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Understanding the emergence of collective emotions is critical to the analysis of online and offline societies. The agent-based simulation community has developed various social norm models to see the polarization of collective emotions. Yet, a few models have psychological background as fundamentals , as well as statistical validation, and this paper aims(More)
Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts(More)
A series of events generates multiple types of time series data, such as numeric and text data over time, and the variations of the data types capture the events from different angles. This paper aims to integrate the analyses on such numerical and text time-series data influenced by common events with a single model to better understand the events.(More)
Identifying the prescription patterns would be a useful and interesting goal from multiple perspectives. Firstly, the identified patterns could expand the horizon of the medical practice knowledge. Secondly, the identified prescription patterns can be evaluated by subject-matter experts to label some of the patterns as anomaly calling for further(More)
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