2,563 research outputs found
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 ”m long Bragg grating was engraved in an ~5 ”m 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
NâČ-Ferrocenyl-2-hydroxyÂbenzohydrazide
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 molÂecule allows the formation of an intraÂmolecular NâHâŻO hydrogen bond involving the hydroxyl group. The CONHNHCO unit and the rings bonded to it are nearly coplanar. The crystal structure is stabilized by interÂmolecular OâHâŻO(carbonÂyl) and NâHâŻO(carbonÂyl) 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
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