We investigate the buildup dynamics of broadband Q-switched noise-like pulse (QS-NLP) driven by slow gain dynamics in a microfiber-based passively mode-locked Yb-doped fiber laser. Based on shot-to-shot tracing of the transient optical spectra and qu
alitatively reproduced numerial simulation, we demonstrate that slow gain dynamics is deeply involved in the onset of such complex temporal and spectral instabilities of QS-NLP. The proposed dynamic model in this work could contribute to deeper insight of such nonlinear dynamics and transient dynamics simulation in ultrafast fiber laser.
In this paper, to the best of our knowledge, we propose the first multi-rate Nyquist-subcarriers modulation (SCM) for C-band 100Gbit/s signal transmission over 50km dispersion-uncompensated link. Chromatic dispersion (CD) introduces severe spectral n
ulls on optical double-sideband signal, which greatly degrades the performance of intensity-modulation and direct-detection systems. In the previous works, high-complexity digital signal processing (DSP) is required to resist the CD-caused spectral nulls. Based on the characteristics of dispersive channel, Nyquist-SCM with multi-rate subcarriers is proposed to keep away from the CD-caused spectral nulls flexibly. Signal on each subcarrier can be individually recovered by a DSP with an acceptable complexity, including the feed-forward equalizer with no more than 31 taps, a two-tap post filter, and maximum likelihood sequence estimation with one memory length. Combining with entropy loading based on probabilistic constellation shaping to maximize the capacity-reach, the C-band 100Gbit/s multi-rate Nyquist-SCM signal over 50km dispersion-uncompensated link can achieve 7% hard-decision forward error correction limit and average normalized generalized mutual information of 0.967. In conclusion, the multi-rate Nyquist-SCM shows great potentials in solving the CD-caused spectral distortions.
Cluster randomized controlled trials (cRCTs) are designed to evaluate interventions delivered to groups of individuals. A practical limitation of such designs is that the number of available clusters may be small, resulting in an increased risk of ba
seline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved with respect to a pre-specified balance metric. However, several aspects of constrained randomization for the design and analysis of multi-arm cRCTs have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful permutation tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. The choice of balance metrics and candidate set size and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
Study of dissipative quantum phase transitions in the Ohmic spin-boson model is numerically challenging in a dense limit of environmental modes. In this work, large-scale numerical simulations are carried out based on the variational principle. The v
alidity of variational calculations, spontaneous breakdown of symmetries, and quantum fluctuations and correlations in the Ohmic bath are carefully analyzed, and the critical coupling as well as exponents are accurately determined in the weak tunneling and continuum limits. In addition, quantum criticality of the Ohmic bath is uncovered both in the delocalized phase and at the transition point.
Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm where the learner can continually adapt to changing environments with limi
ted labels. In this paper, we propose a new online setting -- Online Active Continual Adaptation, where the learner aims to continually adapt to changing distributions using both unlabeled samples and active queries of limited labels. To this end, we propose Online Self-Adaptive Mirror Descent (OSAMD), which adopts an online teacher-student structure to enable online self-training from unlabeled data, and a margin-based criterion that decides whether to query the labels to track changing distributions. Theoretically, we show that, in the separable case, OSAMD has an $O({T}^{1/2})$ dynamic regret bound under mild assumptions, which is even tighter than the lower bound $Omega(T^{2/3})$ of traditional online learning with full labels. In the general case, we show a regret bound of $O({alpha^*}^{1/3} {T}^{2/3} + alpha^* T)$, where $alpha^*$ denotes the separability of domains and is usually small. Our theoretical results show that OSAMD can fast adapt to changing environments with active queries. Empirically, we demonstrate that OSAMD achieves favorable regrets under changing environments with limited labels on both simulated and real-world data, which corroborates our theoretical findings.
