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  1. null (Ed.)
    While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. 
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  2. In this paper, we theoretically demonstrate a dual-band independently tunable absorber consisting of a stacked graphene nanodisk and graphene layer with nanohole structure, and a metal reflector spaced by insulator layers. This structure exhibits a dipole resonance mode in graphene nanodisks and a quadrupole resonance mode in the graphene layer with nanoholes, which results in the enhancement of absorption over a wide range of incident angles for both TE and TM polarizations. The peak absorption wavelength is analyzed in detail for different geometrical parameters and the Fermi energy levels of graphene. The results show that both peaks of the absorber can be tuned dynamically and simultaneously by varying the Fermi energy level of graphene nanodisks and graphene layer with nanoholes structure. In addition, one can also independently tune each resonant frequency by only changing the Fermi energy level of one graphene layer. Such a device could be used as a chemical sensor, detector or multi-band absorber. 
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