Long acquisition time (AQT) due to series acquisition of multi-modality MR images (especially T2 weighted images (T2WI) with longer AQT), though beneficial for disease diagnosis, is practically undesirable. We propose a novel deep network based solution to reconstruct T2W images from T1W images (T1WI) using an encoder-decoder architecture. The proposed learning is aided with semantic features by using multi-channel input with intensity values and gradient of image in two orthogonal directions. A reconstruction module (RM) augmenting the network along with a domain adaptation module (DAM) which is an encoder-decoder model built-in with sharp bottleneck module (SBM) is trained via modular training. The proposed network significantly reduces the total AQT with negligible qualitative artifacts and quantitative loss (reconstructs one volume in (~1 second). The testing is done on publicly available dataset with real MR images, and the proposed network shows (~ 1dB) increase in PSNR over SOTA.

Cloud services provide the ability to provision virtual networked infrastructure on demand over the Internet. The rapid growth of these virtually provisioned cloud networks has increased the demand for automated reasoning tools capable of identifying misconfigurations or security vulnerabilities. This type of automation gives customers the assurance they need to deploy sensitive workloads. It can also reduce the cost and time-to-market for regulated customers looking to establish compliance certification for cloud-based applications. In this industrial case-study, we describe a new network reachability reasoning tool, called Tiros, that uses off-the-shelf automated theorem proving tools to fill this need. Tiros is the foundation of a recently introduced network security analysis feature in the Amazon Inspector service now available to millions of customers building applications in the cloud. Tiros is also used within Amazon Web Services (AWS) to automate the checking of compliance certification and adherence to security invariants for many AWS services that build on existing AWS networking features.

The future electrical grid, i.e., smart grid, is envisioned to use appliance-level control to provide sustainable power usage and flexible energy utilization. However, load trace monitoring for appliance-level control poses privacy concerns given that private behaviors can be inferred by electricity utilization levels. In this paper, we introduce a privacy-preserving and fine-grained power load data analysis mechanism Di-PriDA for appliance-level peak-time load balancing control in the smart grid. Di-PriDA achieved 3ε-differential privacy, which provided indistinguishable application power consumption data to protect against eavesdroppers. The proposed technique explores a new differential privacy problem: the distributed top-k problem without a trusted third party, and provides both rigorous provable privacy and an accuracy guarantee based on distributed differential privacy. We implement a prototype of Di-PriDA on an external microcontroller device used for smart meters, and evaluate its performance under two real-world power usage datasets, as well as a synthetic dataset through a combination of experiments and simulations. We find that Di-PriDA effectively achieves confidentiality for the appliance-level peak-time load balancing control while guaranteeing a good quality-of-service to the various stakeholders of the power grid.