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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Recent Advances in Vision Transformer Robustness Against Adversarial Attacks in Traffic Sign Detection and Recognition: A Survey

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ACM Computing Surveys 1000年

作者： Oluwajuwon Fawole Danda Rawat DoD Center of Excellence in AI/ML and Department of Electrical Engineering & Computer Science Howard University Washington United States

The emergence of Vision Transformers (ViTs) has marked a significant advancement in machine learning, particularly in applications requiring robust visual recognition capabilities, such as traffic sign detection for autonomous driving systems. But, deploying these models in adversarial environments where robustness is critical remains a challenge. This survey provides a comprehensive review of the integration of ViTs in traffic sign detection and recognition, emphasizing their vulnerability to adversarial attacks and the methods developed to enhance their robustness. This paper also presents a compressive comparison of ViTs in a tabular form for side-by-side comparison.

关键词： Adversarial attack adversarial training vision transformers and traffic signs.

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Human-Centric Smart Computing 1

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丛书名： Smart Innovation, Systems and Technologies

1000年

作者： Siddhartha Bhattacharyya Jyoti Sekhar Banerjee Mario Köppen

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Explainable IoT Applications: A Demystification 1

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丛书名： Information Systems engineering and Management

1000年

作者： Sachi Nandan Mohanty Suneeta Satpathy Xiaochun Cheng Subhendu Kumar Pani

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Hybrids of Reinforcement Learning and Evolutionary Computation in Finance: A Survey

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ACM Computing Surveys 1000年

作者： Sandarbh Yadav Vadlamani Ravi Shivaram Kalyanakrishnan Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Centre for AI and ML Institute for Development and Research in Banking Technology Hyderabad India

Many sequential decision-making problems in finance like trading, portfolio optimisation, etc. have been modelled using reinforcement learning (RL) and evolutionary computation (EC). Recent studies on problems from various domains have shown that EC can be used to improve the performance of RL and vice versa. Over the years, researchers have proposed different ways of hybridising RL and EC for trading and portfolio optimisation. However, there is a lack of a thorough survey in this research area, which lies at the intersection of RL, EC, and finance. This paper surveys hybrid techniques combining EC and RL for financial applications and presents a novel taxonomy. Research gaps have been discovered in existing works and some open problems have been identified for future works. A detailed discussion about different design choices made in the existing literature is also included.

关键词： Evolutionary computation finance trading portfolio optimisation

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Intelligent Human Centered Computing 1

Intelligent Human Centered Computing

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丛书名： Springer Tracts in Human-Centered Computing

1000年

作者： Siddhartha Bhattacharyya Jyoti Sekhar Banerjee Debashis De Mufti Mahmud

ISBN: (数字)9789819617616

ISBN: (纸本)9789819617609;9789819617630

This book features high-quality research papers presented at the Second Doctoral Symposium on Human Centered Computing (HUMAN 2024), jointly organized by computer Society of India, Kolkata Chapter and Sister Nivedita University, West Bengal, on March 30, 2024. This book discusses the topics of modern human centered computing and its applications. The book showcases the fusion of human sciences (social and cognitive) with computer science (human–computer interaction, signal processing, machine learning, and ubiquitous computing).

关键词： Computational Intelligence Artificial Intelligence User Interfaces and Human computer Interaction

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Cognitive Computing for Machine Thinking 1

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丛书名： Innovations in Sustainable Technologies and Computing

1000年

作者： Makarand R. Velankar Parikshit N. Mahalle Gitanjali R. Shinde

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Human-Centric Smart Computing 1

引用

丛书名： Smart Innovation, Systems and Technologies

1000年

作者： Siddhartha Bhattacharyya Jyoti Sekhar Banerjee Mario Köppen

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109;9789819977130

This book includes high-quality research papers presented at the Second International Conference on Human-Centric Smart Computing (ICHCSC 2023), organized by the University of engineering and Management, Jaipur, India, on 5–6 July 2023 in New Delhi, India. The topics covered in the book are human-centric computing, hyper connectivity, and data science. The book presents innovative work by leading academics, researchers, and experts from industry.

关键词： Computational Intelligence User Interfaces and Human computer Interaction Data Structures and Information Theory Artificial Intelligence Mobile and Network Security

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Intelligent Human Centered Computing 1

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丛书名： Springer Tracts in Human-Centered Computing

1000年

作者： Siddhartha Bhattacharyya Jyoti Sekhar Banerjee Debashis De Mufti Mahmud

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