Institution
Shanghai Jiao Tong University
Education•Shanghai, Shanghai, China•
About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.
Topics: Population, Cancer, Computer science, Microstructure, Medicine
Papers published on a yearly basis
Papers
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TL;DR: A deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation and achieves substantial gains over state-of-the-art deep recommendation models is proposed.
Abstract: Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.
510 citations
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TL;DR: It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity, which is the essence why the new approach is superior to the existing ones.
Abstract: Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.
509 citations
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TL;DR: Different types of distance and similarity measures for HFLTSs are investigated and developed and the satisfaction degrees for different alternatives are established and are then used to rank alternatives in multi-criteria decision making.
509 citations
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TL;DR: It is demonstrated that electrochemical exfoliation of graphite furnishes graphene sheets of high quality and that the patterned EG can serve as high-performance source/drain electrodes for organic field-effect transistors.
Abstract: Solution-processable thin layer graphene is an intriguing nanomaterial with tremendous potential for electronic applications. In this work, we demonstrate that electrochemical exfoliation of graphite furnishes graphene sheets of high quality. The electrochemically exfoliated graphene (EG) contains a high yield (>80%) of one- to three-layer graphene flakes with high C/O ratio of 12.3 and low sheet resistance (4.8 kΩ/□ for a single EG sheet). Due to the solution processability of EG, a vacuum filtration method in association with dry transfer is introduced to produce large-area and highly conductive graphene films on various substrates. Moreover, we demonstrate that the patterned EG can serve as high-performance source/drain electrodes for organic field-effect transistors.
509 citations
Authors
Showing all 158621 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
Richard A. Flavell | 231 | 1328 | 205119 |
Jie Zhang | 178 | 4857 | 221720 |
Yang Yang | 171 | 2644 | 153049 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Thomas S. Huang | 146 | 1299 | 101564 |
Barbara J. Sahakian | 145 | 612 | 69190 |
Jean-Laurent Casanova | 144 | 842 | 76173 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Weihong Tan | 140 | 892 | 67151 |
Xin Wu | 139 | 1865 | 109083 |
David Y. Graham | 138 | 1047 | 80886 |
Bin Liu | 138 | 2181 | 87085 |
Jun Chen | 136 | 1856 | 77368 |