Web of scholars : a scholar knowledge graph

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM

eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation

Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover, there is no method currently built for an intrinsically interpretable transformer, which is able to explain its reasoning process and provide a faithful explanation. To close these crucial gaps, we propose a novel vision transformer dubbed the eXplainable Vision Transformer (eX-ViT), an intrinsically interpretable transformer model that is able to jointly discover robust interpretable features and perform the prediction. Specifically, eX-ViT is composed of the Explainable Multi-Head Attention (E-MHA) module, the Attribute-guided Explainer (AttE) module and the self-supervised attribute-guided loss. The E-MHA tailors explainable attention weights that are able to learn semantically interpretable representations from local patches in terms of model decisions with noise robustness. Meanwhile, AttE is proposed to encode discriminative attribute features for the target object through diverse attribute discovery, which constitutes faithful evidence for the model's predictions. In addition, a self-supervised attribute-guided loss is developed for our eX-ViT, which aims at learning enhanced representations through the attribute discriminability mechanism and attribute diversity mechanism, to localize diverse and discriminative attributes and generate more robust explanations. As a result, we can uncover faithful and robust interpretations with diverse attributes through the proposed eX-ViT

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values. In this work, we introduce a novel framework called GST-Pro, which utilizes a graph spatiotemporal process and anomaly scorer to tackle the aforementioned challenges in detecting anomalies on irregularly-sampled multivariate time series. Our approach comprises two main components. First, we propose a graph spatiotemporal process based on neural controlled differential equations. This process enables effective modeling of multivariate time series from both spatial and temporal perspectives, even when the data contains missing values. Second, we present a novel distribution-based anomaly scoring mechanism that alleviates the reliance on complete uniform observations. By analyzing the predictions of the graph spatiotemporal process, our approach allows anomalies to be easily detected. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods, regardless of whether there are missing values present in the data. Our code is available: https://github.com/huankoh/GST-Pro.Comment: Accepted by Information Fusio

MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights. © 2023 Association for Computing Machinery

Decaffeinated green tea extract as a nature-derived antibiotic alternative: An application in antibacterial nano-thin coating on medical implants

Plant-derived polyphenols have emerged as molecular building blocks for biomedical architectures. However, the isolation of polyphenols from other components requires labor-intensive procedures, which increases costs and often raises environmental concerns. Here, we suggest that decaffeination can be a convenient and cost-effective method for enhancing the antibacterial performance of polyphenol-rich tea extracts. As a demonstration, we compared the properties of a nano-thin coating made of decaffeinated (dGT coating) and raw green tea extract (GT coating). The dGT coating exhibited enhanced antibacterial performance with regard to bacterial killing and prevention of bacterial attachment compared with the GT coating. Moreover, the chemical reactivity of the dGT coating was further utilized for secondary modifications, which enhanced the overall antibacterial performance of the modified surface. Given its intrinsic low toxicity, we envision that the developed antibacterial coating is ready for the next steps toward application in real clinical settings. © 2022 Elsevier LtdFALS

IL4 Receptor-Targeted Proapoptotic Peptide Blocks Tumor Growth and Metastasis by Enhancing Antitumor Immunity

Cellular cross-talk between tumors and M2-polarized tumor-associated macrophages (TAM) favors tumor progression. Upregulation of IL4 receptor (IL4R) is observed in diverse tumors and TAMs. We tested whether an IL4R-targeted proapoptotic peptide could inhibit tumor progression. The IL4R-binding peptide (IL4RPep-1) preferentially bound to IL4R-expressing tumor cells and M2-polarized macrophages both in vitro and in 4T1 breast tumors in vivo. To selectively kill IL4R-expressing cells, we designed an IL4R-targeted proapoptotic peptide, IL4RPep-1-K, by adding the proapoptotic peptide (KLAKLAK)(2) to the end of IL4RPep-1. IL4RPep-1-K exerted selective cytotoxicity against diverse IL4-Rexpressing tumor cells and M2-polarized macrophages. Systemic administration of IL4RPep-1-K inhibited tumor growth and metastasis in 4T1 breast tumor-bearing mice. Interestingly, IL4RPep-1-K treatment increased the number of activated cytotoxic CD8(+) T cells while reducing the numbers of immunosuppressive regulatory T cells and M2-polarized TAMs. No significant systemic side effects were observed. These results suggest that IL4R-targeted proapoptotic peptide has potential for treating diverse IL4-Rexpressing cancers. (C) 2017 AACR.114sciescopu