Consumption prediction of bearing spare parts based on a hybrid model

Aiming at improving the accuracy of consumption prediction, a hybrid model was constructed, which designs an empirical wavelet filter bank to remove noise factors in original data. Besides the value prediction, the EWT-PGPR model can also give a certain credible interval, which effectively improves the practicability of the model

Photonic-assisted microwave frequency doubling based on silicon ring modulator

We experimentally demonstrate an integrated optical millimeter -wave signal generator based on a silicon ring modulator. A 20 GHz microwave signal with 17 dB suppression ratio is obtained with a 10 GHz input signal

Research on consumption prediction of spare parts based on fuzzy C-means clustering algorithm and fractional order model

In order to achieve the non-stationary de-noising signal effectively, and to solve the prediction of less sample, a hybrid model composed of FCCA (Fuzzy C-means clustering algorithm) and FOM (Fractional Order Model) was constructed. The degree of each data point was determined by FCCA to de-noise and the p order cumulative matrix was extended to r fractional cumulative matrix, so that the fractional order cumulative grey model was established to make forecasting. The results of numerical example showed that the hybrid model can obtain better prediction accuracy

An Open Receptor-Binding Cavity of Hemagglutinin-Esterase-Fusion Glycoprotein from Newly-Identified Influenza D Virus: Basis for Its Broad Cell Tropism.

Influenza viruses cause seasonal flu each year and pandemics or epidemic sporadically, posing a major threat to public health. Recently, a new influenza D virus (IDV) was isolated from pigs and cattle. Here, we reveal that the IDV utilizes 9-O-acetylated sialic acids as its receptor for virus entry. Then, we determined the crystal structures of hemagglutinin-esterase-fusion glycoprotein (HEF) of IDV both in its free form and in complex with the receptor and enzymatic substrate analogs. The IDV HEF shows an extremely similar structural fold as the human-infecting influenza C virus (ICV) HEF. However, IDV HEF has an open receptor-binding cavity to accommodate diverse extended glycan moieties. This structural difference provides an explanation for the phenomenon that the IDV has a broad cell tropism. As IDV HEF is structurally and functionally similar to ICV HEF, our findings highlight the potential threat of the virus to public health

FedGT: Federated Node Classification with Scalable Graph Transformer

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed \emph{subgraph federated learning} methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable \textbf{Fed}erated \textbf{G}raph \textbf{T}ransformer (\textbf{FedGT}) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Moreover, local differential privacy is applied to further protect the privacy of clients. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT.Comment: ICLR 24 submissio