EvAn: Neuromorphic Event-Based Sparse Anomaly Detection.

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application-video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.

Evaluation of doctors' performance as facilitators in basic medical science lecture classes in a new Malaysian medical school.

BACKGROUND: Didactic lecture is the oldest and most commonly used method of teaching. In addition, it is considered one of the most efficient ways to disseminate theories, ideas, and facts. Many critics feel that lectures are an obsolete method to use when students need to perform hands-on activities, which is an everyday need in the study of medicine. This study evaluates students' perceptions regarding lecture quality in a new medical school. METHODS: This was a cross-sectional study conducted of the medical students of Universiti Sultan Zainal Abidin. The study population was 468 preclinical medical students from years 1 and 2 of academic year 2012-2013. Data were collected using a validated instrument. There were six different sections of questions using a 5-point Likert scale. The data were then compiled and analyzed, using SPSS version 20. RESULTS: The response rate was 73%. Among 341 respondents, 30% were male and 70% were female. Eighty-five percent of respondents agree or strongly agree that the lectures had met the criteria with regard to organization of lecture materials. Similarly, 97% of students agree or strongly agree that lecturers maintained adequate voices and gestures. CONCLUSION: Medical students are quite satisfied with the lecture classes and the lectures. However, further research is required to identify student-centered teaching and learning methods to promote active learning.