Background & Aims We performed a systematic review and meta-analysis to comprehensively
estimate adenoma miss rate (AMR) and advanced AMR (AAMR) and explore associated …

Quantitative trading (QT) has been a popular topic in both academia and the financial
industry since the 1970s. In the last decade, deep reinforcement learning (DRL) has garnered …

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement
learning, where agents cooperate with the assistance of a communication protocol and a …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia …

Current value-based multi-agent reinforcement learning methods optimize individual Q
values to guide individuals' behaviours via centralized training with decentralized execution (…

Background & Aims Although current quality indicators of colonoscopy recommend 6
minutes as the minimum standard for withdrawal time (WT), the impact of a WT longer than 6 …

Building agents with large language models (LLMs) for computer control is a burgeoning
research area, where the agent receives computer states and performs actions to complete …

With the rise of online e-commerce platforms, more and more customers prefer to shop online.
To sell more products, online platforms introduce various modules to recommend items …

Portfolio management via reinforcement learning is at the forefront of fintech research, which
explores how to optimally reallocate a fund into different financial assets over the long term …

The environmental pollution caused by plastic films urgently requires the development of
non-toxic, biodegradable, and renewable biopolymer films. However, the poor waterproof and …