Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space

Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolving regularities in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many real-world temporal networks, leading to sub-optimal embeddings. To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. More specially, HTGN maps the temporal graph into hyperbolic space, and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network, to capture the evolving behaviors and implicitly preserve hierarchical information simultaneously. Furthermore, in the hyperbolic space, we propose two important modules that enable HTGN to successfully model temporal networks: (1) hyperbolic temporal contextual self-attention (HTA) module to attend to historical states and (2) hyperbolic temporal consistency (HTC) module to ensure stability and generalization. Experimental results on multiple real-world datasets demonstrate the superiority of HTGN for temporal graph embedding, as it consistently outperforms competing methods by significant margins in various temporal link prediction tasks. Specifically, HTGN achieves AUC improvement up to 9.98% for link prediction and 11.4% for new link prediction. Moreover, the ablation study further validates the representational ability of hyperbolic geometry and the effectiveness of the proposed HTA and HTC modules.Comment: KDD202

Camera-aware Proxies for Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain $14.3\%$ Rank-1 and $10.2\%$ mAP improvements when compared to the second place. Code is available at: \texttt{https://github.com/Terminator8758/CAP-master}.Comment: Accepted to AAAI 2021. Code is available at: https://github.com/Terminator8758/CAP-maste

First-principle calculation of Chern number in gyrotropic photonic crystals

As an important figure of merit for characterizing the quantized collective behaviors of the wavefunction, Chern number is the topological invariant of quantum Hall insulators. Chern number also identifies the topological properties of the photonic topological insulators (PTIs), thus it is of crucial importance in PTI design. In this paper, we develop a first principle computatioal method for the Chern number of 2D gyrotropic photonic crystals (PCs), starting from the Maxwell's equations. Firstly, we solve the Hermitian generalized eigenvalue equation reformulated from the Maxwell's equations by using the full-wave finite-difference frequency-domain (FDFD) method. Then the Chern number is obtained by calculating the integral of Berry curvature over the first Brillouin zone. Numerical examples of both transverse-electric (TE) and transverse-magnetic (TM) modes are demonstrated, where convergent Chern numbers can be obtained using rather coarse grids, thus validating the efficiency and accuracy of the proposed method.Comment: 13 pages, 6 figure

Effective Lifetime of Non-Equilibrium Carriers in Semiconductors from Non-Adiabatic Molecular Dynamics Simulations

The lifetime of non-equilibrium electrons and holes in semiconductors is crucial for solar cell and optoelectronic applications. Non-adiabatic molecular dynamics (NAMD) simulations based on time-dependent density functional theory (TDDFT) are widely used to study excited-state carrier dynamics. However, the calculated carrier lifetimes are often different from experimental results by orders of magnitude. In this work, by revisiting the definition of carrier lifetime and considering different recombination mechanisms, we report a systematic procedure for calculating the effective carrier lifetime in realistic semiconductor crystals that can be compared directly to experimental measurements. The procedure shows that considering all recombination mechanisms and using reasonable densities of carriers and defects are crucial in calculating the effective lifetime. When NAMD simulations consider only Shockey-Read-Hall (SRH) defect-assisted and band-to-band non-radiative recombination while neglect band-to-band radiative recombination, and the densities of non-equilibrium carriers and defects in supercell simulations are much higher than those in realistic semiconductors under solar illumination, the calculated lifetimes are ineffective and thus differ from experiments. Using our procedure, the calculated effective lifetime of the halide perovskite CH3NH3PbI3 agrees with experiments. It is mainly determined by band-to-band radiative and defect-assisted non-radiative recombination, while band-to-band non-radiative recombination is negligible. These results indicate that it is possible to calculate carrier lifetimes accurately based on NAMD simulations, but the directly calculated values should be converted to effective lifetimes for comparison to experiments. The revised procedure can be widely applied in future carrier lifetime simulations.Comment: 30 pages, 5 figure

A Reflecting/Absorbing Dual-Mode Textile Metasurface Design

A textile-based reflecting/absorbing dual-mode metasurface is proposed in this paper. For the reflecting mode of the design, a conventional square patch electromagnetic band gap (EBG) structure is adopted and the zero-degree refection phase center is tuned to 2.4 GHz. For the absorbing mode, a carbon-coated resistive net is applied on top of the EBG patches to redirect the current flow at resonance and hence achieve energy dissipation with the resistance. The underlying reconfigurable logic is analyzed with a dispersion diagram, surface current distribution, and equivalent circuit/impedance matching analysis. By applying a state-of-the-art AI-driven antenna design technique, self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA) method, the geometry parameters can be accurately determined meanwhile maintaining absorption and reflection band of the design centered at the same frequency. The fabricated prototype of the design can achieve a maximal absorption of 99.8% (-29.2 dB) and maintain an absorption over 90% in the frequency range of 2.39 to 2.42 GHz. To verify the reflection properties, a textile monopole antenna was fabricated and tested along with the reflection metasurface. A 5-dB realized gain enhancement can be achieved at 2.4 GHz with the applied metasurface. Both simulations and measurements verify the effectiveness of the proposed dual-mode metasurface design