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Resonant lattice Kerker effect in periodic resonators is one of the most interesting generalizations of the Kerker effect that relates to various fascinating functionalities such as scattering management and Huygens metasurfaces. However, so far this effect has been shown to be sensitive to the incident polarization, restricting its applications. Here, we report, for the first time, polarization-independent resonant lattice Kerker effect in metasurfaces composed of periodic Ge$_2$Se$_2$Te$_5$ (GST) disks. For such a metasurface of square lattice, the spectrally overlap of the electric dipole and magnetic dipole surface lattice resonances can be realized by choosing an appropriate GST crystalline fraction regardless of the incident polarization. The operation wavelength and the required GST crystalline fraction can be conveniently tuned over large ranges since these parameters scale linearly with the disk size and the lattice period, greatly facilitating the design. Making use of the obtained resonant lattice Kerker effect, we realize a reconfigurable and polarization-independent lattice Huygens' metasurface with a dynamic phase modulation of close to $2\pi$ and high transmittance. This work will advance the engineering of the resonant lattice Kerker effect and promote its applications in phase modulation and wavefront control., Comment: 21 pages, 7 figures

The Kerker effect has been generalized in nanophotonics and meta-optics, and has recently been of great interest by relating to various fascinating functionalities such as scattering management and perfect transmission, reflection or absorption. One of the most interesting generalizations is the resonant lattice Kerker effect in periodic nanostructures. However, its active tuning has not been explored yet. Here, we report, for the first time, the active control of the resonant lattice Kerker effect in periodic Ge2Se2Te5 nanodisks. By changing the crystalline fraction, we show that the electric dipole lattice resonance (ED-LR), the magnetic dipole resonance (MDR), and thus the resonant lattice Kerker effect are all red-shifted. We therefore realize the transition from the ED-LR to the resonant lattice Kerker effect, which enables multilevel tuning of reflection, transmission and absorption with modulation depths above 86%. Taking advantage of the MDR redshifts, we also observe broadband and multilevel tuning of transmission with modulation depth of 87% over a broadband range of 588 nm. Our work establishes a new path for designing high-performance active nanophotonic devices., Comment: 18 pages, 6 figures

An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks). However, it is extremely challenging to conduct fast adaptations to real-time new trends while making full use of all historical behaviors in large-scale systems, due to the real-world limitations in real-time training efficiency and external behavior acquisition. To address these practical challenges, we propose a novel Long Short-Term Temporal Meta-learning framework (LSTTM) for online recommendation. It arranges user multi-source behaviors in a global long-term graph and an internal short-term graph, and conducts different GAT-based aggregators and training strategies to learn user short-term and long-term preferences separately. To timely capture users' real-time interests, we propose a temporal meta-learning method based on MAML under an asynchronous optimization strategy for fast adaptation, which regards recommendations at different time periods as different tasks. In experiments, LSTTM achieves significant improvements on both offline and online evaluations. It has been deployed on a widely-used online recommendation system named WeChat Top Stories, affecting millions of users., Comment: 9 pages, accepted by WSDM-2022

Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale. Both micro- and macro-dynamics are the key factors to network evolution; however, how to elegantly capture both of them for temporal network embedding, especially macro-dynamics, has not yet been well studied. In this paper, we propose a novel temporal network embedding method with micro- and macro-dynamics, named $\rm{M^2DNE}$. Specifically, for micro-dynamics, we regard the establishments of edges as the occurrences of chronological events and propose a temporal attention point process to capture the formation process of network structures in a fine-grained manner. For macro-dynamics, we define a general dynamics equation parameterized with network embeddings to capture the inherent evolution pattern and impose constraints in a higher structural level on network embeddings. Mutual evolutions of micro- and macro-dynamics in a temporal network alternately affect the process of learning node embeddings. Extensive experiments on three real-world temporal networks demonstrate that $\rm{M^2DNE}$ significantly outperforms the state-of-the-arts not only in traditional tasks, e.g., network reconstruction, but also in temporal tendency-related tasks, e.g., scale prediction., Comment: CIKM2019

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

We demonstrate Fourier single-pixel imaging in the terahertz regime. The experimental system is implemented with a photo-induced coded aperture setup, where a monolayer graphene on a high-resistance silicon substrate illuminated by a coded laser beam works as a terahertz modulator. Results show that high-quality terahertz images can be reconstructed using greatly reduced number of measurements. We further find that deep photo-induced terahertz modulation by adding a monolayer graphene on the silicon substrate and by using high laser power can significantly improve the image quality. Compared to Hadamard single-pixel imaging with re-ordered Hadamard matrix, the Fourier approach has higher image quality. We expect that this work will speed up the efficiency of single-pixel terahertz imaging and advance terahertz imaging applications., Comment: 9 pages, 9 figures