Shaobo Liu

Yang Zhao, Chang Zhou, Jin Cao et al.

This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

Banafshe Felfeliyan, Yuyue Zhou, Shrimanti Ghosh et al.

Osteoarthritis (OA) poses a global health challenge, demanding precise diagnostic methods. Current radiographic assessments are time consuming and prone to variability, prompting the need for automated solutions. The existing deep learning models for OA assessment are unimodal single task systems and they don't incorporate relevant text information such as patient demographics, disease history, or physician reports. This study investigates employing Vision Language Processing (VLP) models to predict OA severity using Xray images and corresponding reports. Our method leverages Xray images of the knee and diverse report templates generated from tabular OA scoring values to train a CLIP (Contrastive Language Image PreTraining) style VLP model. Furthermore, we incorporate additional contrasting captions to enforce the model to discriminate between positive and negative reports. Results demonstrate the efficacy of these models in learning text image representations and their contextual relationships, showcase potential advancement in OA assessment, and establish a foundation for specialized vision language models in medical contexts.

Chang Zhou, Yang Zhao, Shaobo Liu et al.

In a society where traffic accidents frequently occur, fatigue driving has emerged as a grave issue. Fatigue driving detection technology, especially those based on the YOLOv8 deep learning model, has seen extensive research and application as an effective preventive measure. This paper discusses in depth the methods and technologies utilized in the YOLOv8 model to detect driver fatigue, elaborates on the current research status both domestically and internationally, and systematically introduces the processing methods and algorithm principles for various datasets. This study aims to provide a robust technical solution for preventing and detecting fatigue driving, thereby contributing significantly to reducing traffic accidents and safeguarding lives.