Petri, Akmansoy, Olivier, Kaeck, Andreas, Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, van Kervel, Vincent, Khan, Saad, Alcock, Jamie, Khomyn, Marta, Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Krahnen, Jan Pieter, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Kwan, Amy, Alexeev, Vitali, Lajaunie, Quentin, Lam, Full Yet Eric Campbell, Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, (Michael) Hui, Aloosh, Arash, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver B., Liu, Jiacheng, Liu, Anqi, Llorente-Alvarez, Jesus-Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis A., Lopez-Lira, Alejandro, Amato, Livia, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah C, Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Amaya, Diego, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Nordén, Lars L., O'Neill, Peter, Angel, James J., Obaid, Khaled, Ødegaard, Bernt Arne, Östberg, Per, Painter, Marcus, Palan, Stefan, Palit, Imon, Park, Andreas, Pascual Gascó, Roberto, Pasquariello, Paolo, Pastor, Lubos, Dreber, Anna, Bach, Amadeus, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Baidoo, Edwin, Press, Oliver-Alexander, Prodromou, Tina, Putnins, Talis J., Raizada, Gaurav, Rakowski, David A., Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Wang, Renjie, Bakalli, Gaetan, Renò, Roberto, Riddiough, Steven, Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Rösch, Dominik, Rognone, Lavinia, Roseman, Brian, Barbon, Andrea, Rosu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen, Rzayev, Khaladdin, Rzeźnik, Aleksandra, Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Bashchenko, Oksana, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus Reiner, Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Bindra, Parampreet Christopher, Seeger, Norman, Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Bjonnes, Geir Hoidal, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Neusüss, Sebastian, Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nicholas, Tham, Wing Wah, Theissen, Erik, Black, Jeffrey R., Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Black, Bernard S., Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, van der Wel, Michel, Werner, Ingrid M., Bohorquez, Santiago, Westerholm, P. Joakim, Westheide, Christian, Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Xia, Shuo, Xiu, Dacheng, Holzmeister, Felix, Bondarenko, Oleg, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Shihao, Yueshen, Bart Zhou, Bos, Charles S., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan, Zhang, Lu, Zhang, Xiaoyu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu, Bosch-Rosa, Ciril, Zoican, Marius, Zwinkels, Remco C.J., Chen, Jian, Duevski, Teodor, Gao, Ge, Gemayel, Roland, Gilder, Dudley, Kuhle, Paul, Pagnotta, Emiliano, Pelli, Michele, Bouri, Elie, Sönksen, Jantje, Ilczuk, Konrad, Bogoev, Dimitar, Qian, Ya, Wika, Hans C., Yu, Yihe, Zhao, Lu, Mi, Michael, Bao, Li, Brownlees, Christian T., Vaduva, Andreea, Prokopczuk, Marcel, Avetikian, Alejandro, Wu, Zhen-Xing, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera, Laura, Caporin, Massimiliano, Huber, Juergen, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chernov, Mikhail, Cheung, William Ming Yan, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Johanneson, Magnus, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca, Declerck, Fany, Deev, Oleg, Degryse, Hans, Kirchler, Michael, Deku, Solomon, Desagre, Christophe, Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip, Dudda, Tom, Dumitrescu, Ariadna, Dyakov, Teodor, Razen, Michael, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, ter Ellen, Saskia, Eugster, Nicolas, Evans, Martin D.D., Farrell, Michael, Félez-Viñas, Ester, Ferrara, Gerardo, Weitzel, Utz, FERROUHI, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan, Foley, Sean, Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco A., Frijns, Bart, Frömmel, Michael, Abad, David, Fu, Servanna Mianjun, Füllbrunn, Sascha, Gan, Baoqing, Gehrig, Thomas, Gerritsen, Dirk, Gil-Bazo, Javier, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Güçbilmez, Ufuk, Van Dijk, Matthijs A., Menkveld, Albert J., Abudy, Menachem (Meni), Grammig, Joachim, Gregoire, Vincent, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, Adrian, Tobias, He, Xue-Zhong 'Tony', Heath, Davidson, Hediger, Simon, Hendershott, Terrence J., Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth, Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Ait-Sahalia, Yacine, Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylha, Petri, Akmansoy, Olivier, Kaeck, Andreas, Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, van Kervel, Vincent, Khan, Saad, Alcock, Jamie, Khomyn, Marta, Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Krahnen, Jan Pieter, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Kwan, Amy, Alexeev, Vitali, Lajaunie, Quentin, Lam, Full Yet Eric Campbell, Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, (Michael) Hui, Aloosh, Arash, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver B., Liu, Jiacheng, Liu, Anqi, Llorente-Alvarez, Jesus-Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis A., Lopez-Lira, Alejandro, Amato, Livia, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah C, Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Amaya, Diego, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Nordén, Lars L., O'Neill, Peter, Angel, James J., Obaid, Khaled, Ødegaard, Bernt Arne, Östberg, Per, Painter, Marcus, Palan, Stefan, Palit, Imon, Park, Andreas, Pascual Gascó, Roberto, Pasquariello, Paolo, Pastor, Lubos, Dreber, Anna, Bach, Amadeus, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Baidoo, Edwin, Press, Oliver-Alexander, Prodromou, Tina, Putnins, Talis J., Raizada, Gaurav, Rakowski, David A., Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Wang, Renjie, Bakalli, Gaetan, Renò, Roberto, Riddiough, Steven, Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Rösch, Dominik, Rognone, Lavinia, Roseman, Brian, Barbon, Andrea, Rosu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen, Rzayev, Khaladdin, Rzeźnik, Aleksandra, Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Bashchenko, Oksana, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus Reiner, Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Bindra, Parampreet Christopher, Seeger, Norman, Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Bjonnes, Geir Hoidal, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Neusüss, Sebastian, Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nicholas, Tham, Wing Wah, Theissen, Erik, Black, Jeffrey R., Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Black, Bernard S., Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, van der Wel, Michel, Werner, Ingrid M., Bohorquez, Santiago, Westerholm, P. Joakim, Westheide, Christian, Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Xia, Shuo, Xiu, Dacheng, Holzmeister, Felix, Bondarenko, Oleg, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Shihao, Yueshen, Bart Zhou, Bos, Charles S., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan, Zhang, Lu, Zhang, Xiaoyu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu, Bosch-Rosa, Ciril, Zoican, Marius, Zwinkels, Remco C.J., Chen, Jian, Duevski, Teodor, Gao, Ge, Gemayel, Roland, Gilder, Dudley, Kuhle, Paul, Pagnotta, Emiliano, Pelli, Michele, Bouri, Elie, Sönksen, Jantje, Ilczuk, Konrad, Bogoev, Dimitar, Qian, Ya, Wika, Hans C., Yu, Yihe, Zhao, Lu, Mi, Michael, Bao, Li, Brownlees, Christian T., Vaduva, Andreea, Prokopczuk, Marcel, Avetikian, Alejandro, Wu, Zhen-Xing, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera, Laura, Caporin, Massimiliano, Huber, Juergen, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chernov, Mikhail, Cheung, William Ming Yan, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Johanneson, Magnus, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca, Declerck, Fany, Deev, Oleg, Degryse, Hans, Kirchler, Michael, Deku, Solomon, Desagre, Christophe, Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip, Dudda, Tom, Dumitrescu, Ariadna, Dyakov, Teodor, Razen, Michael, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, ter Ellen, Saskia, Eugster, Nicolas, Evans, Martin D.D., Farrell, Michael, Félez-Viñas, Ester, Ferrara, Gerardo, Weitzel, Utz, FERROUHI, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan, Foley, Sean, Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco A., Frijns, Bart, Frömmel, Michael, Abad, David, Fu, Servanna Mianjun, Füllbrunn, Sascha, Gan, Baoqing, Gehrig, Thomas, Gerritsen, Dirk, Gil-Bazo, Javier, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Güçbilmez, Ufuk, and Van Dijk, Matthijs A.