In this paper, we present a field-trial C-band 72Gbit/s optical on-off keying (OOK) system over 18.8km dispersion-uncompensated submarine optical cable in the South China Sea. Chromatic dispersion (CD) of 18.8km submarine optical cable causes four sp
ectral nulls on the 36GHz bandwidth of 72Gbit/s OOK signal, which is the main obstacle for achieving an acceptable bit-error-rate (BER) performance. Decision feedback equalizer (DFE) is effective to compensate for the spectral nulls. However, DFE has a serious defect of burst-error propagation when the burst errors emerge due to the unstable submarine environment. Weighted DFE (WDFE) can be used to mitigate the burst-error propagation, but it cannot fully compensate for the spectral nulls because only a part of feedback symbols is directly decided. Fortunately, maximum likelihood sequence estimation (MLSE) can be added after the WDFE to simultaneously eliminate the resisting spectral distortions and implement optimal detection. Compared to the joint DFE and MLSE algorithm, the joint WDFE and MLSE algorithm can effectively suppress the burst-error propagation to obtain a maximum 2.9dB improvement of $boldsymbol{Q}$ factor and eliminate the phenomenon of BER floor. In conclusion, the joint WDFE and MLSE algorithm can solve the burst-error propagation for the field-trial fiber-optic communications.
Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful repres
entation. However, most of the existing methods are not capable of providing an accurate estimation of MI with low-variance when the MI is large. We argue that directly estimating the gradients of MI is more appealing for representation learning than estimating MI in itself. To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions. MIGE exhibits a tight and smooth gradient estimation of MI in the high-dimensional and large-MI settings. We expand the applications of MIGE in both unsupervised learning of deep representations based on InfoMax and the Information Bottleneck method. Experimental results have indicated significant performance improvement in learning useful representation.
In this paper, we propose adaptive channel-matched detection (ACMD) for C-band 64-Gbit/s intensity-modulation and direct-detection (IM/DD) optical on-off keying (OOK) system over a 100-km dispersion-uncompensated link. The proposed ACMD can adaptivel
y compensate most of the link distortions based on channel and noise characteristics, which includes a polynomial nonlinear equalizer (PNLE), a decision feedback equalizer (DFE) and maximum likelihood sequence estimation (MLSE). Based on the channel characteristics, PNLE eliminates the linear and nonlinear distortions, while the followed DFE compensates the spectral nulls caused by chromatic dispersion. Finally, based on the noise characteristics, a post filter can whiten the noise for implementing optimal signal detection using MLSE. To the best of our knowledge, we present a record C-band 64-Gbit/s IM/DD optical OOK system over a 100 km dispersion-uncompensated link achieving 7% hard-decision forward error correction limit using only the proposed ACMD at the receiver side. In conclusion, ACMD-based C-band 64-Gbit/s optical OOK system shows great potential for future optical interconnects.
With topologcial semimetal developing, semimetal with nodal-line ring comes into peoples vision as a powerful candidate for practical application of topological devices. We propose a method using ultracold atoms in two-dimensional amplitude-shaken bi
partite hexagonal optical lattice to simulate nodal-line semimetal, which can be achieved in experiment by attaching one triangular optical lattice to a hexangonal optical lattice and periodically modulating the intensity and position of the triangular lattice. By amplitude shaking, a time-reversal-symmetry-unstable mode is introduced into the bipartite optical lattice, and then the nodal-line semimetal is gotten by adjusting the proportion of such mode and the trivial mode of hexagonal lattice. Through calculating the energy spectrum of effective Hamiltonian, the transformation from Dirac semimetal to nodal-line semimetal in pace with changing shaking parameters is observed. We also study the change of Berry curvature and Berry phase in the transformation, which provides guidance on measuring the transformation in experiment. By analyzing the symmetry of the system, the emergence of the time-reversal-symmetry-unstable mode is researched. This proposal provides a way to research the pure nodal-line semimetal without the influence of other bands, which may contribute to the study of those unique features of surface states and bulk states of nodal-line semimetal.
Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning. In HRL, the policy model has an inner representation structured in levels. With this structure, the reinforcement learning task is
expected to be decomposed into corresponding levels with sub-tasks, and thus the learning can be more efficient. In HRL, although it is intuitive that a high-level policy only needs to make macro decisions in a low frequency, the exact frequency is hard to be simply determined. Previous HRL approaches often employed a fixed-time skip strategy or learn a terminal condition without taking account of the context, which, however, not only requires manual adjustments but also sacrifices some decision granularity. In this paper, we propose the emph{temporal-adaptive hierarchical policy learning} (TEMPLE) structure, which uses a temporal gate to adaptively control the high-level policy decision frequency. We train the TEMPLE structure with PPO and test its performance in a range of environments including 2-D rooms, Mujoco tasks, and Atari games. The results show that the TEMPLE structure can lead to improved performance in these environments with a sequential adaptive high-level control.
