Tuning the Dielectric and Microwaves Absorption Properties of N-Doped Carbon Nanotubes by Boron Insertion.

It is of great significance to regulate the dielectric parameters and microstructure of carbon materials by elemental doping in pursuing microwave absorption (MA) materials of high performance. In this work, the surface electronic structure of N-doped CNTs was tuned by boron doping, in which the MA performance of CNTs was improved under the synergistic action of B and N atoms. The B,N-doped carbon nanotubes (B,N-CNTs) exhibited excellent MA performance, where the value of minimum reflection loss was -40.04 dB, and the efficient absorption bandwidth reached 4.9 GHz (10.5-15.4 GHz). Appropriate conductance loss and multi-polarization loss provide the main contribution to the MA of B,N-CNTs. This study provides a novel method for the design of CNTs related MA materials.

Operation-aware Neural Networks for user response prediction.

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising systems and recommendation systems are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for each operation may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment.

Locally linear SVMs based on boundary anchor points encoding.

In this paper, we propose a locally linear classifier based on boundary anchor points encoding (LLBAP) to achieve the efficiency of linear SVM and the power of kernel SVM. LLBAP partitions linearly non-separable data into approximately linearly separable parts based on boundary point scanning and local coding. Each part of data is solved by a linear SVM. Experiments on large-scale benchmark datasets demonstrate that the proposed method is more efficient than kernel SVM in both training and testing phases; its efficiency and classification accuracy also outperform other locally linear classifiers on those benchmark datasets.