Abstract

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Published
2024

47. Nonstandard Errors

Author

Universidad de Alicante. Departamento de Economía Financiera y Contabilidad, Menkveld, Albert J., Dreber, Anna, Holzmeister, Felix, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Neusüß, Sebastian, Razen, Michael, Weitzel, Utz, Abad, David, Abudy, Menachem (Meni), Adrian, Tobias, Ait-Sahalia, Yacine, Akmansoy, Olivier, Alcock, Jamie T., Alexeev, Vitali, Aloosh, Arash, Amato, Livia, Amaya, Diego, Angel, James J., Avetikian, Alejandro T., Bach, Amadeus, Baidoo, Edwin, Bakalli, Gaetan, Bao, Li, Barbon, Andrea, Bashchenko, Oksana, Bindra, Parampreet C., Bjønnes, Geir H., Black, Jeffrey R., Black, Bernard S., Bogoev, Dimitar, Bohorquez Correa, Santiago, Bondarenko, Oleg, Bos, Charles S., Bosch-Rosa, Ciril, Bouri, Elie, Brownlees, Christian, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera Romero, Laura M., Caporin, Massimiliano, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chen, Jian, Chernov, Mikhail, Cheung, William, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung-Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca F., Declerck, Fany, Deev, Oleg, Degryse, Hans, Deku, Solomon Y., Desagre, Christophe, Van Dijk, Mathijs A., Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip A., Dudda, Tom, Duevski, Teodor, Dumitrescu, Ariadna, Dyakov, Teodor, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, Ellen, Saskia Ter, Eugster, Nicolas, Evans, Martin D. D., Farrell, Michael, Felez-Vinas, Ester, Ferrara, Gerardo, Ferrouhi, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan T., Foley, Sean D. V., Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco, Frijns, Bart, Frömmel, Michael, Fu, Servanna M., Füllbrunn, Sascha C., Gan, Baoqing, Gao, Ge, Gehrig, Thomas P., Gemayel, Roland, Gerritsen, Dirk, Gil-Bazo, Javier, Gilder, Dudley, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Grammig, Joachim, Grégoire, Vincent, Güçbilmez, Ufuk, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, He, Xue-Zhong (Tony), Heath, Davidson, Hediger, Simon, Hendershott, Terrence, Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth A., Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ilczuk, Konrad, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylhä, Petri, Kaeck, Andreas T., Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, Van Kervel, Vincent, Khan, Saad A., Khomyn, Marta K., Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Krahnen, Jan P., Kuhle, Paul, Kwan, Amy, Lajaunie, Quentin, Lam, F. Y. Eric C., Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, Hui, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver, Liu, Jiacheng, Liu, Anqi, Llorente, Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis, Lopez-Lira, Alejandro, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah, Mi, Michael G., Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Norden, Lars L., O'neill, Peter, Obaid, Khaled, Ødegaard, Bernt A., Östberg, Per, Pagnotta, Emiliano, Painter, Marcus, Palan, Stefan, Palit, Imon J., Park, Andreas, Pascual, Roberto, Pasquariello, Paolo, Pastor, Lubos, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelli, Michele, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Press, Oliver-Alexander, Prodromou, Tina, Prokopczuk, Marcel, Putnins, Talis, Qian, Ya, Raizada, Gaurav, Rakowski, David, Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Renjie, Rex W., Reno, Roberto, Riddiough, Steven J., Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Roesch, Dominik, Rognone, Lavinia, Roseman, Brian, Roşu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen R., Rzayev, Khaladdin, Rzeźnik, Aleksandra A., Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus R., Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Seeger, Norman J., Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Sönksen, Jantje, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nick, Tham, Wing Wah, Theissen, Erik, Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Vaduva, M. Andreea, Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, Van Der Wel, Michel, Werner, Ingrid M., Westerholm, P. Joakim, Westheide, Christian, Wika, Hans C., Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Wu, Zhen-Xing, Xia, Shuo, Xiu, Dacheng, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Yihe, Yu, Shihao, Yueshen, Bart Z., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan M., Zhang, Lu, Zhang, S. Sarah, Zhang, Xiaoyu, Zhao, Lu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu