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POSTSUBSCRIPT by combining the scree plot, goodness-of-fit, and evaluation of output communities (details are in Section D.4 of the supplement). Details about the calculations are available in Suppl. Today, probably the most popular sandals are flip-flops. First, it is debatable what are primary areas and representative sub-areas in statistics. Natural-inspired traction - strategically placed directional lugs in key areas for improved traction and grip during the swing, resulting in more grip and strength. It also helps understand several important problems in social science and science: characterizing research evolvement, predicting emerging communities and significant advancement in each research area, checking whether the development of different areas is balanced, and identifying unknown biases in publications. Communication is an essential component in any collaboration for the co-regulation between the collaborators and helps the AI agent make decisions in a creative process (Bown et al., 2020). Communication is critical for achieving understanding and coordination between collaborators. Whereas asynchronous cooperation does not require simultaneous interaction of all collaborators (Rodden and Blair, 1991; Reinhard et al., 1994; Cacciagrano and Corradini, 2001). In CSCW, the distinction between synchronous and asynchronous interaction is information exchange in terms of time

So instead of focusing on the mixed-memberships as in Section 2, we focus on the problem of recursive community detection: We think that the co-authorship network has many communities (each is a research sub-area in statistics), and the sub-areas may have a tree structure. Be sure you have access to a full-length mirror. We present a new data set about the publications of statisticians, collected and cleaned by ourselves with enormous efforts. In this paper, we focus on two topics: (Custom air force 1 high Hiking Shoes) How to use the citation data to estimate the research interests of statisticians, and (2) How to use the coauthorship data to study the network topology of statisticians. We present some ethical questions and issues in the context of co-creative AI based on the user study results and the literature. In summary, our findings are the combined results of (a) a large-scale high-quality data set we collected, (b) many new approaches we developed, and (c) many new ideas and substantial efforts in data analysis

The citation counts in our data set provide a valuable resource to study the research interests. The ablation study also shows the effectiveness of the proposed co-attention mechanism. We create a template where we showcase how to use modern statistical tools to study a vast volume of publication data. Our idea is to first use the co-citation relationships to construct a citee network and then model the research interests of the author as the mixed-memberships he/she has over different network communities. For example, in Section 2, we discover that ignoring some "old" citations makes the constructed citee network more useful. From a statistical standpoint, most existing quantitative approaches are overly simple, using preliminary metrics (e.g., counts of papers or citations) for analysis. We observe that two authors being frequently cited together in the same papers (i.e., co-cited) indicates that their works are scientifically related and that they share some common research interests. K research papers published in 36363636 representative journals in statistics, probability, machine learning, and related fields, spanning 41414141 years. More information of these journals is presented in Table B.1 of the supplement. Compared to the citee networks, the effect of mixed-memberships in co-authorship networks is notably less significant; see Section D.5 of the supplement for detailed discussion

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