We develop a framework for sampling from discrete distributions µ on the hypercube {± 1}n by sampling from continuous distributions supported on ℝn obtained by convolution with spherical Gaussians. We show that for well-studied families of discrete distributions µ, the result of the convolution is well-conditioned log-concave, whenever the Gaussian’s variance is above an O(1) threshold. We plug off-the-shelf continuous sampling methods into our framework to obtain novel discrete sampling algorithms. Additionally, we introduce and study a crucial notion of smoothness for discrete distributions that we call transport stability, which we use to control the propagation of error in our framework. We expect transport stability to be of independent interest, as we connect it to constructions of optimally mixing local random walks and concentration inequalities. As our main application, we resolve open questions raised by Anari, Hu, Saberi, and Schild on the parallel sampling of distributions which admit parallel counting. We show that determinantal point processes can be sampled via RNC algorithms, that is in time log(n)O(1) using nO(1) processors. For a wider class of distributions, we show our framework yields Quasi-RNC sampling, i.e., log(n)O(1) time using nO(logn) processors. This wider class includes non-symmetric determinantal point processes and random Eulerian tours in digraphs, the latter nearly resolving another open question raised by prior work.

In this paper, we present a vulnerability assessment framework that could be used to assess and prevent cyber threats related to wired and wireless networks and computer systems. We have performed vulnerability assessment tests for aviation systems including data loaders and in order to meet aviation industry requirements for wireless network security. Our contributions include detecting cyber vulnerabilities in these aviation systems by using vulnerability assessment and penetration testing tools such as Metasploit Pro and BackTrack and improving security and safety of aircraft. Based on our test results of cyber vulnerabilities, the corresponding solutions will be developed to fix these vulnerabilities. New vulnerability assessment tests will be conducted again until our solutions are secure and safe to use. Some results of our vulnerability assessment tests against our software-hardware products are presented.

To address the current need of innovative technologies that blend rapid data processing capabilities of computers with intuitive decision making skills of humans, we have developed a prototype of Cloud Enabled Brain Computer Interface (CEB) decision making technologies. The implemented architecture integrates cloud enabled big data analytics capabilities, networked BCI (Brain Computer Interface) devices, and Decision Making Engine. The novel CEB technology comprises of 1. Cloud-enabled BCI (Brain-Computer Interface) headsets, which is developed and networked in a cloud to enable rapid decision making and 2. Genetic algorithm based decision making engine, to intelligently assist the users in decision making; Advantage of our architecture is that when CEB loads the data, it will automatically recommend the best applicable Machine Learning (ML) algorithms after being evaluated to solve a given problem. Hence, with such automated machine learning techniques, CEB users workload is significantly reduced. Our experiments on DARPA dataset indicate that CEB technologies performed 10 times faster and about 4 times less false negative rate than current computational methods in seeking and understanding information. Our results demonstrate that these CEB technologies would enable humans to accurately and quickly detect meaningful information from a mass amount of data. The novel CEB technologies ensure that the reduced manpower does not result in reduced performance.