With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.

Circular dichroism (CD) studies on single nanostructures can yield novel insights into chiroptical physics that are not available from traditional ensemble-based measurements, yet they are challenging because of their weak signals. By introducing an oblique excitation beam, we demonstrate the observation and spectroscopic analysis of a prominent plasmonic chiroptical response from a single v-shaped gold nanorod dimer nanostructure. We show that circular differential scattering from the obliquely excited gold nanorod dimer yields a characteristic bisignate peak-dip spectral shape at hybridized energies of the dimer. This chiroptical response can be ascribed to extrinsic chirality which depends on the geometry configurations of the chiral arrangement. Due to strong near-field coupling, the dipole orientations of the hybridized resonance modes can be in favor of the incident circularly polarized light where a maximum g-factor of ∼0.4 is observed. Promising applications of this chiroptical arrangement as a key component can be in electronics, photonics, or metamaterials.

Multilingual knowledge graphs constructed by entity alignment are the indispensable resources for numerous AI-related applications. Most existing entity alignment methods only use the triplet-based knowledge to find the aligned entities across multilingual knowledge graphs, they usually ignore the neighborhood subgraph knowledge of entities that implies more richer alignment information for aligning entities. In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs. NAEA devises an attention mechanism to learn neighbor-level representation by aggregating neighbors' representations with a weighted combination. The attention mechanism enables entities not only capture different impacts of their neighbors on themselves, but also attend over their neighbors' feature representations with different importance. We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models.