Context-sensitive graph representation learning

Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce. In this paper, we propose a Multi-Semantic Aligned Graph Convolutional Network (MSAGCN), which contains two fundamental operations: multi-angle aggregation and semantic alignment, to resolve two challenges simultaneously. The core of MSAGCN is the aggregation of nodes that belong to the same class from three perspectives: nodes, features, and graph structure, and expects the obtained node features to be mapped nearby. Specifically, multi-angle aggregation is applied to extract features from three angles of the labelled nodes, and semantic alignment is utilised to align the semantics in the extracted features to enhance the similar content from different angles. In this way, the problem of over-smoothing and over-fitting for GCN can be alleviated. We perform the node clustering task on three citation datasets, and the experimental results demonstrate that our method outperforms the state-of-the-art (SOTA) baselines

Feature recommendation strategy for graph convolutional network

Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes. GCN is decisive for node classification and link prediction tasks in recent research. Although the existing GCN performs well, we argue that the current design ignores the potential features of the node. In addition, the presence of features with low correlation to nodes can likewise limit the learning ability of the model. Due to the above two problems, we propose Feature Recommendation Strategy (FRS) for Graph Convolutional Network in this paper. The core of FRS is to employ a principled approach to capture both node-to-node and node-to-feature relationships for encoding, then recommending the maximum possible features of nodes and replacing low-correlation features, and finally using GCN for learning of features. We perform a node clustering task on three citation network datasets and experimentally demonstrate that FRS can improve learning on challenging tasks relative to state-of-the-art (SOTA) baselines

Leakage-Flexible CCA-secure Public-Key Encryption: Simple Construction and Free of Pairing

In AsiaCrypt~2013, Qin and Liu proposed a new approach to CCA-security of Public-Key Encryption (PKE) in the presence of bounded key-leakage, from any universal hash proof system (due to Cramer and Shoup) and any one-time lossy filter (a simplified version of lossy algebraic filters, due to Hofheinz). They presented two instantiations under the DDH and DCR assumptions, which result in leakage rate (defined as the ratio of leakage amount to the secret-key length) of $1/2-o(1)$. In this paper, we extend their work to broader assumptions and to flexible leakage rate, more specifically to leakage rate of $1-o(1)$. \begin{itemize} \item We introduce the Refined Subgroup Indistinguishability (RSI) assumption, which is a subclass of subgroup indistinguishability assumptions, including many standard number-theoretical assumptions, like the quadratic residuosity assumption, the decisional composite residuosity assumption and the subgroup decision assumption over a group of known order defined by Boneh et al. \item We show that universal hash proof (UHP) system and one-time lossy filter (OT-LF) can be simply and efficiently constructed from the RSI assumption. Applying Qin and Liu\u27s paradigm gives simple and efficient PKE schemes under the RSI assumption. \item With the RSI assumption over a specific group (free of pairing), public parameters of UHP and OT-LF can be chosen in a flexible way, resulting in a leakage-flexible CCA-secure PKE scheme. More specifically, we get the first CCA-secure PKE with leakage rate of $1-o(1)$ without pairing. \end{itemize

(Sr3La2O5)(Zn1-xMnx)2As2: A Bulk Form Diluted Magnetic Semiconductor isostructural to the "32522" Fe-based Superconductors

A new diluted magnetic semiconductor system, (Sr3La2O5)(Zn1-xMnx)2As2, has been synthesized and characterized. 10% Mn substitution for Zn in bulk form (Sr3La2O5)Zn2As2 results in a ferromagnetic ordering below Curie temperature, TC ~ 40 K. (Sr3La2O5)(Zn1-xMnx)2As2 has a layered crystal structure identical to that of 32522-type Fe based superconductors, and represents the fifth DMS family that has a direct counterpart among the FeAs high temperature superconductor families.Comment: Accepted for publication in EP

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making

Radio Sources Segmentation and Classification with Deep Learning

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies

Emergence of synchronization induced by the interplay between two prisoner's dilemma games with volunteering in small-world networks

We studied synchronization between prisoner's dilemma games with voluntary participation in two Newman-Watts small-world networks. It was found that there are three kinds of synchronization: partial phase synchronization, total phase synchronization and complete synchronization, for varied coupling factors. Besides, two games can reach complete synchronization for the large enough coupling factor. We also discussed the effect of coupling factor on the amplitude of oscillation of $cooperator$ density.Comment: 6 pages, 4 figure