Virtual Relational Knowledge Graphs for Recommendation

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods

Human performance analysis of processes for retrieving Beidou satellite navigation system during breakdown

Satellite navigation systems provide continuous, timely, and accurate signals of location, speed, and time to users all over the world. Although the running of these systems has become highly automated, the human operator is still vital for its continued operation, especially when certain equipment failures occur. In this paper, we examined 180 incidents of one particular type of equipment failure and the whole recovery process as recorded in the log files from a ground control center of the Beidou satellite navigation system. We extracted the information, including the technical description of the failure, the time when the fault occurred, the full recovery time, and the demographic information of the team members on the shift responsible for responding to the failure. We then transformed these information into the cognitive complexity of the task, time of day, shift handover period, and team skill composition. Multiple regression analysis showed that task complexity and shift handover were key predictors of recovery time. Time of day also influenced the recovery time, during midnight to 4 a.m., operators made longer responses. We also found that the fault handling processes could be improved if the team’s most adept member is more skillful at that role than in other teams. We discussed the theoretical and practical implication of this study

LKCA: Large Kernel Convolutional Attention

We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it with a single large kernel convolution. LKCA combines the advantages of convolutional neural networks and visual transformers, possessing a large receptive field, locality, and parameter sharing. We explained the superiority of LKCA from both convolution and attention perspectives, providing equivalent code implementations for each view. Experiments confirm that LKCA implemented from both the convolutional and attention perspectives exhibit equivalent performance. We extensively experimented with the LKCA variant of ViT in both classification and segmentation tasks. The experiments demonstrated that LKCA exhibits competitive performance in visual tasks. Our code will be made publicly available at https://github.com/CatworldLee/LKCA

Blockchain-Based Distributed Energy Trading in Energy Internet: An SDN Approach

The new network paradigm of Software Defined Networking (SDN) has been widely adopted. Due to its inherent advantages, SDN has been widely used in various network fields such as data centers, WAN, enterprise, Optical Networks and energy Internet. Among them, SDN-based energy Internet systems are receiving more and more attention. But at the same time, some problems and challenges are gradually becoming more prominent. The SDN-based energy Internet is a distributed architecture for renewable energy, so the traditional centralized electric energy trading model will no longer apply. The blockchain has been rapidly developed and applied in various domains by virtue of its decentralization, coordinated autonomy, and non-tamperability. We propose an SDN-based energy Internet distributed energy trading scheme supported by blockchain technology. The proposed scheme achieves a reasonable match of the transaction objects under the premise of protecting privacy. Finally, we conducted a comprehensive, systematic security and applicability analysis of the proposed solution, further confirming that the system meets our design goals. - 2013 IEEE.This work was supported in part by the Beijing Natural Science Foundation under Grant 4182060, in part by the National Natural Science Foundation of China under Grant 61972148, and in part by the Fundamental Research Funds for the Central Universities under Grant 2019MS020.Scopu

A CD8 T Cell/Indoleamine 2,3-Dioxygenase Axis Is Required for Mesenchymal Stem Cell Suppression of Human Systemic Lupus Erythematosus

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Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT

Objective: The purpose is to establish and validate a machine-learning-derived radiomics approach to deter-mine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. Methods: Patients with AF based on electrocardio-graphic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. Results: In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI: 0.84–1.00), higher than 0.71 (0.58–0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong’s p = 0.01), and higher than 0.73 (0.61–0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77–0.92), higher than that of the CT attenuation model (p 0.05). Conclusion: EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. Advances in knowledge: A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT