  The advances of sensor technology enable people to monitor air quality
through widely distributed low-cost sensors. However, measurements from these
sensors usually encounter high biases and require a calibration step to reach
an acceptable performance in down-streaming analytical tasks. Most existing
calibration methods calibrate one type of sensor at a time, which we call
single-task calibration. Despite the popularity of this single-task schema, it
may neglect interactions among calibration tasks of different sensors, which
encompass underlying information to promote calibration performance. In this
paper, we propose a multi-task calibration network (MTNet) to calibrate
multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors)
simultaneously, modeling the interactions among tasks. MTNet consists of a
single shared module, and several task-specific modules. Specifically, in the
shared module, we extend the multi-gate mixture-of-experts structure to
harmonize the task conflicts and correlations among different tasks; in each
task-specific module, we introduce a feature selection strategy to customize
the input for the specific task. These improvements allow MTNet to learn
interaction information shared across different tasks, and task-specific
information for each calibration task as well. We evaluate MTNet on three
real-world datasets and compare it with several established baselines. The
experimental results demonstrate that MTNet achieves the state-of-the-art
performance.
