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The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals in the area of global scale, multimodal geospatial data cloud platform and analytics development. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice. GCA is a 24-month, three-phase program. At present, DARPA seeks innovative proposals covering the tasks in Phase 1 (6 month base effort) and Phase 2 (12 month costed option) of the program. Proposals must address both Phase 1 and Phase 2. Phase 3 will be the subject of a separate procurement. For Phases 1 and 2, DARPA seeks proposals in two technical areas (TAs). TA-1 (Scalable Geospatial Data Platform) will provide access to geospatial data and an extensible computing platform on which TA-2 performers can efficiently access and process massive amounts of curated geospatial data. TA-2 (Analytical Applications and Competitions) will create software for use in one or more of the analytics competitions (predicting food shortages, locating fracking construction detection, illegal fishing detection, open call) using data and platforms provided by TA-1 proposers. In Phase 1, DARPA anticipates making up to three awards for TA-1 and up to 16 awards for TA-2. A proposer may respond to one or both technical areas but a separate proposal is required for each TA. A TA-2 proposer can propose to one or more competition areas, using costed options if proposing to more than one competition.

The CREATE program seeks proposals to develop, and experimentally test, systems that use crowdsourcing and structured analytic techniques (STs) to improve analytic reasoning. These systems will help people better understand the evidence and assumptions that support-or conflict with-conclusions. Secondarily, they will also help users better communicate their reasoning and conclusions. STs hold promise for increasing the logical rigor and transparency of analysis. They can help reveal underlying logic and identify unstated assumptions. Yet they are not widely used in the Intelligence Community or elsewhere-possibly because current versions are cumbersome or require too much time. Crowdsourcing has the potential to solve these problems by dividing the labor, allowing dispersed groups of analysts to contribute information and ideas where they have comparative advantages. Crowdsourcing can help analysts identify and understand alternative hypotheses, arguments, and points of view. Crowdsourcing of structured techniques may facilitate rational deliberation by integrating different perspectives, so that analysis can effectively benefit from "crowd wisdom."

The CREATE program seeks proposals to develop, and experimentally test, systems that use crowdsourcing and structured analytic techniques (STs) to improve analytic reasoning. These systems will help people better understand the evidence and assumptions that support-or conflict with-conclusions. Secondarily, they will also help users better communicate their reasoning and conclusions. STs hold promise for increasing the logical rigor and transparency of analysis. They can help reveal underlying logic and identify unstated assumptions. Yet they are not widely used in the Intelligence Community or elsewhere-possibly because current versions are cumbersome or require too much time. Crowdsourcing has the potential to solve these problems by dividing the labor, allowing dispersed groups of analysts to contribute information and ideas where they have comparative advantages. Crowdsourcing can help analysts identify and understand alternative hypotheses, arguments, and points of view. Crowdsourcing of structured techniques may facilitate rational deliberation by integrating different perspectives, so that analysis can effectively benefit from "crowd wisdom."

The Defense Sciences Office (DSO) at the Defense Advanced Research Projects Agency (DARPA) is soliciting innovative research proposals in the areas of: automated and sensor fused data analytics, network infrastructure and system integration, and interfaces and interoperability to support a networked system for the detection, interdiction, and deterrence of clandestine weapons of mass destruction (WMD) activities. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, and systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. The MGI Strategic Plan highlights four sets of goals: -Leading a culture shift in materials science research to encourage and facilitate an integrated team approach; -Integrating experimentation, computation, and theory and equipping the materials community with advanced tools and techniques; -Making digital data accessible; and -Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. The MGI Strategic Plan highlights four sets of goals: -Leading a culture shift in materials science research to encourage and facilitate an integrated team approach; -Integrating experimentation, computation, and theory and equipping the materials community with advanced tools and techniques; -Making digital data accessible; and -Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry.

Researchers in all fields of science and engineering are being challenged in two key directions.  The first challenge is to push beyond the current boundaries of knowledge to provide ever-deeper insights through fundamental disciplinary research by addressing increasingly complex questions, which often requires extremely sophisticated integration of theoretical, experimental, observational and simulation and modeling results.   These efforts, which have relied heavily on observing platforms and other data collection efforts, computing facilities, software, advanced networking, analytics, visualization and models have led to important breakthroughs in all areas of science and engineering and represent a very strong bottom-up approach to the necessary research infrastructure.  The second, and more extensive challenge, is to synthesize these fundamental ground breaking efforts across multiple fields to transform scientific research into an endeavor that integrates the deep knowledge and research capabilities developed within the universities, industry and government labs. Individuals, teams and communities need to be able work together; likewise, instruments, facilities (including MREFCs), datasets, and cyber-services must be integrated from the group to campus to national scale. One can imagine secure, geographically distributed infrastructure components including advanced computing facilities, scientific instruments, software environments, advanced networks, data storage capabilities, and the critically important human capital and expertise. Greater understanding is also needed of how scientific and research communities will evolve in the presence of new cyberinfrastructure. 

