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NASA created the University Leadership Initiative (ULI) to engage creative and innovative minds in the academic arena to identify significant aeronautics and aviation research challenges and define their unique approach to their solution. The ULI was started in 2015 as part of the larger University Innovation Project, with the goal of seeking new, innovative ideas that can support the U.S. aviation community and NASA's long-term aeronautics research goals, as established by its Aeronautics Research Mission Directorate. Assessing NASA's University Leadership Initiative reviews the ULI and makes recommendations to enhance program's impact to benefit students, faculty, industry, and the U.S. public.
Protecting Earth's environment and other solar system bodies from harmful contamination has been an important principle throughout the history of space exploration. For decades, the scientific, political, and economic conditions of space exploration converged in ways that contributed to effective development and implementation of planetary protection policies at national and international levels. However, the future of space exploration faces serious challenges to the development and implementation of planetary protection policy. The most disruptive changes are associated with (1) sample return from, and human missions to, Mars; and (2) missions to those bodies in the outer solar system possessing water oceans beneath their icy surfaces. Review and Assessment of Planetary Protection Policy Development Processes addresses the implications of changes in the complexion of solar system exploration as they apply to the process of developing planetary protection policy. Specifically, this report examines the history of planetary protection policy, assesses the current policy development process, and recommends actions to improve the policy development process in the future.
At the request of NASA, an IOM committee reviewed NASA Human Research Program's (HRP's) Scientific Merit Assessment Processes for directed research. Directed research is commissioned or noncompetitively awarded research that is not competitively solicited because of specific reasons, such as time limitations or highly focused or constrained research topics. The scientific merit assessment processes have been developed by NASA to evaluate individual directed research tasks in order to ensure the scientific integrity of the HRP's directed research portfolio. The committee examined the HRP's current scientific merit assessment processes and conducted a public workshop to identify best practices among other federal agencies that use various assessment processes for similar types of directed research. Review of NASA Human Research Program's Scientific Merit Processes: Letter Report finds that the scientific merit assessment process used by the HRP for directed research is scientifically rigorous and is similar to the processes and merit criteria used by many other federal agencies and organizations - including the Department of Defense, National Institutes of Health, and the United States Department of Agriculture - for comparable types of research. This report also makes recommendations on ways to streamline and bolster the accountability and transparency of NASA's current processes.
This book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.
In response to a Congressional mandate, the National Research Council conducted a review of the Small Business Innovation Research Program (SBIR) at the five federal agencies with SBIR programs with budgets in excess of $100 million (DOD, NIH, NASA, DOE, and NSF). The project was designed to answer questions of program operation and effectiveness, including the quality of the research projects being conducted under the SBIR program, the commercialization of the research, and the program's contribution to accomplishing agency missions. The first in a series to be published in response to the Congressional request, this report summarizes the presentations at a symposium convened at the beginning of the project. The report provides a comprehensive overview of the SBIR program's operations at the five agencies responsible for 96 percent of the program's operations.
Astrobiology is a scientific discipline devoted to the study of life in the universe - its origin, evolution, distribution, and future. In 1997, NASA established an Astrobiology program (the NASA Astrobiology Institute - NAI) as a result of a series of new results from solar system exploration and astronomical research in the mid-1990s together with advances in the biological sciences. To help evaluate the NAI, NASA asked the NRC to review progress made by the Institute in developing the field of astrobiology. This book presents an evaluation of NAI's success in meeting its goals for fostering interdisciplinary research, training future astrobiology researchers, providing scientific and technical leadership, exploring new research approaches with information technology, and supporting outreach to K-12 education programs.
This contributed volume aims to build the foundation of a framework for computationally aware algorithmic design for cyber-physical systems (CPSs), focusing on approaches that take computation into account at the design stage to address their impact on performance and safety. It demonstrates how novel techniques may emerge from the combination of formal methods, model predictive control, distributed optimization, data-driven methods, reconfigurable/adaptive methods, and information-theoretic techniques. Chapters are written by both researchers and practitioners and cover such topics as analysis and design of uncertain CPSs, cooperative and non-cooperative paradigms for handling complexity in large scale CPSs, task-relevant environment abstractions for autonomous systems based on information theory, information flow in event-based stabilization of CPSs, set-valued model predictive control, and automated synthesis of certifiable controllers for CPSs. State-of-the-art applications and case studies are provided throughout with a special focus on intelligent transportation systems and autonomous vehicles. Graduate students and researchers with an interest in CPS verification and control will find this volume to be a valuable resource in their work. It will also appeal to researchers from disciplines other than control, such as computer science, operations research, applied mathematics, and robotics.
The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.