Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities

The composition of author teams is an important factor influencing the novelty of academic papers. However, existing studies have paid limited attention to the role of institutional composition, and most novelty measures remain at a general level, making it difficult to explain the specific sources and types of novelty in papers. Taking the field of natural language processing as an example, this study investigates the relationship between team institutional composition and the fine-grained novelty of academic papers. Author teams are classified into three types: academic institutions, industrial institutions, and mixed academic and industrial institutions. Four types of fine-grained knowledge entities are extracted from full-text papers, including methods, datasets, tools, and metrics. The novelty of papers is then measured based on entity combinations, and pairwise combinations of different entity types are further analyzed to examine their contributions to novel papers. The results show that, in the field of natural language processing, collaboration between industrial and academic institutions is more likely to produce novel papers than purely industrial collaboration. From the perspective of fine-grained knowledge entities, mixed academic and industrial teams pay more attention to the novelty of method-metric combinations, whereas industrial teams pay more attention to the novelty of method-tool combinations. This study reveals the relationship between institutional team composition and paper novelty through fine-grained novelty measurement, providing useful evidence for improving paper quality and promoting industry-academia-research collaboration.

The Catastrophic Failure of The k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It

Lloyd's k-means algorithm is one of the most widely used clustering methods. We prove that in high-dimensional, high-noise settings, the algorithm exhibits catastrophic failure: with high probability, essentially every partition of the data is a fixed point. Consequently, Lloyd's algorithm simply returns its initial partition - even when the underlying clusters are trivially recoverable by other methods. In contrast, we prove that Hartigan's k-means algorithm does not exhibit this pathology. Our results show the stark difference between these algorithms and offer a theoretical explanation for the empirical difficulties often observed with k-means in high dimensions.