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No abstract

Kawasaki disease (KD) is an acute systemic vasculitis that is currently the leading cause of acquired heart disease in childhood in the United States. Cardiovascular complications are the major cause of morbidity, are responsible for virtually all deaths from KD and should be evaluated as soon as possible after the acute phase to establish the baseline status, in order to predict disease progression and determine adequate treatment. In selected patients, electrocardiography (ECG)-gated cardiac computed tomography (CT) and magnetic resonance (MR) imaging are valuable non-invasive techniques that can be used to help diagnose the cardiovascular complications associated with KD. In this article, we review the epidemiology, aetiology and pathogenesis, histopathology, clinical features, cardiovascular complications and imaging, focusing on the role of cardiac CT and MR on the initial assessment and follow-up of the cardiovascular complications of KD.

Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection.

No abstract