S., Zoican, Marius, Zwinkels, Remco, Universidad de Alicante. Departamento de Economía Financiera y Contabilidad, Menkveld, Albert J., Dreber, Anna, Holzmeister, Felix, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Neusüß, Sebastian, Razen, Michael, Weitzel, Utz, Abad, David, Abudy, Menachem (Meni), Adrian, Tobias, Ait-Sahalia, Yacine, Akmansoy, Olivier, Alcock, Jamie T., Alexeev, Vitali, Aloosh, Arash, Amato, Livia, Amaya, Diego, Angel, James J., Avetikian, Alejandro T., Bach, Amadeus, Baidoo, Edwin, Bakalli, Gaetan, Bao, Li, Barbon, Andrea, Bashchenko, Oksana, Bindra, Parampreet C., Bjønnes, Geir H., Black, Jeffrey R., Black, Bernard S., Bogoev, Dimitar, Bohorquez Correa, Santiago, Bondarenko, Oleg, Bos, Charles S., Bosch-Rosa, Ciril, Bouri, Elie, Brownlees, Christian, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera Romero, Laura M., Caporin, Massimiliano, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chen, Jian, Chernov, Mikhail, Cheung, William, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung-Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca F., Declerck, Fany, Deev, Oleg, Degryse, Hans, Deku, Solomon Y., Desagre, Christophe, Van Dijk, Mathijs A., Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip A., Dudda, Tom, Duevski, Teodor, Dumitrescu, Ariadna, Dyakov, Teodor, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, Ellen, Saskia Ter, Eugster, Nicolas, Evans, Martin D. D., Farrell, Michael, Felez-Vinas, Ester, Ferrara, Gerardo, Ferrouhi, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan T., Foley, Sean D. V., Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco, Frijns, Bart, Frömmel, Michael, Fu, Servanna M., Füllbrunn, Sascha C., Gan, Baoqing, Gao, Ge, Gehrig, Thomas P., Gemayel, Roland, Gerritsen, Dirk, Gil-Bazo, Javier, Gilder, Dudley, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Grammig, Joachim, Grégoire, Vincent, Güçbilmez, Ufuk, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, He, Xue-Zhong (Tony), Heath, Davidson, Hediger, Simon, Hendershott, Terrence, Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth A., Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ilczuk, Konrad, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylhä, Petri, Kaeck, Andreas T., Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, Van Kervel, Vincent, Khan, Saad A., Khomyn, Marta K., Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Krahnen, Jan P., Kuhle, Paul, Kwan, Amy, Lajaunie, Quentin, Lam, F. Y. Eric C., Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, Hui, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver, Liu, Jiacheng, Liu, Anqi, Llorente, Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis, Lopez-Lira, Alejandro, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah, Mi, Michael G., Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Norden, Lars L., O'neill, Peter, Obaid, Khaled, Ødegaard, Bernt A., Östberg, Per, Pagnotta, Emiliano, Painter, Marcus, Palan, Stefan, Palit, Imon J., Park, Andreas, Pascual, Roberto, Pasquariello, Paolo, Pastor, Lubos, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelli, Michele, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Press, Oliver-Alexander, Prodromou, Tina, Prokopczuk, Marcel, Putnins, Talis, Qian, Ya, Raizada, Gaurav, Rakowski, David, Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Renjie, Rex W., Reno, Roberto, Riddiough, Steven J., Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Roesch, Dominik, Rognone, Lavinia, Roseman, Brian, Roşu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen R., Rzayev, Khaladdin, Rzeźnik, Aleksandra A., Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus R., Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Seeger, Norman J., Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Sönksen, Jantje, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nick, Tham, Wing Wah, Theissen, Erik, Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Vaduva, M. Andreea, Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, Van Der Wel, Michel, Werner, Ingrid M., Westerholm, P. Joakim, Westheide, Christian, Wika, Hans C., Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Wu, Zhen-Xing, Xia, Shuo, Xiu, Dacheng, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Yihe, Yu, Shihao, Yueshen, Bart Z., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan M., Zhang, Lu, Zhang, S. Sarah, Zhang, Xiaoyu, Zhao, Lu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu S., Zoican, Marius, and Zwinkels, Remco

