All-optical wavelength-tunable narrow-linewidth fiber laser

Parameter regulations of narrow-linewidth fiber lasers in frequency domain has drawn considerable interests for widespread applications in the light quantum computing, precise coherent detection, and generation of micro-waves. All-optical methods provide compact, precise and fast accesses to achieving these lasers with wavelength-tunability. Here, the optical-thermal effects of graphene is utilized to precisely control operations of free-running lasers with a tuning speed of 140 MHz/ms. Assisted by the single-longitude-mode operation and linewidth suppression of stimulated Brillouin backscattering, we obtain an optical-controllable ~750 Hz fiber laser with a wavelength-tuning range of 3.7 nm

Optical polarization rogue waves from supercontinuum generation in zero dispersion fiber pumped by dissipative soliton

Optical rogue waves emerge in nonlinear optical systems with extremely large amplitudes, and leave without a trace. In this work, we reveal the emergence of optical polarization rogue waves in supercontinuum generation from a zero-dispersion fiber, pumped by a dissipative soliton laser. Flat spectral broadening is achieved by modulation instability, followed by cascaded four-wave-mixing. In this process, we identify the emergence of optical polarization rogue waves, based on the probability density function of the relative distance among polarization states. Experimental results show that optical polarization rogue waves originate from vector multi-wave-mixing. Besides, we observe double peaks, and even triple peaks in the histogram of the state of polarization. This is a new and intriguing property, never observed so far in optical rogue waves, for example those emerging in the statistics of pulse intensities. Our polarization domain statistical analysis provides a new insight into the still debated topic of the mechanism for rogue wave generation in optical supercontinuum

Knowledge-refined Denoising Network for Robust Recommendation

Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \textit{task-irrelevant knowledge propagation} and \textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL

Improving image quality in compressed ultrafast photography with a space- and intensity-constrained reconstruction algorithm

The single-shot compressed ultrafast photography (CUP) camera is the fastest receive-only camera in the world. In this work, we introduce an external CCD camera and a space- and intensity-constrained (SIC) reconstruction algorithm to improve the image quality of CUP. The CCD camera takes a time-unsheared image of the dynamic scene. Unlike the previously used unconstrained algorithm, the proposed algorithm incorporates both spatial and intensity constraints, based on the additional prior information provided by the external CCD camera. First, a spatial mask is extracted from the time-unsheared image to define the zone of action. Second, an intensity threshold constraint is determined based on the similarity between the temporally projected image of the reconstructed datacube and the time-unsheared image taken by the external CCD. Both simulation and experimental studies showed that the SIC reconstruction improves the spatial resolution, contrast, and general quality of the reconstructed image

Ultrafast imaging of light scattering dynamics using second-generation compressed ultrafast photography

We present single-shot real-time video recording of light scattering dynamics by second-generation compressed ultrafast photography (G2-CUP). Using G2-CUP at 100 billion frames per second, in a single camera exposure, we experimentally captured the evolution of the light intensity distribution in an engineered thin scattering plate assembly. G2-CUP, which implements a new reconstruction paradigm and a more efficient hardware design than its predecessors, markedly improves the reconstructed image quality. The ultrafast imaging reveals the instantaneous light scattering pattern as a photonic Mach cone. We envision that our technology will find a diverse range of applications in biomedical imaging, materials science, and physics

API Usage Recommendation via Multi-View Heterogeneous Graph Representation Learning

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1

Optical puff mediated laminar-turbulent polarization transition

Various physical structures exhibit a fundamentally probabilistic nature over diverse scales in space and time, to the point that the demarcation line between quantum and classic laws gets blurred. Here, we characterize the probability of intermittency in the laminar-turbulence transition of a partially mode-locked fiber laser system, whose degree of coherence is deteriorated by multiple mode mixing. Two competing processes, namely the proliferation and the decay of an optical turbulent puff, determine a critical behavior for the onset of turbulence in such a nonlinear dissipative system. A new kind of polarization rogue waves is introduced at the point of transition to polarization turbulence. The probabilistic description of the puff-mediated laminar-turbulence polarization transition provides an additional degree of freedom for our understanding of the complex physics of lasers