The Duke Funding Alert newsletter, published every Monday, provides information on all new and updated grants and fellowships added to the database during the prior week. This listserv is restricted to members of the Duke community.
Modeling Adversarial Activity (MAA) -- Deadline Extended
DARPA is soliciting innovative research proposals in the area of modeling adversarial activity for the purpose of producing high-confidence indications and warnings of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terrorism (WMT). This solicitation is focused upon the development of mathematical and computational methods that integrate multiple data sources to detect relevant activities and events with high probability of detection and low rates of false alarms. Proposed research should investigate innovative approaches that enable revolutionary advances in science and technology. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of the art.
The goal of the Modeling Adversarial Activity (MAA) program is to develop mathematical and computational techniques for modeling adversarial activity for the purpose of producing highconfidence indications and warnings of efforts to acquire, fabricate, proliferate, and/or deploy WMTs. The program is structured in two phases, each with its own BAA. This BAA is for MAA Phase 1. Upon the successful completion of MAA Phase 1, DARPA plans to release a BAA for MAA Phase 2. MAA Phase 2 will focus on continued development of Phase 1 methods and integration of methods into a proof-of-concept prototype system.
Relying solely on synthetic data, MAA Phase 1 is focused on developing the mathematical and computational methods to enable large-scale graph analytics including graph alignment and merging, sub-graph detection, and sub-graph matching. The methods must operate in noisy, complex, and time-dependent environments. Synthetic data sets will be created for the program to support the development of tools that can perform on real world data that is uncertain, incomplete, imprecise, and contradictory. The program will pursue a variety of approaches to the challenges of graph alignment and merging, sub-graph detection and sub-graph matching. The methods developed in MAA Phase 1 will be the foundation of the MAA Phase 2 system.
The MAA program requires realistic data to drive technology development. However, out of respect to the issues of privacy and classification, the MAA program will not use real-world data and MAA performers will, at no time, have access to or use real-world data.
o Abstract Due Date: October 28, 2016, 12:00 noon (ET)
o Proposal Due Date: December 22, 2016, 12:00 noon (ET) (was Dec. 15, 2016)
Areas of Interest
- Technical Area 1 – Synthetic Data Creation
- Technical Area 2 – Graph Merging
- Technical Area 3 – Activity Detection
While proposers may submit proposals for all three technical areas, proposers selected for Technical Area 1 cannot be selected for any portion of the other two technical areas, whether as a prime, subcontractor, or in any other capacity from an organizational to individual level. This is to avoid OCI situations between the technical areas and to ensure objective test and evaluation results. The decision as to which proposal to consider for award is at the discretion of the Government. Proposers may submit one proposal for both TA2 and TA3 efforts jointly.