The Dynamics, Control and Systems Diagnostics (DCSD) program supports fundamental research on the analysis, measurement, monitoring and control of dynamic systems, including development of new analytical, computational and experimental tools, and novel applications to engineered and natural systems. Dynamics is the science of systems that change in time. Control concerns the use of external influences to produce desired dynamic behaviors. Systems diagnostics concerns the use of observation to infer information about a dynamic system. Objectives of the DCSD program are the discovery of new phenomena and the investigation of innovative methods and applications in dynamics, control and diagnostics. The intellectual merit of proposals submitted to the DCSD program will be evaluated on the basis of fundamental innovation in foundational areas of dynamics and control, and on the potential for transformative impact within and across disciplinary boundaries.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) other computing and information researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, artificial intelligence and its applications, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of both the algorithmic contributions and the resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

The Scalable Parallelism in the Extreme (SPX) program aims to support research addressing the challenges of increasing performance in this modern era of parallel computing. This will require a collaborative effort among researchers in multiple areas, from services and applications down to micro-architecture. SPX encompasses all five NSCI Strategic Objectives, including supporting foundational research toward architecture and software approaches that drive performance improvements in the post-Moore's Law era; development and deployment of programmable, scalable, and reusable platforms in the national HPC and scientific cyberinfrastructure ecosystem; increased coherence of data analytic computing and modeling and simulation; and capable extreme-scale computing. Coordination with industrial efforts that pursue related goals are encouraged.

The Dynamics, Control and Systems Diagnostics (DCSD) program supports fundamental research on the analysis, measurement, monitoring and control of dynamic systems, including development of new analytical, computational and experimental tools, and novel applications to engineered and natural systems. Dynamics is the science of systems that change in time. Control concerns the use of external influences to produce desired dynamic behaviors. Systems diagnostics concerns the use of observation to infer information about a dynamic system. Objectives of the DCSD program are the discovery of new phenomena and the investigation of innovative methods and applications in dynamics, control and diagnostics. The intellectual merit of proposals submitted to the DCSD program will be evaluated on the basis of fundamental innovation in foundational areas of dynamics and control, and on the potential for transformative impact within and across disciplinary boundaries. Proposals submitted to the DCSD program should be aligned with the disciplinary thrusts of the CMMI division. For example, innovative research that primarily concerns electromagnetic or chemical phenomena should be directed to the ECCS or CBET divisions. To ensure that a project is appropriate for the DCSD program, PIs are very strongly encouraged to email a project summary of approximately 250 words to the DCSD Program Directors prior to the full submission. The DCSD Program does not fund fundamental research relating to sensing modalities or sensor development. Proposals offering fundamental research on sensing modalities should be submitted to the Communications, Circuits and Sensing Systems (CCSS) program or the Electronics, Photonics, and Magnetic Devices (EPMD) program in the ECCS Division.

The MES program supports research on design, planning, and control of operations in manufacturing enterprises. Research is supported that is both grounded in an interesting and relevant application and requires the development of novel analytical and computational methodologies that may be of broader interest. Topics of interest include supply chain optimization and management; production planning and scheduling; monitoring and control of manufacturing processes; and maintenance and repair. Of particular interest are methods that incorporate increasingly rich enterprise process and product information and models, methods that address sustainability, and methods that incorporate characteristic uncertainty and risk.

This program supports innovative research on the theories of dynamical systems, including new analytical and computational tools, as well as the novel application of dynamical systems to engineered systems.  The program is especially interested in transformative research in the area of complex systems, uncertain or stochastic nonlinear dynamical systems, model order reduction of nonlinear or infinite dimensional dynamical systems, discrete nonlinear dynamical systems, and modeling, simulation, analysis and design of multi-scale multi-physics dynamical systems. 

The lack of logistics systems capable of handling and delivering sufficiently high tonnage year-round volumes of high quality feedstocks to support the rapid escalation of cellulosic biofuels production has been identified as a significant barrier to the expansion of a sustainable domestic biofuels industry. In particular, biomass physical and chemical quality parameters have repeatedly been identified as significant challenges to the smooth operation and economic viability of biorefineries. This FOA will focus on developing and demonstrating strategies, equipment, and rapid analytical methods to manage feedstock quality within economic constraints throughout the feedstock supply chain. The main effort in Proposals must be directed toward full-scale demonstration of integrated feedstock supply chain systems that can deliver the volume of high quality, affordable, high impact feedstocks required by commercial biorefineries over a significant geographic area in the United States. DOE plans to support the increased production of high volumes of sustainably produced domestic biofuels from cellulosic feedstocks by seeking Proposals to design new systems or adapt existing systems to handle industrial scale volumes of cellulosic feedstocks from the harvest point to the throat of the biorefinery reactor.

