Fei Teng

Large-scale industrial systems often include multiple scenarios to satisfy diverse user needs. The common approach of using one model per scenario does not scale well and not suitable for minor scenarios with limited samples. An solution is to train a model on all scenarios, which can introduce domination and bias from the main scenario. MMoE-like structures have been proposed for multi-scenario prediction, but they do not explicitly address the issue of gradient unbalancing. This work proposes an adaptive loss harmonizing (ALH) algorithm for multi-scenario CTR prediction. It dynamically adjusts the learning speed for balanced training and improved performance. Experiments on real industrial datasets and rigorous A/B testing prove our method’s superiority.

A theoretical model of the thermal phase noise in a square-wave modulated solid core photonic crystal fiber-optic gyroscope has been established, and then verified by measurements. The results demonstrate a good agreement between theory and experiment. The contribution of the thermal phase noise to the random walk coefficient of the gyroscope is derived. A fiber coil with 2.8 km length is used in the experimental solid core photonic crystal fiber-optic gyroscope, showing a random walk coefficient of 9.25 × 10⁻⁵ deg/√h.