Browsing by Author "Liu, Bei"

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Role of Depolarization Factors in the Evolution of a Dipolar Plasmonic Spectral Line in the Far- and Near-Field Regimes
(2020-02-06)
Januar, Mochamad
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Liu, Bei
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Cheng, Jui-Ching
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Hatanaka, Koji
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Misawa, Hiroaki
In this article, the excitation of dipolar localized surface plasmon resonances (LSPRs) in both the far- and near-field regions is described in terms of the relevant static, dynamic, and radiative depolarization factors. This approach offers a direct relationship between the evolution of the LSPR spectral line and the depolarization components in an analogous sense to a harmonic oscillator. The static, dynamic, and radiative terms reflect the coefficients of the "stiffness", effective mass, and damping in the oscillator system, respectively. Hence, one can immediately perceive that the static part of the depolarization factor is mainly responsible for the shifts in the resonant frequency, and the radiative part is responsible for the change in bandwidth. Additionally, the dynamic part behaves like an effective mass, acting as an inertial weighting factor that decides how significant the changes taking place in the system are. From this model, we can rationalize that the qualitative behavior of the far-field efficiency primarily depends on the shifting resonant frequencies, and the corresponding near-field efficiency is highly sensitive to the presence of damping. The model also clarifies the discrepancy in the resonant frequency and bandwidth between the far- and near-field spectra, which is due to the significant presence of the radiative component. These basic descriptions can be used as a guiding principle for handling more sophisticated structures and gaining more rationalized designs for novel applications related to the LSPR mechanism.
SMP Challenge: An Overview of Social Media Prediction Challenge 2019
(2019-01-01)
Wu, Bo
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Cheng, Wen-Huang
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Liu, Peiye
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Liu, Bei
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Zeng, Zhaoyang
"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic decisions for the future, but also explores advanced predictive learning and analytic methods on various problems and scenarios, such as multimedia recommendation, advertising system, fashion analysis etc. In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.) of new online posts in social media feeds before uploading. We also collected and released a large-scale SMPD benchmark with over 480K posts from 69K users. In this paper, we define the challenge problem, give an overview of the dataset, present statistics of rich information for data and annotation and design the accuracy and correlation evaluation metrics for temporal popularity prediction to the challenge.