Hetero2^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin

Trait Mindfulness and Problematic Smartphone Use in Chinese Early Adolescent: The Multiple Mediating Roles of Negative Affectivity and Fear of Missing Out

This study used a cross-sectional study design to investigate whether the mindfulness trait was a protective factor against problematic smartphone use (PSPU) of early adolescents, and whether negative affectivity and fear of missing out (FoMO) mediated this relationship. The study selected a sample of middle school students (N = 517, 46.03% males, Mage = 13.81, SD = 1.40) in China. The results of the structural equation modelling indicated that (a) mindfulness significantly and negatively predicted PSPU, (b) FoMO played a mediating role between mindfulness and PSPU, (c) negative affectivity (including depression and anxiety) played a mediating role between mindfulness and PSPU, but loneliness did not, and (d) negative affectivity and FoMO played a chain-mediated role, and depression, anxiety, and loneliness played a chain-mediated role with FoMO between mindfulness and PSPU. We discuss the possibility that high levels of mindfulness in early adolescents may reduce the short-term effects of problematic smartphone use by reducing negative emotions and FoMO and relate our results to an emphasis on the role of enhanced mindfulness in long-term internal self-regulation and well-being. Findings have implications for individuals and schools for PSPU prevention and intervention

Similarity Measure Based on Partial Information of Time Series

Similarity measure of time series is an important subroutine in many KDD applications. Previous similarity models mainly focus on the prominent series behaviors by considering the whole information of time series. In this paper, we address the problem: which portion of information is more suitable for similarity measure for the data collected from a certain field. We propose a model for the retrieval and representation of the partial information in time series data, and a methodology for evaluating the similarity measurements based on partial information. The methodology is to retrieve various portions of information from the raw data and represent it in a concise form, then cluster the time series using the partial information and evaluate the similarity measurements through comparing the results with a standard classification. Experiments on data set from stock market give some interesting observations and justify the usefulness of our approach. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications- dat

Automatic diagnosis and real-time monitoring software for train control systems

Train control systems consist of supervisory controller, network, and multiple trains. Actual trains are controlled and supervised by computer based signals, which put this system in the paradigm of CPS (Cyber-Physical Systems). Recent trends in train control systems are adopting wireless communication protocols to improve operational efficiency. While efficiency is improved by overcoming legacy fixed block based operation, wireless communication channels open new surfaces for external attack and malfunction. Actual attacks on CPS indeed have been materialized in trains, and as a result, attention from academia and industry for resiliency for train control systems are increasing. This manuscript overviews the trend and latest effort to this regard and introduces status of train control software that enhances the aspect of automatic diagnosis and real-time monitoring features. © ICROS 202.1

The complete mitochondrial genome of the edible mushroom Grifola frondosa

The culinary-medicinal mushroom Grifola frondosa is widely cultivated in East Asia. In this study, the complete mitochondrial genome of G. frondosa was determined using Illumina sequencing. The circular molecule was 197,486 bp in length with a content of 25.01% GC, which was one of the largest mitochondrial genomes in the order Polyporales. A total of 39 known genes encoding 13 common mitochondrial genes, 24 tRNA genes, 1 ribosomal protein s3 gene (rps3), and 1 DNA polymerase gene (dpo) were predicted in this genome. The phylogenetic analysis showed that G. frondosa clustered together with Sparassis crispa, Laetiporus sulphureus, Wolfiporia cocos, and Taiwanofungus camphoratus. The complete mitochondrial genome reported here may provide new insight into genetic information and evolution for further studies