Ying Fu

TL;DR: This study identifies inflammatory cancer-associated fibroblasts (iCAFs) as a key component of lung adenocarcinoma's tumor microenvironment, developing a prognostic signature (ICAFBS) that predicts clinical outcome, immune landscape, and treatment response in lung adenocarcinoma patients.

TL;DR: In this paper, a semi-supervised probabilistic latent semantic analysis based software change log classification method is proposed, in which a word dictionary determined through prior knowledge is combined, and classification is performed on software change logs objectively according to Probabilistic dependencies between words, probabilistically dependencies between the words and change log categories, and accordingly the classification on the software changes logs according to weight values of the word frequency characteristics is avoided, the accuracy of the classification can be improved, and the problems that errors are produced and the accuracy is low in the process of the classifying

TL;DR: Wang et al. as mentioned in this paper proposed Drain+ based on a state-of-the-art log parser Drain, which includes a statistical-based separators generation component, which generates separators automatically for log message splitting, and a candidate event template merging component which merges the candidate event templates by a template similarity method.

TL;DR: A comprehensively empirical study to investigate the impact of six state-of-the-art log parsers belonging to four categories (including heuristic-based, frequency- based, clustering-based; and subsequence-based) on six state of theart log-based anomaly detection methods (including machine-learning-based and deep- learning-based methods).