MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201

An optical fiber tip micrograting thermometer

An ~12 µm long Bragg grating was engraved in an ~5 µm diameter optical fiber tip by focused ion beam (FIB) milling. An ~10-dB extinction was achieved at 1570 nm with only 11 indentations. The grating was used for temperature sensing, and it exhibited a temperature sensitivity of ~22 pm/°C

S-KMN: Integrating Semantic Features Learning and Knowledge Mapping Network for Automatic Quiz Question Annotation

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users' sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users' mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension \& number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling and similarity-based sampling, to speed up the training process and obtain optimal recommenders. In addition, we design two novel approaches to generate more effective reference datasets while protecting users' privacy

N′-Ferrocenyl-2-hydroxy­benzohydrazide

The title complex, [Fe(C5H5)(C13H11N2O3)], was prepared via self-assembly using ferrocenyl hydrazide and ethyl salicylate. The compound is potentially a tridentate ferrocene-based ligand. The conformation of the mol­ecule allows the formation of an intra­molecular N—H⋯O hydrogen bond involving the hydroxyl group. The CONHNHCO unit and the rings bonded to it are nearly coplanar. The crystal structure is stabilized by inter­molecular O—H⋯O(carbon­yl) and N—H⋯O(carbon­yl) hydrogen bonds

RF-thermal-structural-RF coupled analysis on the travelling wave disk-loaded accelerating structure

Travelling wave (TW) disk-loaded accelerating structure is one of the key components in normal conducting (NC) linear accelerators, and has been studied for many years. In the design process, usually after the dimensions of each cell and the two couplers are finalized, the structure is fabricated and tuned, and then the whole structure characteristics can be measured by the vector network analyzer. Before the structure fabrication, the whole structure characteristics are less simulated limited by the available computer capability. In this paper, we described the method to do the RF-thermal-structural-RF coupled analysis on the TW disk-loaded structure with one single PC. In order to validate our method, we first analyzed and compared our RF simulation results on the 3m long BEPCII structure with the corresponding experimental results, which shows very good consistency. Finally, the RF-thermal-structure-RF coupled analysis results on the 1.35m long NSC KIPT linac accelerating structure are presented.Comment: 5 pages, 16 figures, Submitted to the Chinese Physics C (Formerly High Energy Physics and Nuclear Physics