The original version of this chapter contained an error in the third author’s name. The spelling of Chaim Yosefy’s name was incorrect in the header of the paper. The author name has been corrected.
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect, segment, and analyze retinal vessels. In particular, we propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder. We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions. Unlike previous deep-learning-based approaches to retinal vessel segmentation that mainly rely on patch-wise analysis, our proposed method leverages a whole-image approach during training and inference, resulting in more efficient training and faster inference through the access of global content in the image. We have tested our method on two publicly available datasets, and our state-of-the-art results on both the DRIVE and CHASE-DB1 datasets attest to the effectiveness of our approach.
We present a domain-specific language called SAL(the Streaming Analytics Language) for processing data in a semi-streaming model. In particular we examine the use case of processing netflow data in order to identify malicious actors within a network. Because of the large volume of data generated from networks, it is often only feasible to process the data with a single pass, utilizing a streaming (O(polylog n) space requirements) or semi-streaming computing model ( O(n polylog n) space requirements). Despite these constraints, we are able to achieve an average of 0.87 for the AUC of the ROC curve for a set of situations dealing with botnet detection. The implementation of an interpreter for SAL, which we call SAM (Streaming Analytics Machine), achieves scaling results that show improved throughput to 61 nodes (976 cores), with an overall rate of 373,000 netflows per second or 32.2 billion per day. SAL provides a succinct way to describe common analyses that allow cyber analysts to find data of interest, and SAM is a scalable interpreter of the language.
