663 research outputs found
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 Rank-1
and 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
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