Wei Zhao

PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks

Wen Qiu, Zhiqiang He, Wei Zhao, Hiroshi Masui

Most reinforcement learning controllers for these networks assume stationary conditions, and the few that handle change react to the external environment while leaving the network's internal state unexamined. We show that sustained non-stationarity damages this internal state dir…Read more

Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Xiongjie Liang, Yuechi Zhang, Weifeng Hu, Shiyan Lv, Fan Jia, Yan Zhang, et al.

ABSTRACT Pathological microenvironments linked to aging, trauma, malignancies, and metabolic disorders significantly hinder bone fractures and frequently result in fracture nonunion, posing substantial worldwide clinical difficulties. Widely prevalent therapies encounter difficul…Read more

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

Shihao Zhang, Yunzhi Li, Yuguang Yan, Junzhe Zhang, Wei Zhao, Bohan Wang, et al.

Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with human aesthetics and improve realism. These signals, however, incur substantial computational overhead, require costly human annot…Read more

Enhancing Human-Generative Artificial Intelligence Online Collaboration Outcomes: The Pivotal Function of Symbiotic Role Design

Nuo Cheng, Hongxia Liu, Xiaoqing Xu, Wei Zhao, Lifang Qiao, Guohao Zhang

While generative artificial intelligence (GAI) has emerged as a vital support tool for collaborative learning, further exploration is required to achieve effective human-machine symbiosis in online collaborative processes. Grounded in symbiosis theory, our study developed a role-…Read more

Comparation of Graph Neural Networks and Traditional Machine Learning for Property Prediction in All-Inorganic Perovskite Materials

Jingyu Liu, Xueqiong Su, Lishan Yang, Jiansen Ding, Jin Wang, Xing Ling, et al.

Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain lim…Read more

An Improved Non-Destructive and High-Efficiency Model for Detecting the Purity of Denghai 605 Maize Variety Based on Machine Vision and Deep Learning

Jia Wang, Rui Ma, Wei Zhao, Hongjie Guo, Yuliang Yun, Li Li, et al.