6,013 research outputs found
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
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