English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Urban air pollution has become a major environmental problem that threatenspublic health. It has become increasingly important to infer fine-grained urbanair quality based on existing monitoring stations. One of the challenges is howto effectively select some relevant stations for air quality inference. In thispaper, we propose a novel model based on reinforcement learning for urban airquality inference. The model consists of two modules: a station selector and anair quality regressor. The station selector dynamically selects the mostrelevant monitoring stations when inferring air quality. The air qualityregressor takes in the selected stations and makes air quality inference withdeep neural network. We conduct experiments on a real-world air quality datasetand our approach achieves the highest performance compared with several popularsolutions, and the experiments show significant effectiveness of proposed modelin tackling problems of air quality inference.

AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference