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OBJECTIVE To summarize the prevalence of retinal vein occlusion (RVO) from studies in the United States, Europe, Asia, and Australia. DESIGN Pooled analysis using individual population-based data. PARTICIPANTS Individual participant data from population-based studies around the world that had ascertained RVO from fundus photographs. METHODS Each study(More)
Infrastructure-as-a-Service (IaaS) clouds are prone to performance anomalies due to their complex nature. Although previous work has shown the effectiveness of using statistical learning to detect performance anomalies, existing schemes often assume labelled training data, which requires significant human effort and can only handle previously known(More)
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. PREPARE(More)
OBJECTIVE To describe the natural history of central retinal vein occlusion (CRVO) based on the best available evidence from the literature. CLINICAL RELEVANCE Central retinal vein occlusion is a common sight-threatening retinal vascular disease. Despite the introduction of new interventions, the natural history of CRVO is unclear. METHODS Systemic(More)
OBJECTIVE To describe the natural history of branch retinal vein occlusion (BRVO) based on the best available evidence from the literature. CLINICAL RELEVANCE Branch retinal vein occlusion is the second most frequent major retinal vascular disease. Although several new treatments for BRVO are currently being introduced, data on its natural history are(More)
Distributed applications running inside cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. One big challenge for diagnosing an abnormal distributed application is to pinpoint the faulty components. In this paper, we present a black-box online fault localization system(More)
Distributed applications running inside cloud are prone to performance anomalies due to various reasons such as insufficient resource allocations, unexpected workload increases, or software bugs. However, those applications often consist of multiple interacting components where one component anomaly may cause its dependent components to exhibit anomalous(More)
BACKGROUND With increased use of robotic surgery in specialties including urology, development of training methods has also intensified. However, current approaches lack the ability to discriminate between operational and surgical skills. METHODS An automated recording system was used to longitudinally (monthly) acquire instrument motion/telemetry and(More)
OBJECTIVES Current robotic training approaches lack the criteria for automatically assessing and tracking (over time) technical skills separately from clinical proficiency. We describe the development and validation of a novel automated and objective framework for the assessment of training. METHODS We are able to record all system variables (stereo(More)