1,721,249 research outputs found
Robust control of systems with sharp resonances
No full textIn this paper, a general framework to design robust controllers for systems with uncertain sharp resonances in the control bandwidth is presented. This problem typically arises in high performance mechanical systems with flexible parts
Brand community analysis on social networks using graph representation learning
No full textIn a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users. We exploit the network that builds around the brands by encoding it into a graph model. We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks
Concentration and ROC Curves, Revisited
Full text linkThis work is aimed at illustrating the strict relationship between a general definition of concentration function appeared quite some time ago on this journal and a widely used measure of the diagnostic strength of a family of binary classifiers indexed by a threshold parameter, the so-called ROC curve. The ROC curve is a common work tool in Statistics, Machine Learning and Artificial Intelligence, appearing in many applications where a binary classification (diagnosis) procedure is of interest. Hence, it is worth remarking that diagnostic strength and concentration are two sides of the same coin: the higher the concentration of one probability measure with respect to another, the higher the diagnostic strength of the likelihood ratio classification rule
On the definition of a concentration function relevant to the ROC curve
Full text linkThis work provides a definition of concentration curve alternative to the one presented on this journal by Schechtman and Schechtman (Metron 77:171–178, 2019). Our definition clarifies, at the population level, the relationship between concentration and the omnipresent ROC curve in diagnostic and classification problems
Erratum: A curve-free method for phase I clinical trials (Biometrics (JUNE 2000) 56 (609-615))
No full textNapoli "porta al paradiso terrestre" nel racconto di un viaggiatore tedesco nell'età romantica: Karl August Mayer
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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