Knowledge Navigation: Inferring the Interlocking Map of Knowledge from Research Trajectories

"If I have seen further, it is by standing on the shoulders of giants," Isaac Newton's renowned statement hints that new knowledge builds upon existing foundations, which means there exists an interdependent relationship between knowledge, which, yet uncovered, is implied in the historical development of scientific systems for hundreds of years. By leveraging natural language processing techniques, this study introduces an innovative embedding scheme designed to infer the "knowledge interlocking map." This map, derived from the research trajectories of millions of scholars, reveals the intricate connections among knowledge. We validate that the inferred map effectively delineates disciplinary boundaries and captures the intricate relationships between diverse concepts. The utility of the interlocking map is showcased through multiple applications. Firstly, we demonstrated the multi-step analogy inferences within the knowledge space and the functional connectivity between concepts in different disciplines. Secondly, we trace the evolution of knowledge across domains, observing trends such as shifts from "Theoretical" to "Applied" or "Chemistry" to "Biomedical" along predefined functional directions. Lastly, by analyzing the high-dimensional knowledge network structure, we found that knowledge connects each other with shorter global pathways, and the interdisciplinary knowledge plays a critical role in accessibility of the global knowledge network. Our framework offers a novel approach to mining knowledge inheritance pathways in extensive scientific literature, which is of great significance for understanding scientific development patterns, tailoring scientific learning trajectories, and accelerating scientific progress.

Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders

Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE.MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning. For anomaly detection, we accomplish anomaly removal by replacing the original encoded image features at each scale with most relevant prototype features,and fuse these features before feeding to the decoding module to reconstruct image. Experimental results on various datasets testify that our MMAE successfully removes anomalies at different scales and performs favorably on several datasets compared to similar reconstruction-based methods.