Robotics/AI

NIST Nomination Deadline: Candidate Algorithms for Lightweight Cryptographic Standards

The National Institute of Standards and Technology (NIST) solicits nominations from any interested party for candidate algorithms to be considered for lightweight cryptographic standards. In recent years, there has been increased demand for cryptographic standards that are tailored for constrained devices. NIST has decided to create a portfolio of lightweight cryptographic algorithms, designed for limited use in applications and environments where cryptographic operations are performed by constrained devices that are unable to use existing NIST standards.

The submission requirements and the minimum acceptability requirements of a “complete and proper” candidate algorithm submission, as well as the evaluation criteria that will be used to appraise the candidate algorithms, can be found on the NIST Computer Security Resource Center website at: https://csrc.nist.gov/​Projects/​Lightweight-Cryptography.

Algorithms may be submitted electronically to lightweight-crypto@nist.gov, or by mail. More information on submission packages can be found at this Federal Register notice.

NFS Information Request: National Strategic Overview for Quantum Information Science

The National Science and Technology Council (NSTC) Subcommittee on Quantum Information Science (SCQIS) release of the “National Strategic Overview for Quantum Information Science” (hereafter “Strategic Overview”) calls upon agencies to develop plans to address six key policy areas to enable continued American leadership in quantum information science. The National Science Foundation (NSF), working with the NSTC, is requesting information from the research and development community around quantum information science (QIS) to inform the subcommittee as the Government develops potential means of addressing specific policy recommendations.

Responders are asked to answer one or more of the following questions:

  1. What specific actions could the US Government take that would contribute best to implementing the policy recommendations in the Strategic Overview? What challenges, not listed in section 3, should also be taken into account in implementation of the Strategic Overview recommendations?
  2. What are the scientific and technological challenges that, with substantial resources and focus over the next ten years, will transform the QIS research and development landscape?
  3. Regarding industrial engagement, what roles can the U.S. Government play in enabling the innovation ecosystem around QIS-related technologies? Are there critical barriers for industrial innovation in this space? How can these barriers be addressed? What role can the U.S. Government play in mitigating early or premature investment risks?
  4. How can the U.S. Government engage with academia and other workforce development programs and stakeholders to appropriately train and maintain researchers in QIS while expanding the size and scope of the `quantum-smart' workforce?
  5. What existing infrastructure should be leveraged, and what new infrastructure could be considered, to foster future breakthroughs in QIS research and development?
  6. What other activities/partnerships could the U.S. Government use to engage with stakeholders to ensure America's prosperity and economic growth through QIS research and development?
  7. How can the United States continue to attract and retain the best domestic and international talent and expertise in QIS?
  8. How can the United States ensure that US researchers in QIS have access to cutting-edge international technologies, research facilities, and knowledge?

Comments may be submitted by members of the public, either through email or directly through this link. More information is available at this Federal Register notice.

Deep learning democratizes nano-scale imaging

Phys.org – To democratize access to high-resolution fluorescence imaging and be able to resolve and monitor objects at nano-scale, UCLA researchers have developed a new method, based on artificial intelligence, to digitally transform fluorescence images acquired using a lower resolution and simpler microscope into images that match the resolution and quality of higher resolution and advanced microscopes that are built for nano-scale imaging.