Abstract

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Published
2024

48. Non-Standard Errors

Author

Leerstoel Stigchel, Helmholtz Institute, Experimental Psychology (onderzoeksprogramma PF), Finance, UU LEG Research UUSE Multidisciplinary Economics, Sub General Pharmaceutics, Menkveld, Albert J., Abudy, Menachem (Meni), Grammig, Joachim, Gregoire, Vincent, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, Adrian, Tobias, He, Xue-Zhong 'Tony', Heath, Davidson, Hediger, Simon, Hendershott, Terrence J., Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth, Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Ait-Sahalia, Yacine, Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylha, Petri, Akmansoy, Olivier, Kaeck, Andreas, Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, van Kervel, Vincent, Khan, Saad, Alcock, Jamie, Khomyn, Marta, Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Krahnen, Jan Pieter, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Kwan, Amy, Alexeev, Vitali, Lajaunie, Quentin, Lam, Full Yet Eric Campbell, Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, (Michael) Hui, Aloosh, Arash, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver B., Liu, Jiacheng, Liu, Anqi, Llorente-Alvarez, Jesus-Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis A., Lopez-Lira, Alejandro, Amato, Livia, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah C, Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Amaya, Diego, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Nordén, Lars L., O'Neill, Peter, Angel, James J., Obaid, Khaled, Ødegaard, Bernt Arne, Östberg, Per, Painter, Marcus, Palan, Stefan, Palit, Imon, Park, Andreas, Pascual Gascó, Roberto, Pasquariello, Paolo, Pastor, Lubos, Dreber, Anna, Bach, Amadeus, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Baidoo, Edwin, Press, Oliver-Alexander, Prodromou, Tina, Putnins, Talis J., Raizada, Gaurav, Rakowski, David A., Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Wang, Renjie, Bakalli, Gaetan, Renò, Roberto, Riddiough, Steven, Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Rösch, Dominik, Rognone, Lavinia, Roseman, Brian, Barbon, Andrea, Rosu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen, Rzayev, Khaladdin, Rzeźnik, Aleksandra, Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Bashchenko, Oksana, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus Reiner, Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Bindra, Parampreet Christopher, Seeger, Norman, Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Bjonnes, Geir Hoidal, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Neusüss, Sebastian, Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nicholas, Tham, Wing Wah, Theissen, Erik, Black, Jeffrey R., Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Black, Bernard S., Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, van der Wel, Michel, Werner, Ingrid M., Bohorquez, Santiago, Westerholm, P. Joakim, Westheide, Christian, Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Xia, Shuo, Xiu, Dacheng, Holzmeister, Felix, Bondarenko, Oleg, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Shihao, Yueshen, Bart Zhou, Bos, Charles S., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan, Zhang, Lu, Zhang, Xiaoyu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu, Bosch-Rosa, Ciril, Zoican, Marius, Zwinkels, Remco C.J., Chen, Jian, Duevski, Teodor, Gao, Ge, Gemayel, Roland, Gilder, Dudley, Kuhle, Paul, Pagnotta, Emiliano, Pelli, Michele, Bouri, Elie, Sönksen, Jantje, Ilczuk, Konrad, Bogoev, Dimitar, Qian, Ya, Wika, Hans C., Yu, Yihe, Zhao, Lu, Mi, Michael, Bao, Li, Brownlees, Christian