Yi Li

Yimian Ding, Xinqi Wang, Jingzehua Xu et al.

The Information Updating Networks (IUNs) offers significant potential for ocean exploration but encounters challenges due to dynamic underwater environments and severe system attenuation. Current methods relying on Autonomous Underwater Vehicles (AUVs) based on online reinforcement learning (RL) lead to high computational costs and low data utilization. To address these issues and the constraints of turbulent ocean environments, we propose a multi-AUV assisted data collection framework for IUNs based on multi-agent offline RL. This framework maximizes data rate and the value of information (VoI), minimizes energy consumption, and ensures collision avoidance by utilizing environmental and equipment status data. We introduce a semi-communication decentralized training with decentralized execution (SC-DTDE) paradigm and a multi-agent independent conservative Q-learning algorithm (MAICQL) to effectively tackle the problem. Extensive simulations demonstrate the high applicability, robustness, and data collection efficiency of the proposed framework.

Zhe Fei, Yi Li

Epigenetic aging clocks play a pivotal role in estimating an individual's biological age through the examination of DNA methylation patterns at numerous CpG (Cytosine-phosphate-Guanine) sites within their genome. However, making valid inferences on predicted epigenetic ages, or more broadly, on predictions derived from high-dimensional inputs, presents challenges. We introduce a novel U-learning approach via combinatory multi-subsampling for making ensemble predictions and constructing confidence intervals for predictions of continuous outcomes when traditional asymptotic methods are not applicable. More specifically, our approach conceptualizes the ensemble estimators within the framework of generalized U-statistics and invokes the Hájek projection for deriving the variances of predictions and constructing confidence intervals with valid conditional coverage probabilities. We apply our approach to two commonly used predictive algorithms, Lasso and deep neural networks (DNNs), and illustrate the validity of inferences with extensive numerical studies. We have applied these methods to predict the DNA methylation age (DNAmAge) of patients with various health conditions, aiming to accurately characterize the aging process and potentially guide anti-aging interventions.