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Your search keyword '"Zhou, Xiaoxiao"' showing total 522 results
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522 results on '"Zhou, Xiaoxiao"'

1. Sample size and power calculation for propensity score analysis of observational studies

Author

Liu, Bo, Zhou, Xiaoxiao, and Li, Fan

Subjects
Statistics - Methodology
Abstract

This paper develops theoretically justified analytical formulas for sample size and power calculation in the propensity score analysis of causal inference using observational data. By analyzing the variance of the inverse probability weighting estimator of the average treatment effect (ATE), we clarify the three key components for sample size calculations: propensity score distribution, potential outcome distribution, and their correlation. We devise analytical procedures to identify these components based on commonly available and interpretable summary statistics. We elucidate the critical role of covariate overlap between treatment groups in determining the sample size. In particular, we propose to use the Bhattacharyya coefficient as a measure of covariate overlap, which, together with the treatment proportion, leads to a uniquely identifiable and easily computable propensity score distribution. The proposed method is applicable to both continuous and binary outcomes. We show that the standard two-sample $z$-test and variance inflation factor methods often lead to, sometimes vastly, inaccurate sample size estimates, especially with limited overlap. We also derive formulas for the average treatment effects for the treated (ATT) and overlapped population (ATO) estimands. We provide simulated and real examples to illustrate the proposed method. We develop an associated R package PSpower.

Published
2025

23. KRNET: Image Denoising with Kernel Regulation Network

Author

Liu, Peng, Zhou, Xiaoxiao, Li, Junyiyang, D, El Basha Mohammad, and Fang, Ruogu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination of kernels (filters) on the input. However, existing CNN-based methods mainly focus on synthetic gray-scale images. These methods still exhibit low performance when tackling multi-channel color image denoising. In this paper, we optimize CNN regularization capability by developing a kernel regulation module. In particular, we propose a kernel regulation network-block, referred to as KR-block, by integrating the merits of both large and small kernels, that can effectively estimate features in solving image denoising. We build a deep CNN-based denoiser, referred to as KRNET, via concatenating multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN), multi-channel (MC) noise, and realistic noise, where KRNET obtains significant performance gains over state-of-the-art methods across a wide spectrum of noise levels.

Published
2019

24. Image Restoration Using Deep Regulated Convolutional Networks

Author

Liu, Peng, Zhou, Xiaoxiao, Li, Yangjunyi, D, El Basha Mohammad, and Fang, Ruogu

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

While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest. The width of networks, defined as the size of the receptive fields and the density of the channels, has demonstrated crucial importance in low-level vision tasks such as image denoising and restoration. However, the limited generalization ability, due to the increased width of networks, creates a bottleneck in designing wider networks. In this paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep network composed of regulated sub-network blocks cascaded by skip-connections, to overcome this bottleneck. Specifically, the Regulated Convolution block (RC-block), featured by a combination of large and small convolution filters, balances the effectiveness of prominent feature extraction and the generalization ability of the network. RC-Nets have several compelling advantages: they embrace diversified features through large-small filter combinations, alleviate the hazy boundary and blurred details in image denoising and super-resolution problems, and stabilize the learning process. Our proposed RC-Nets outperform state-of-the-art approaches with significant performance gains in various image restoration tasks while demonstrating promising generalization ability. The code is available at https://github.com/cswin/RC-Nets.

Published
2019

28. A Fine-Grained Approach for Vulnerabilities Discovery Using Augmented Vulnerability Signatures

Author

Zhou, Xiaoxiao, Niu, Weina, Zhang, Xiaosong, Chen, Ruidong, Wang, Yan, 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, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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44. Pain scores reduction with the use of ultrasound-guided paracervical nerve block in patients with cervical cancer undergoing intracavitary brachytherapy: A randomized controlled trial.

