Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing

In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer from serious damage when the collected node features or structure relationships are partially missing owning to numerous unpredictable factors. Recently emerged graph completion learning (GCL) has received increasing attention, which aims to reconstruct the missing node features or structure relationships under the guidance of a specifically supervised task. Although these proposed GCL methods have made great success, they still exist the following problems: the reliance on labels, the bias of the reconstructed node features and structure relationships. Besides, the generalization ability of the existing GCL still faces a huge challenge when both collected node features and structure relationships are partially missing at the same time. To solve the above issues, we propose a more general GCL framework with the aid of self-supervised learning for improving the task performance of the existing GNN variants on graphs with features and structure missing, termed unsupervised GCL (UGCL). Specifically, to avoid the mismatch between missing node features and structure during the message-passing process of GNN, we separate the feature reconstruction and structure reconstruction and design its personalized model in turn. Then, a dual contrastive loss on the structure level and feature level is introduced to maximize the mutual information of node representations from feature reconstructing and structure reconstructing paths for providing more supervision signals. Finally, the reconstructed node features and structure can be applied to the downstream node classification task. Extensive experiments on eight datasets, three GNN variants and five missing rates demonstrate the effectiveness of our proposed method.Comment: Accepted by 23rd IEEE International Conference on Data Mining (ICDM 2023

Preparation and Properties of Mo Coating on H13 Steel by Electro Spark Deposition Process

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-29, pub-electronic 2021-07-01Publication status: PublishedFunder: Jilin Science and Technology Development Project; Grant(s): 20200401034GX, 2020C029-1Funder: Fundamental Research Funds for the Central Universities; Grant(s): 45120031B094H13 steel is often damaged by wear, erosion, and thermal fatigue. It is one of the essential methods to improve the service life of H13 steel by preparing a coating on it. Due to the advantages of high melting point, good wear, and corrosion resistance of Mo, Mo coating was fabricated on H13 steel by electro spark deposition (ESD) process in this study. The influences of the depositing parameters (deposition power, discharge frequency, and specific deposition time) on the roughness of the coating, thickness, and properties were investigated in detail. The optimized depositing parameters were obtained by comparing roughness, thickness, and crack performance of the coating. The results show that the cross-section of the coating mainly consisted of strengthening zone and transition zone. Metallurgical bonding was formed between the coating and substrate. The Mo coating mainly consisted of Fe9.7Mo0.3, Fe-Cr, FeMo, and Fe2Mo cemented carbide phases, and an amorphous phase. The Mo coating had better microhardness, wear, and corrosion resistance than substrate, which could significantly improve the service life of the H13 steel

Controlling remote instruments using Web services for online experiment systems

Online experimentation allows students from anywhere to operate remote instruments at any time. This promising e-learning application is well positioned to use Web Services to conduct online experiment systems due to its interoperability and Internet compliance. We present a double client-server architecture for online experiment systems and the methodology to wrap the functions of instruments into Web Services. We propose that the instrument Web Services should be stateful services and we present the framework to manage the states of the instrument web services. We benchmark the performance of this system when using SOAP as the wire format for communication and propose solutions to optimize performance

Design Instrumental Web Services for Online Experiment Systems

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Microstructure and mechanical properties of ZrC modified Ni60 hard-facing alloy fabricated by laser metal deposition

Crack-free specimens were prepared from ZrC-modified Ni60 alloy by laser metal deposition. The effects of ZrC powder adulteration on the mechanism of microstructure refinement were investigated. The results demonstrated that the adulteration of ZrC powder did not provide nucleation sites for the hardening phase. The ZrC powder was added to Ni60 alloy powder to reduce the content of columnar dendrites and the eutectics of laser metal deposition specimens. The transformation of the dendritic hardening phase into a massive hardening phase was induced. The fine Ni–B–Si eutectic microstructure changed to a massive NiZr eutectic and peritectic microstructure with increasing ZrC powder mass fraction. The microstructure transformation mechanism constrained the initiation and propagation of cracks in the deposited specimens. The present research provides a method for improving the cracking defect of laser additive manufacturing Ni–Cr–B–Si system alloys and a theoretical basis for promoting the application of the alloy in additive manufacturing

Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

Abstract Recently, transformer‐based networks have been introduced for the classification of hyperspectral image (HSI). Although transformer‐based methods can well capture spectral sequence information, their ability to fuse different types of information contained in HSI is still insufficient. To exploit rich spectral, spatial and semantic information in HSI, a novel semantic and spatial‐spectral feature fusion transformer (S3FFT) network is proposed in this study. In the proposed S3FFT method, spatial attention and efficient channel attention (ECA) modules are employed for the extraction of shallow spatial‐spectral features. Then, a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature extraction. Finally, the semantic, spatial and spectral features are combined by the transformer for classification. Compared with traditional deep learning methods and recently transformer‐based methods, the proposed S3FFT shows relatively better results on three HSI datasets