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851 results on '"Zhao, Yuxi"'

1. Robust Emax Model Fitting: Addressing Nonignorable Missing Binary Outcome in Dose-Response Analysis

Author

Zhang, Jiangshan, Pradhan, Vivek, and Zhao, Yuxi

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

The Binary Emax model is widely employed in dose-response analysis during drug development, where missing data often pose significant challenges. Addressing nonignorable missing binary responses, where the likelihood of missing data is related to unobserved outcomes, is particularly important, yet existing methods often lead to biased estimates. This issue is compounded when using the regulatory-recommended imputing as treatment failure approach, known as non-responder imputation. Moreover, the problem of separation, where a predictor perfectly distinguishes between outcome classes, can further complicate likelihood maximization. In this paper, we introduce a penalized likelihood-based method that integrates a modified Expectation-Maximization algorithm in the spirit of Ibrahim and Lipsitz to effectively manage both nonignorable missing data and separation issues. Our approach applies a noninformative Jeffreys prior to the likelihood, reducing bias in parameter estimation. Simulation studies demonstrate that our method outperforms existing methods, such as NRI, and the superiority is further supported by its application to data from a Phase II clinical trial. Additionally, we have developed an R package, ememax, to facilitate the implementation of the proposed method.

Published
2024

2. SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations

Author

Jiang, Yujiao, Liao, Qingmin, Wang, Zhaolong, Lin, Xiangru, Lu, Zongqing, Zhao, Yuxi, Wei, Hanqing, Ye, Jingrui, Zhang, Yu, and Shao, Zhijing

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

Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with unified topology is paramount. However, existing human body datasets either have images or textured models and lack parametric models which fit clothes well. We propose a new parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned mesh while maintaining stable geometry in the face, hand and foot regions. We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a conditional variational autoencoder model that takes human pose and facial keypoints as input, and generates a photorealistic drivable human avatar., Comment: ICME 2024;Project page: https://alex-jyj.github.io/SMPLX-Lite/

Published
2024

9. Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling

Author

Zhao, Yuxi, Gong, Xiaowen, and Mao, Shiwen

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Federated learning (FL) has emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML, the labels of training data need to be generated manually by human agents. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior of clients who may not make desired effort in their local data labeling and local model computation and may misreport their local models to the FL server. We characterize the performance bounds on the training loss as a function of clients' data labeling effort, local computation effort, and reported local models. We devise truthful incentive mechanisms which incentivize strategic clients to make truthful efforts and report true local models to the server. The truthful design exploits the non-trivial dependence of the training loss on clients' efforts and local models. Under the truthful mechanisms, we characterize the server's optimal local computation effort assignments. We evaluate the proposed FL algorithms with crowdsourced data labeling and the incentive mechanisms using experiments., Comment: Accepted by INFOCOM 2023 (10 pages, 7 figures)

Published
2023

50. Human capital and entrepreneurial intention in deprived areas : a case study of Nottingham, UK

Author

Zhao, Yuxi

Abstract

In light of the relatively poor outcomes from the policies enacted to facilitate entrepreneurship in deprived areas that are not consistent with policymakers' expectations, this research study constitutes the importance of human capital to investigate its influence on entrepreneurial intention in deprived areas. Due to the given barriers and difficulties objectively existing in such areas, this research study points out the inapplicability of those prevalently applied theories and models that emphasize general relationships. Meanwhile, it argues that the superficiality of policymakers' preconceptions of a vicious circle has led to various barriers identified in such areas. Therefore, this research study draws upon existing literature to create a new entrepreneurial intention model with a particular relevance to deprived areas. By combining both secondary and primary data sources, quantitative data (i.e. a largescale secondary dataset and primary data collected through a survey) has been utilized to examine the existence of relationships in the new model, whilst qualitative data has been utilized to explore deeper possibilities behind them and reveal unexpected phenomena to further enrich comprehension of the relationships relating to entrepreneurship and human capital in deprived areas. This research study takes the first step in updating knowledge about the relationship between human capital and entrepreneurial intention in deprived areas, which can be regarded as a base for future researchers to further explore this specific field and rigorously test the new model. Moreover, a variety of bidirectional relationships between different neighbourhood mechanisms and different types of human capital found in this research study have revealed hidden factors to explain in more depth lower levels of human capital and entrepreneurship in deprived areas. In a disadvantaged environment, most importantly, this research study found local residents' psychological barriers play a more severe and lasting role in hampering their personal, human capital and entrepreneurial development compared to the simple deficiencies in each per se. These findings provide a deeper perspective about underlying local residents' specific demands for the government to consider adjustments to policy.

Published
2020

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