171 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
Dynamics in direct two-photon transition by frequency combs
Two-photon resonance transition technology has been proven to have a wide
range of applications,it's limited by the available wavelength of commercial
lasers.The application of optical comb technology with direct two-photon
transition (DTPT) will not be restricted by cw lasers.This article will further
theoretically analyze the dynamics effects of the DTPT process driven by
optical frequency combs. In a three-level atomic system, the population of
particles and the amount of momentum transfer on atoms are increased compared
to that of the DTPT-free process. The 17% of population increasement in 6-level
system of cesium atoms has verified that DTPT process has a robust enhancement
on the effect of momentum transfer. It can be used to excite the DTPTs of
rubidium and cesium simultaneously with the same mode-locked laser. And this
technology has potential applications in cooling different atoms to obtain
polar cold molecules, as well as high-precision spectroscopy measurement.Comment: 7 pages, 7 figure
Transfer Reinforcement Learning Based Negotiating Agent Framework
While achieving tremendous success, there is still a major issue standing out in the domain of automated negotiation: it is inefficient for a negotiating agent to learn a strategy from scratch when being faced with an unknown opponent. Transfer learning can alleviate this problem by utilizing the knowledge of previously learned policies to accelerate the current task learning. This work presents a novel Transfer Learning based Negotiating Agent (TLNAgent) framework that allows a negotiating agent to transfer previous knowledge from source strategies optimized by deep reinforcement learning, to boost its performance in new tasks. TLNAgent comprises three key components: the negotiation module, the adaptation module and the transfer module. To be specific, the negotiation module is responsible for interacting with the other agent during negotiation. The adaptation module measures the helpfulness of each source policy based on a fusion of two selection mechanisms. The transfer module is based on lateral connections between source and target networks and accelerates the agent’s training by transferring knowledge from the selected source strategy. Our comprehensive experiments clearly demonstrate that TL is effective in the context of automated negotiation, and TLNAgent outperforms state-of-the-art Automated Negotiating Agents Competition (ANAC) negotiating agents in various domains
Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Offline reinforcement learning (RL) is a learning paradigm where an agent
learns from a fixed dataset of experience. However, learning solely from a
static dataset can limit the performance due to the lack of exploration. To
overcome it, offline-to-online RL combines offline pre-training with online
fine-tuning, which enables the agent to further refine its policy by
interacting with the environment in real-time. Despite its benefits, existing
offline-to-online RL methods suffer from performance degradation and slow
improvement during the online phase. To tackle these challenges, we propose a
novel framework called Ensemble-based Offline-to-Online (E2O) RL. By increasing
the number of Q-networks, we seamlessly bridge offline pre-training and online
fine-tuning without degrading performance. Moreover, to expedite online
performance enhancement, we appropriately loosen the pessimism of Q-value
estimation and incorporate ensemble-based exploration mechanisms into our
framework. Experimental results demonstrate that E2O can substantially improve
the training stability, learning efficiency, and final performance of existing
offline RL methods during online fine-tuning on a range of locomotion and
navigation tasks, significantly outperforming existing offline-to-online RL
methods
SPI1-induced downregulation of FTO promotes GBM progression by regulating pri-miR-10a processing in an m6A-dependent manner
As one of the most common post-transcriptional modifications of mRNAs and noncoding RNAs, N6-methyladenosine (m6A) modification regulates almost every aspect of RNA metabolism. Evidence indicates that dysregulation of m6A modification and associated proteins contributes to glioblastoma (GBM) progression. However, the function of fat mass and obesity-associated protein (FTO), an m6A demethylase, has not been systematically and comprehensively explored in GBM. Here, we found that decreased FTO expression in clinical specimens correlated with higher glioma grades and poorer clinical outcomes. Functionally, FTO inhibited growth and invasion in GBM cells in vitro and in vivo. Mechanistically, FTO regulated the m6A modification of primary microRNA-10a (pri-miR-10a), which could be recognized by reader HNRNPA2B1, recruiting the microRNA microprocessor complex protein DGCR8 and mediating pri-miR-10a processing. Furthermore, the transcriptional activity of FTO was inhibited by the transcription factor SPI1, which could be specifically disrupted by the SPI1 inhibitor DB2313. Treatment with this inhibitor restored endogenous FTO expression and decreased GBM tumor burden, suggesting that FTO may serve as a novel prognostic indicator and therapeutic molecular target of GBM.publishedVersio
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