T., Vaduva, Andreea, Prokopczuk, Marcel, Avetikian, Alejandro, Wu, Zhen-Xing, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera, Laura, Caporin, Massimiliano, Huber, Juergen, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chernov, Mikhail, Cheung, William Ming Yan, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Johanneson, Magnus, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca, Declerck, Fany, Deev, Oleg, Degryse, Hans, Kirchler, Michael, Deku, Solomon, Desagre, Christophe, Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip, Dudda, Tom, Dumitrescu, Ariadna, Dyakov, Teodor, Razen, Michael, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, ter Ellen, Saskia, Eugster, Nicolas, Evans, Martin D.D., Farrell, Michael, Félez-Viñas, Ester, Ferrara, Gerardo, Weitzel, Utz, FERROUHI, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan, Foley, Sean, Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco A., Frijns, Bart, Frömmel, Michael, Abad, David, Fu, Servanna Mianjun, Füllbrunn, Sascha, Gan, Baoqing, Gehrig, Thomas, Gerritsen, Dirk, Gil-Bazo, Javier, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Güçbilmez, Ufuk, Van Dijk, Matthijs A., Leerstoel Stigchel, Helmholtz Institute, Experimental Psychology (onderzoeksprogramma PF), Finance, UU LEG Research UUSE Multidisciplinary Economics, Sub General Pharmaceutics, Menkveld, Albert J., Abudy, Menachem (Meni), Grammig, Joachim, Gregoire, Vincent, Hagströmer, Björn, Hambuckers, Julien, Hapnes, Erik, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, Hasse, Jean-Baptiste, Hautsch, Nikolaus, Adrian, Tobias, He, Xue-Zhong 'Tony', Heath, Davidson, Hediger, Simon, Hendershott, Terrence J., Hibbert, Ann Marie, Hjalmarsson, Erik, Hoelscher, Seth, Hoffmann, Peter, Holden, Craig W., Horenstein, Alex R., Ait-Sahalia, Yacine, Huang, Wenqian, Huang, Da, Hurlin, Christophe, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jalkh, Naji, Jones, Charles M., Jurkatis, Simon, Jylha, Petri, Akmansoy, Olivier, Kaeck, Andreas, Kaiser, Gabriel, Karam, Arzé, Karmaziene, Egle, Kassner, Bernhard, Kaustia, Markku, Kazak, Ekaterina, Kearney, Fearghal, van Kervel, Vincent, Khan, Saad, Alcock, Jamie, Khomyn, Marta, Klein, Tony, Klein, Olga, Klos, Alexander, Koetter, Michael, Krahnen, Jan Pieter, Kolokolov, Aleksey, Korajczyk, Robert A., Kozhan, Roman, Kwan, Amy, Alexeev, Vitali, Lajaunie, Quentin, Lam, Full Yet Eric Campbell, Lambert, Marie, Langlois, Hugues, Lausen, Jens, Lauter, Tobias, Leippold, Markus, Levin, Vladimir, Li, Yijie, Li, (Michael) Hui, Aloosh, Arash, Liew, Chee Yoong, Lindner, Thomas, Linton, Oliver B., Liu, Jiacheng, Liu, Anqi, Llorente-Alvarez, Jesus-Guillermo, Lof, Matthijs, Lohr, Ariel, Longstaff, Francis A., Lopez-Lira, Alejandro, Amato, Livia, Mankad, Shawn, Mano, Nicola, Marchal, Alexis, Martineau, Charles, Mazzola, Francesco, Meloso, Debrah C, Mihet, Roxana, Mohan, Vijay, Moinas, Sophie, Moore, David, Amaya, Diego, Mu, Liangyi, Muravyev, Dmitriy, Murphy, Dermot, Neszveda, Gabor, Neumeier, Christian, Nielsson, Ulf, Nimalendran, Mahendrarajah, Nolte, Sven, Nordén, Lars L., O'Neill, Peter, Angel, James J., Obaid, Khaled, Ødegaard, Bernt Arne, Östberg, Per, Painter, Marcus, Palan, Stefan, Palit, Imon, Park, Andreas, Pascual Gascó, Roberto, Pasquariello, Paolo, Pastor, Lubos, Dreber, Anna, Bach, Amadeus, Patel, Vinay, Patton, Andrew J., Pearson, Neil D., Pelizzon, Loriana, Pelster, Matthias, Pérignon, Christophe, Pfiffer, Cameron, Philip, Richard, Plíhal, Tomáš, Prakash, Puneet, Baidoo, Edwin, Press, Oliver-Alexander, Prodromou, Tina, Putnins, Talis J., Raizada, Gaurav, Rakowski, David A., Ranaldo, Angelo, Regis, Luca, Reitz, Stefan, Renault, Thomas, Wang, Renjie, Bakalli, Gaetan, Renò, Roberto, Riddiough, Steven, Rinne, Kalle, Rintamäki, Paul, Riordan, Ryan, Rittmannsberger, Thomas, Rodríguez