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3. Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

Author

Zhu, Hongtao, Zhang, Sizhe, Su, Yang, Zhao, Zhenyu, and Chen, Nan

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns., Comment: conference paper

Published
2024

4. Structural basis for the allosteric regulation and dynamic assembly of DNMT3B.

Author

Lu, Jiuwei, Fang, Jian, Zhu, Hongtao, Liang, Kimberly, Khudaverdyan, Nelli, and Song, Jikui

Abstract

Oligomerization of DNMT3B, a mammalian de novo DNA methyltransferase, critically regulates its chromatin targeting and DNA methylation activities. However, how the N-terminal PWWP and ADD domains interplay with the C-terminal methyltransferase (MTase) domain in regulating the dynamic assembly of DNMT3B remains unclear. Here, we report the cryo-EM structure of DNMT3B under various oligomerization states. The ADD domain of DNMT3B interacts with the MTase domain to form an autoinhibitory conformation, resembling the previously observed DNMT3A autoinhibition. Our combined structural and biochemical study further identifies a role for the PWWP domain and its associated ICF mutation in the allosteric regulation of DNMT3B tetramer, and a differential functional impact on DNMT3B by potential ADD-H3K4me0 and PWWP-H3K36me3 bindings. In addition, our comparative structural analysis reveals a coupling between DNMT3B oligomerization and folding of its substrate-binding sites. Together, this study provides mechanistic insights into the allosteric regulation and dynamic assembly of DNMT3B.

Published
2023

30. Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation

Author

Yap, Moi Hoon, Hachiuma, Ryo, Alavi, Azadeh, Brungel, Raphael, Cassidy, Bill, Goyal, Manu, Zhu, Hongtao, Ruckert, Johannes, Olshansky, Moshe, Huang, Xiao, Saito, Hideo, Hassanpour, Saeed, Friedrich, Christoph M., Ascher, David, Song, Anping, Kajita, Hiroki, Gillespie, David, Reeves, Neil D., Pappachan, Joseph, O'Shea, Claire, and Frank, Eibe

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarises the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhanced the F1-Score but not the mAP., Comment: 19 pages, 18 figures, 10 tables

Published
2020
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46. Paleogeomorphology Restoration of Post-Rift Basin: Volcanic Activity and Differential Subsidence Influence in Xihu Sag, East China Sea.

Author

Yu, Xiongbiao, Liu, Qianghu, Zhu, Hongtao, Li, Zhiyao, Qin, Lanzhi, and Xu, Donghao

Subjects
GRAVIMETRY, IGNEOUS provinces, PLATE tectonics, WATER depth, MAGNETIC measurements
Abstract

In post-rift basins, the thickness center, fine-grained deposit center, and subsidence center rarely converge. Clearing the three centers with the thickest center is difficult. In the Huangyan district of Xihu Sag, the East China Sea Shelf Basin, an Oligocene post-rift basin beneath major potential igneous provinces, has inconsistent thickness and composition. Analysis of core samples, drilling, and 3D seismic data corroborated this finding. This means that the formation thickness center does not match the lithology center, which indicates water depth. Gravity and magnetic measurements in the studied region show that significant magmatic activity is responsible for the difference between the center of thickness and the fine-grained deposit. Thermal sinking must be restored to fix this. Therefore, we propose (1) recreating the early Oligocene residual geomorphology in Huangyan using 3D seismic data. (2) Software computing quantitative subsidence. (3) Paleogeomorphology is verified by normal and trace element paleowater depths. (4) Reconstruct the paleogeomorphology and analyze how volcanic activity affected them and the three centers in the basin formed after tectonic plates separated. A shallow water delta and thermal subsidence show that magmatic activity is persistent in the north. With less thermal subsidence and deeper water, the southern area features a shallow lake sedimentary system. The thickness and fine-grained deposition centers were in the north and south, respectively. Geophysical and geological methods were used to reproduce the post-rift paleogeomorphology shaped by magmatic processes. [ABSTRACT FROM AUTHOR]

Published
2024
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