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

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

Motif Analysis in Internet of the Things Platform for Wind Turbine Maintenance Management

Wind energy is one of the most competitive renewable energy sources. Supervisory control and data acquisition system provides alarm activations in case of failure, and also signals of the system. Due to the volume and different type of data, these systems require advanced analytics to ensure a suitable maintenance management. Several methods are employed, mainly based in artificial intelligence that involve advanced trainings and elevated computational costs with high possibilities to detect false positives. The novelty proposed in this work is based on motif analysis using an Internet of the Things platform to analyze large time series data for wind turbine monitoring. It is presented an approach considering personalized motifs in specific periods of the signal dataset with more influence in the alarm activation. A real case study is presented analyzing periods before historical alarm activation to forecast relevant trends in time series data. The results obtained with the proposed method provide high accuracy, where this information can be implanted in the maintenance management plan.Sin financiaciónNo data WoSNo data Scopus670 SPI - ICEE (2020), 4/96 General - Editoriales extranjerasUE