Longarela, Iñaki, Rösch, Dominik, Rognone, Lavinia, Roseman, Brian, Barbon, Andrea, Rosu, Ioanid, Roy, Saurabh, Rudolf, Nicolas, Rush, Stephen, Rzayev, Khaladdin, Rzeźnik, Aleksandra, Sanford, Anthony, Sankaran, Harikumar, Sarkar, Asani, Sarno, Lucio, Bashchenko, Oksana, Scaillet, Olivier, Scharnowski, Stefan, Schenk-Hoppé, Klaus Reiner, Schertler, Andrea, Schneider, Michael, Schroeder, Florian, Schürhoff, Norman, Schuster, Philipp, Schwarz, Marco A., Seasholes, Mark S., Bindra, Parampreet Christopher, Seeger, Norman, Shachar, Or, Shkilko, Andriy, Shui, Jessica, Sikic, Mario, Simion, Giorgia, Smales, Lee A., Söderlind, Paul, Sojli, Elvira, Sokolov, Konstantin, Bjonnes, Geir Hoidal, Spokeviciute, Laima, Stefanova, Denitsa, Subrahmanyam, Marti G., Neusüss, Sebastian, Szaszi, Barnabas, Talavera, Oleksandr, Tang, Yuehua, Taylor, Nicholas, Tham, Wing Wah, Theissen, Erik, Black, Jeffrey R., Thimme, Julian, Tonks, Ian, Tran, Hai, Trapin, Luca, Trolle, Anders B., Valente, Giorgio, Van Ness, Robert A., Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Black, Bernard S., Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, van der Wel, Michel, Werner, Ingrid M., Bohorquez, Santiago, Westerholm, P. Joakim, Westheide, Christian, Wipplinger, Evert, Wolf, Michael, Wolff, Christian C. P., Wolk, Leonard, Wong, Wing-Keung, Wrampelmeyer, Jan, Xia, Shuo, Xiu, Dacheng, Holzmeister, Felix, Bondarenko, Oleg, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Shihao, Yueshen, Bart Zhou, Bos, Charles S., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan, Zhang, Lu, Zhang, Xiaoyu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu, Bosch-Rosa, Ciril, Zoican, Marius, Zwinkels, Remco C.J., Chen, Jian, Duevski, Teodor, Gao, Ge, Gemayel, Roland, Gilder, Dudley, Kuhle, Paul, Pagnotta, Emiliano, Pelli, Michele, Bouri, Elie, Sönksen, Jantje, Ilczuk, Konrad, Bogoev, Dimitar, Qian, Ya, Wika, Hans C., Yu, Yihe, Zhao, Lu, Mi, Michael, Bao, Li, Brownlees, Christian T., Vaduva, Andreea, Prokopczuk, Marcel, Avetikian, Alejandro, Wu, Zhen-Xing, Calamia, Anna, Cao, Viet Nga, Capelle-Blancard, Gunther, Capera, Laura, Caporin, Massimiliano, Huber, Juergen, Carrion, Allen, Caskurlu, Tolga, Chakrabarty, Bidisha, Chernov, Mikhail, Cheung, William Ming Yan, Chincarini, Ludwig B., Chordia, Tarun, Chow, Sheung Chi, Clapham, Benjamin, Colliard, Jean-Edouard, Johanneson, Magnus, Comerton-Forde, Carole, Curran, Edward, Dao, Thong, Dare, Wale, Davies, Ryan J., De Blasis, Riccardo, De Nard, Gianluca, Declerck, Fany, Deev, Oleg, Degryse, Hans, Kirchler, Michael, Deku, Solomon, Desagre, Christophe, Dim, Chukwuma, Dimpfl, Thomas, Dong, Yun Jiang, Drummond, Philip, Dudda, Tom, Dumitrescu, Ariadna, Dyakov, Teodor, Razen, Michael, Dyhrberg, Anne Haubo, Dzieliński, Michał, Eksi, Asli, El Kalak, Izidin, ter Ellen, Saskia, Eugster, Nicolas, Evans, Martin D.D., Farrell, Michael, Félez-Viñas, Ester, Ferrara, Gerardo, Weitzel, Utz, FERROUHI, El Mehdi, Flori, Andrea, Fluharty-Jaidee, Jonathan, Foley, Sean, Fong, Kingsley Y. L., Foucault, Thierry, Franus, Tatiana, Franzoni, Francesco A., Frijns, Bart, Frömmel, Michael, Abad, David, Fu, Servanna Mianjun, Füllbrunn, Sascha, Gan, Baoqing, Gehrig, Thomas, Gerritsen, Dirk, Gil-Bazo, Javier, Glosten, Lawrence R., Gomez, Thomas, Gorbenko, Arseny, Güçbilmez, Ufuk, and Van Dijk, Matthijs A.

Published
2024

49. Plasma Proteomics of Exercise Blood Pressure and Incident Hypertension.

Author

Rao, Prashant, Keyes, Michelle. J., Mi, Michael Y., Barber, Jacob L., Tahir, Usman A., Deng, Shuliang, Clish, Clary B., Shen, Dongxiao, Farrell, Laurie. A., Wilson, James G., Gao, Yan, Yimer, Wondwosen K., Ekunwe, Lynette, Hall, Michael E., Muntner, Paul M., Guo, Xiuqing, Taylor, Kent D., Tracy, Russell P., Rich, Stephen S., and Rotter, Jerome I.

Published
2024
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