Algorithms in the Field encourages closer collaboration between two groups of researchers: (i) theoretical computer science researchers, who focus on the design and analysis of provably efficient and provably accurate algorithms for various computational models; and (ii) applied researchers including a combination of systems and domain experts (very broadly construed - including but not limited to researchers in computer architecture, programming languages and systems, computer networks, cyber-physical systems, cyber-human systems, machine learning, database and data analytics, etc.) who focus on the particular design constraints of applications and/or computing devices. Each proposal must have at least one co-PI interested in theoretical computer science and one interested in any of the other areas typically supported by CISE. Proposals are expected to address the dissemination of the algorithmic contributions and resulting applications, tools, languages, compilers, libraries, architectures, systems, data, etc.

This program seeks to prepare, nurture, and grow the national scientific research workforce for creating, utilizing, and supporting advanced cyberinfrastructure (CI) to enable and potentially transform fundamental science and engineering research and contribute to the Nation's overall economic competitiveness and security. The goals of this solicitation are to (i) ensure broad adoption of CI tools, methods, and resources by the research community in order to catalyze major research advances and to enhance researchers' abilities to lead the development of new CI; and (ii) integrate core literacy and discipline-appropriate advanced skills in advanced CI as well as computational and data-driven science and engineering into the Nation's educational curriculum/instructional material fabric spanning undergraduate and graduate courses for advancing fundamental research. Pilot and Implementation projects may target one or both of the solicitation goals, while Large-scale Project Conceptualization projects must address both goals. For the purpose of this solicitation, advanced CI is broadly defined as the set of resources, tools, methods, and services for advanced computation, large-scale data handling and analytics, and networking and security for large-scale systems that collectively enable potentially transformative fundamental research.

he intent of this solicitation is to request proposals from organizations willing to serve as service providers (SPs) within the NSF Innovative High-Performance Computing (HPC) program to provide advanced cyberinfrastructure (CI) capabilities and/or services in production operations to support the full range of computational- and data-intensive research across all of science and engineering (S&E). The current solicitation is intended to complement previous NSF investments in advanced computational infrastructure by provisioning resources, broadly defined in this solicitation to include systems and/or services, in two categories: Category I, Capacity Systems: production computational resources maximizing the capacity provided to support the broad range of computation and data analytics needs in S&E research; and Category II, Innovative Prototypes/Testbeds: innovative forward-looking capabilities deploying novel technologies, architectures, usage modes, etc., and exploring new target applications, methods, and paradigms for S&E discoveries.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

The Civil Infrastructure Systems (CIS) program supports fundamental and innovative research in the design, operation and management of civil infrastructure that contributes to creating smart, sustainable and resilient communities at local, national and international scales. This program focuses on civil infrastructure as a system in which interactions between spatially- and functionally- distributed components and intersystem connections exist. All critical civil infrastructure systems are of interest, including transportation, power, water, pipelines and others. The CIS program encourages potentially disruptive ideas that will open new frontiers and significantly broaden and transform relevant research communities. The program particularly welcomes research that addresses novel system and service design, system integration, big data analytics, and socio-technological-infrastructure connections. The program values diverse theoretical, scientific, mathematical, or computational contributions from a broad set of disciplines. While component-level, subject-matter knowledge may be crucial in many research efforts, the program does not support research with a primary contribution pertaining to individual infrastructure components such as materials, sensor technology, extreme event analysis, human factors, climate modeling, structural, geotechnical, hydrologic or environmental engineering.

The Acquisition Research Program (ARP) (www.acquisitionresearch.net) conducts and supports research in academic disciplines that bear on public procurement policy and management. These include economics, finance, financial management, information systems, organization theory, operations management, human resources management, risk management, and marketing, as well as the traditional public procurement areas such as contracting, program/project management, logistics, test and evaluation and systems engineering management. The ARP is interested in innovative proposals that will provide unclassified and non-proprietary findings suitable for publication in open scholarly literature. Studies of government processes, systems, or policies should also expand the body of knowledge and theory of processes, systems, or policies outside the government. The following research areas are of special interest: Leading-edge techniques in data collection, management, visual analytics and decision-making; Robust risk modeling techniques; Performance metrics and methodologies; Collaboration and cross-functional teams; and, Model-Based Acquisition. Offerors bear prime responsibility for the design, management, direction and conduct of research. Researchers should exercise judgment and original thought toward attaining the goals within broad parameters of the research areas proposed and the resources provided. Offerors are encouraged to be creative in the selection of the technical and management processes and approaches and consider the greatest and broadest impact possible.