Long-term Stabilization of Fiber Laser Using Phase-locking Technique with Ultra-low Phase Noise and Phase Drift

We review the conventional phase-locking technique in the long-term stabilization of the mode-locked fiber laser and investigate the phase noise limitation of the conventional technique. To break the limitation, we propose an improved phase-locking technique with an optic-microwave phase detector in achieving the ultra-low phase noise and phase drift. The mechanism and the theoretical model of the novel phase-locking technique are also discussed. The long-term stabilization experiments demonstrate that the improved technique can achieve the long-term stabilization for the MLFL with ultra-low phase noise and phase drift. The excellent locking performance of the improved phase-locking technique implies that this technique can be used to stabilize the mode-locked fiber laser with the highly stable H-master or optical clock without stability loss

A Collaborative Transfer Learning Framework for Cross-domain Recommendation

In the recommendation systems, there are multiple business domains to meet the diverse interests and needs of users, and the click-through rate(CTR) of each domain can be quite different, which leads to the demand for CTR prediction modeling for different business domains. The industry solution is to use domain-specific models or transfer learning techniques for each domain. The disadvantage of the former is that the data from other domains is not utilized by a single domain model, while the latter leverage all the data from different domains, but the fine-tuned model of transfer learning may trap the model in a local optimum of the source domain, making it difficult to fit the target domain. Meanwhile, significant differences in data quantity and feature schemas between different domains, known as domain shift, may lead to negative transfer in the process of transferring. To overcome these challenges, we propose the Collaborative Cross-Domain Transfer Learning Framework (CCTL). CCTL evaluates the information gain of the source domain on the target domain using a symmetric companion network and adjusts the information transfer weight of each source domain sample using the information flow network. This approach enables full utilization of other domain data while avoiding negative migration. Additionally, a representation enhancement network is used as an auxiliary task to preserve domain-specific features. Comprehensive experiments on both public and real-world industrial datasets, CCTL achieved SOTA score on offline metrics. At the same time, the CCTL algorithm has been deployed in Meituan, bringing 4.37% CTR and 5.43% GMV lift, which is significant to the business.Comment: KDD2023 accepte