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The Cyberinfrastructure for Sustained Scientific Innovation (CSSI) umbrella program seeks to enable funding opportunities that are flexible and responsive to the evolving and emerging needs in cyberinfrastructure. This program continues the CSSI program by removing the distinction between software and data elements/framework implementations, and instead emphasizing integrated cyberinfrastructure services, quantitative metrics with targets for delivery and usage of these services, and community creation.

The need to process large data sets arising in many practical problems require real-time learning from data streams makes high-performance hardware necessary, and yet the very nature of these problems, along with currently known algorithms for addressing them, imposes significant hardware challenges. Current versions of deep-learning algorithms operate by using millions of parameters whose optimal values need to be determined for good performance in real time on high-performance hardware. Conversely, the availability of fast hardware implementations can enable fuller use of Bayesian techniques, attractive for their ability to quantify prediction uncertainty and thus give estimates of reliability and prediction breakdown. The abilities of ML systems to self-assess for reliability and predict their own breakdowns (and also recover without significant ill effects) constitute critical areas for algorithm development as autonomous systems become widely deployed in both decision support and embodied AI agents. Only with attention to these challenges can we construct systems that are robust when they encounter novel situations or degradation and failure of sensors.

The Cyberinfrastructure for Sustained Scientific Innovation (CSSI) umbrella program encompasses the long-running Data Infrastructure Building Blocks (DIBBs) and Software Infrastructure for Sustained Innovation (SI2) programs, as NSF seeks to enable funding opportunities that are flexible and responsive to the evolving and emerging needs in data and software cyberinfrastructure.

The Cyber-Physical Systems (CPS) program solicitation has been revised for FY 2018, and prospective Principal Investigators are encouraged to read the solicitation carefully. Among the changes are the following:

CISE's Division of Computer and Network Systems (CNS) supports research and education projects that develop new knowledge in two core programs: Computer Systems Research (CSR) program; and Networking Technology and Systems (NeTS) program.

CISE's Division of Information and Intelligent Systems (IIS) supports research and education projects that develop new knowledge in three core programs: The Cyber-Human Systems (CHS) program; The Information Integration and Informatics (III) program; and The Robust Intelligence (RI) program. Proposals in the area of computer graphics and visualization may be submitted to any of the three core programs described above.

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 Dynamics, Control and Systems Diagnostics (DCSD) program supports fundamental research on the analysis, measurement, monitoring and control of dynamic systems. The program promotes innovation in the following areas: -Modeling: creation of new mathematical frameworks to apply tools of dynamics to physical systems -Analysis: discovery and exploration of structure in dynamic behavior -Diagnostics: dynamic methods that infer system properties from observations -Control: methods that produce desired dynamic behavior

The Dynamics, Control and Systems Diagnostics (DCSD) program supports fundamental research on the analysis, measurement, monitoring and control of dynamic systems. The program promotes innovation in the following areas: -Modeling: creation of new mathematical frameworks to apply tools of dynamics to physical systems -Analysis: discovery and exploration of structure in dynamic behavior -Diagnostics: dynamic methods that infer system properties from observations -Control: methods that produce desired dynamic behavior

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.

CISE's Division of Information and Intelligent Systems (IIS) supports research and education projects that develop new knowledge in three core programs: The Cyber-Human Systems (CHS) program; The Information Integration and Informatics (III) program; and The Robust Intelligence (RI) program. Proposals in the area of computer graphics and visualization may be submitted to any of the three core programs described above. Proposers are invited to submit proposals in three project classes, which are defined as follows: Small Projects - up to $500,000 total budget with durations up to three years; Medium Projects - $500,001 to $1,200,000 total budget with durations up to four years; and Large Projects - $1,200,001 to $3,000,000 total budget with durations up to five years.

The Dynamics, Control and Systems Diagnostics (DCSD) program supports fundamental research on the analysis, measurement, monitoring and control of dynamic systems. The program promotes innovation in the following areas: Modeling: creation of new mathematical frameworks to apply tools of dynamics to physical systems Analysis: discovery and exploration of structure in dynamic behavior Diagnostics: dynamic methods that infer system properties from observations Control: methods that produce desired dynamic behavior

This CRISP 2.0 solicitation responds both to national needs on the resilience of critical infrastructures and to increasing NSF emphasis on transdisciplinary research. In this context, the solicitation is one element of the NSF-wide Risk and Resilience activity, with the overarching goal of advancing knowledge in support of improvement of the nation's infrastructure resilience. The devastating effects of recent disasters such as Hurricanes Harvey, Irma and Maria have underscored that a great deal remains to be done. In addition, CRISP 2.0 is aligned with the NSF-wide frontier thinking on convergence, characterized as "deep integration of knowledge, techniques, and expertise from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities".

A key focus of the design of modern computing systems is performance and scalability, particularly in light of the limits of Moore's Law and Dennard scaling. To this end, systems are increasingly being implemented by composing heterogeneous computing components and continually changing memory systems as novel, performant hardware surfaces. Applications fueled by rapid strides in machine learning, data analysis, and extreme-scale simulation are becoming more domain-specific and highly distributed. In this scenario, traditional boundaries between hardware-oriented and software-oriented disciplines increasingly are blurred. Achieving scalability of systems and applications will therefore require coordinated progress in multiple disciplines such as computer architecture, high-performance computing (HPC), programming languages and compilers, security and privacy, systems, theory, and algorithms. Cross-cutting concerns such as performance (including, but not limited to, time, space, and communication resource usage and energy efficiency), correctness and accuracy (including, but not limited to, emerging techniques for program analysis, testing, debugging, probabilistic reasoning and inference, and verification), security and privacy, robustness and reliability, domain-specific design, and heterogeneity must be taken into account from the outset in all aspects of systems and application design and implementation.