Author

Rong, Yuanyuan, Yang, Yi, Zhang, Xi, Zhou, Xiaoxiao, Fu, Jianfeng, Zhao, Xuelian, Liu, Huaqin, Pei, Huanshuang, and Zhou, Chao

Subjects
PATIENT satisfaction, NERVE block, PAIN management, RANDOMIZED controlled trials, VISUAL analog scale
Abstract

Study objective: To determine the safety and effectiveness of ultrasound-guided paracervical nerve blocks for the painless treatment of patients with cervical cancer post-implantation. Design: Single-center randomized controlled trial. Setting: Fourth Hospital of Hebei Medical University (July 2023 to October 2023). Trial number: ChiCTR2300071580 [https://clin.larvol.com/trial-detail/ChiCTR2300071580]. Patients: Eighty patients with cervical cancer underwent post-implantation treatment. Interventions: Patients receiving and not receiving paracervical nerve blocks (Groups T and C, respectively) were randomly allocated. Measurements: Primary measures included visual analog scale (VAS) scores and patient body movement scores at various stages, including vaginal speculum placement (T1), applicator/needle insertion (T2), treatment administration following connection of the treatment tube (T3), needle withdrawal and hemostasis (T4), and willingness to receive further treatment. The secondary observation indices in this study included the operation time, incidence of hypoxemia, occurrence of nausea and vomiting, adverse events related to the circulatory system, patient satisfaction score, operator satisfaction score, and operation duration required by patients with an Alderte score of ≥ 9. Main results: Forty patients each were randomly allocated into Groups T and C. The VAS scores did not differ significantly between the two groups at T1. However, at T3 and T4, the VAS scores of Group T was significantly lower than that of Group C. No significant difference was observed in the body movement scores between the two groups at T1 and T3. However, the body movement score of Group T was significantly lower than that of Group C (P < 0.001) at T2 and T4. Group T showed higher postoperative satisfaction and willingness to receive further treatment compared to that of Group C. Conclusions: Ultrasound-guided paracervical nerve block effectively reduced the pain scores in patients with cervical cancer undergoing post-implantation treatment and enhanced their inclination to undergo further treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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45. GEOCHEMICAL ANALYSES OF EOCENE OILS IN DEEPLY BURIED SANDSTONE RESERVOIRS IN THE DONGYING DEPRESSION, BOHAI BAY BASIN, NE CHINA.

Author

Zhou, Xiaoxiao, Li, Xiaojun, Song, Xu, Li, Yuzhi, Wang, Xuejun, Han, Ke, and Yan, Haiqing

Subjects
*MUDSTONE, *ANALYTICAL geochemistry, *PETROLEUM, *DRILL core analysis, *GENETIC correlations
Abstract

We report the results of organic geochemical analyses of 19 crude oil samples from reservoir sandstones in the 4th Member of the Eocene Shahejie Formation from wells in the Minfeng Sag, Dongying Depression, Bohai Bay Basin (NE China). In addition, 42 Shahejie Formation core samples of dark‐coloured mudstones, including 28 extracts, were analysed. Geochemical data included Rock‐Eval measurements, gas chromatography, GC‐MS and diamondoid analyses. Maceral analyses showed that mudstones in the 4th Member of the Shahejie Formation ("Es4") contain Types I and II1 kerogen. The member can be divided into upper (Es4s) and lower (Es4x) intervals. Oil‐prone Es4s rock samples have good to excellent hydrocarbon‐generating potential based on calculated initial TOC values; Rock‐Eval Tmax values indicate that they are sufficiently mature for hydrocarbon generation. Analytical results suggest that both Es4s and Es4x mudrocks are potential source rocks for oils produced at fields in the Minfeng Sag. Analysed crude oils from the Minfeng Sag were classified into three genetic groups. Group I oils are mature to highly mature and have undergone a moderate degree of thermal cracking. They are characterized by a low β‐carotane/nC25 ratio and C30 4‐methylsterane index (4MI); high values of oleanane index (oleanane /C30‐hopane), C27 diasterane/C27 regular sterane (C27Dia/C27), regular sterane/17α hopane and gammacerane/C30 hopane (G/H); and medium pristane/phytane ratios (Pr/Ph). This suggests that Group I oils are mostly derived from source rocks in the upper part of the Es4x unit which are interbedded with evaporites. Group II oils are mature and have high 4MI and Pr/Ph ratios, low oleanane index, regular sterane/17α hopane and C27Dia/C27 ratios, and mediumβ‐carotane/nC25 and G/H. These features are similar to those of Es4s source rocks, indicating their genetic correlation. Group III oils show the lowest maturity and highβ‐carotane/nC25 and regular sterane/17α hopane, and low oleanane index, Pr/Ph and 4MI. Previously‐published data indicates that oils similar to those in Group III were mainly sourced by Es4s mudstones. [ABSTRACT FROM AUTHOR]

Published
2024
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