arXiv subjects

Yuxi Zhao

Publications and source records attributed to Yuxi Zhao.

7 recordsLinked to original sources

arXiv2026

Energy-Based Cell Association in Nonuniform Renewable Energy-Powered Cellular Networks: Analysis and Optimization of Carbon Efficiency

The increasing global push for carbon reduction highlights the importance of integrating renewable energy into the supply chain of cellular networks. However, due to the stochastic nature of renewable energy generation and the uneven load distribution across base stations, the utilization rate of renewable energy remains low. To address these challenges, this paper investigates the trade-off between carbon emissions and downlink throughput in cellular networks, offering insights into optimizing both network performance and sustainability. The renewable energy state of base station batteries and the number of occupied channels are modeled as a quasi-birth-death process. We construct models for the probability of channel blocking, average successful transmission probability for users, downlink throughput, carbon emissions, and carbon efficiency based on stochastic geometry. Based on these analyses, an energy-based cell association scheme is proposed to optimize the carbon efficiency of cellular networks. The results show that, compared to the closest cell association scheme, the energy-based cell association scheme is capable of reducing the carbon emissions of the network by 13.0% and improving the carbon efficiency by 11.3%.

eess.SP↗

arXiv2026

Proactive Anomaly Screen for Multiple Endpoints Using Bayesian Latent Class Modeling: A k-Step Ahead Approach

In clinical trials, ensuring the quality and validity of data for downstream analysis and results is paramount, thus necessitating thorough data monitoring. This typically involves employing edit checks and manual queries during data collection. Edit checks consist of straightforward schemes programmed into relational databases, though they lack the capacity to assess data intelligently. In contrast, manual queries are initiated by data managers who manually scrutinize the collected data, identifying discrepancies needing clarification or correction. Manual queries pose significant challenges, particularly when dealing with large-scale data in late-phase clinical trials. Moreover, they are reactive rather than predictive, meaning they address issues after they arise rather than preemptively preventing errors. In this paper, we propose a joint model for multiple endpoints, focusing on primary and key secondary measures, using a Bayesian latent class approach. This model incorporates adjustments for risk monitoring factors, enabling proactive, $k$-step ahead, detection of conflicting or anomalous patterns within the data. Furthermore, we develop individualized dynamic predictions at consecutive time-points to identify potential anomalous values based on observed data. This analysis can be integrated into electronic data capture systems to provide objective alerts to stakeholders. We present simulation results and demonstrate effectiveness of this approach with real-world data.

stat.OT↗

arXiv2025

Evaluating Bias Reduction Methods in Binary Emax Model for Reliable Dose-Response Estimation

The Binary Emax model is widely employed in dose-response analysis during Phase II clinical studies to identify the optimal dose for subsequence confirmatory trials. The parameter estimation and inference heavily rely on the asymptotic properties of Maximum Likelihood (ML) estimators; however, this approach may be questionable under small or moderate sample sizes and is not robust to violation of model assumptions. To provide a reliable solution, this paper examines three bias-reduction methods: the Cox-Snell bias correction, Firth-score modification, and a maximum penalized likelihood estimator (MPLE) using Jeffreys prior. Through comprehensive simulation studies, we evaluate the performance of these methods in reducing bias and controlling variance, especially when model assumptions are violated. The results demonstrate that both Firth and MPLE methods provide robust estimates, with MPLE outperforming in terms of stability and lower variance. We further illustrate the practical application of these methods using data from the TURANDOT study, a Phase II clinical trial. Our findings suggest that MPLE with Jeffreys prior offers an effective and reliable alternative to the Firth method, particularly for dose-response relationships that deviate from monotonicity, making it valuable for robust parameter estimation in dose-ranging studies.

stat.AP↗

arXiv2024

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

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.

stat.ME↗

arXiv2024

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

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.

cs.CV↗

arXiv2023

Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling

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.

cs.LG↗

arXiv2018

Testing the ABS method with the simulated Planck temperature maps

In this study, we apply the Analytical method of Blind Separation (ABS) of the cosmic microwave background (CMB) from foregrounds to estimate the CMB temperature power spectrum from multi-frequency microwave maps. We test the robustness of the ABS estimator and assess the accuracy of the power spectrum recovery by using realistic simulations based on the seven-frequency Planck data, including various frequency-dependent and spatially-varying foreground components (synchrotron, free-free, thermal dust and anomalous microwave emission), as well as an uncorrelated Gaussian-distributed instrumental noise. Considering no prior information about the foregrounds, the ABS estimator can analytically recover the CMB power spectrum over almost all scales with less than $0.5\%$ error for maps where the Galactic plane region ($|b|<10^{\circ}$) is masked out. To further test the flexibility and effectiveness of the ABS approach in a variety of situations, we apply the ABS to the simulated Planck maps in three cases: (1) without any mask, (2) imposing a two-times-stronger synchrotron emission and (3) including only the Galactic plane region ($|b|<10^{\circ}$) in the analysis. In such extreme cases, the ABS approach can still provide an unbiased estimate of band powers at the level of 1 $μ\rm{K}^2$ on average over all $\ell$ range, and the recovered powers are consistent with the input values within 1-$σ$ for most $\ell$ bins.

astro-ph.CO↗