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Self-Awareness for Autonomous Systems

PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Autonomous car using CNN deep learning algorithm

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Journal of Physics: Conference Series 2021年第1期1869卷

作者： I Sonata Y Heryadi L Lukas A Wibowo Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta Indonesia 11480 Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta Indonesia 12930

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment.

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Corrections to "Contrastive Graph Pooling for Explainable Classification of Brain Networks"

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IEEE transactions on medical imaging 2024年第11期43卷 4075页

作者： Jiaxing Xu Qingtian Bian Xinhang Li Aihu Zhang Yiping Ke Miao Qiao Wei Zhang Wei Khang Jeremy Sim Balazs Gulyas School of Computer Science and Engineering Nanyang Technological University Jurong West Singapore Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science The University of Auckland Auckland New Zealand Cognitive Neuroimaging Centre and the Lee Kong Chian School of Medicine Nanyang Technological University Jurong West Singapore Cognitive Neuroimaging Centre and IGP-Neuroscience Interdisciplinary Graduate Program Nanyang Technological University Jurong West Singapore Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden

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Cross-sectional and longitudinal associations between sedentary behavior and behavioral problems in children with overweight/obesity

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Mental Health and Physical Activity 2025年 29卷

作者： Kaiqi Guan Jiayi Yang Boris Cheval Matthew Health Fabian Herold André O. Werneck Cassandra J. Lowe Mats Hallgren Benjamin Tari Dominika Pindus Ryan S. Falck Markus Gerber Justin A. Haegele Arthur F. Kramer Neville Owen Charles H. Hillman Tianyou Guo Liye Zou Body-Brain-Mind Laboratory School of Psychology Shenzhen University Shenzhen 518060 China Department of Sport Sciences and Physical Education École Normale Supérieure de Rennes Bruz France VIPS2 Laboratory University of Rennes France Canadian Centre for Activity and Aging University of Western Ontario London ON N6A 3K7 Canada Graduate Program in Neuroscience The University of Western Ontario 1151 Richmond St London ON N6A 3K7 Canada School of Kinesiology Faculty of Health Sciences The University of Western Ontario 1151 Richmond St London ON N6A 3K7 Canada Department of Physiology Faculty of Medicine HMU Health and Medical University Erfurt Erfurt Germany Center for Epidemiological Research in Nutrition and Health Department of Nutrition School of Public Health Universidade de São Paulo São Paulo Brazil Department of Psychology University of Exeter UK Department of Global Public Health Karolinska Institute Stockholm Sweden Institute of Sport Exercise and Health Division of Surgery and Interventional Sciences University College London London UK Department of Health and Kinesiology University of Illinois Urbana-Champaign Urbana IL USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign Urbana IL USA Neuroscience Program University of Illinois Urbana-Champaign Urbana IL USA School of Biomedical Engineering University of British Columbia Vancouver BC Canada Department of Sport Exercise and Health University of Basel 4052 Basel Switzerland Center for Movement Health & Disability Old Dominion University Norfolk VA USA Darden College of Education & Professional Studies Old Dominion University Norfolk VA USA Center for Cognitive and Brain Health Northeastern University Boston MA USA Department of Psychology Northeastern University Boston MA 02115 USA Physical Activity Laboratory Baker Heart & Diabetes Institute Melbourne Victoria Australia Centre for Urban Transitions Swinburne University of

Background Sedentary behaviors (ST) are linked to children's mental health, including internalizing and externalizing problems. Research on these associations in children with overweight/obesity is limited. To this end, we examined cross-sectional and longitudinal associations between sedentary time (ST) and behavioral problems in children with overweight/obesity, followed by an investigation of potential sex-related differences. Methods We included 2273 children with overweight/obesity (49.9 % boys) from the UK Millennium Cohort Study. Accelerometer-measured sedentary time (ST) and MVPA were collected at age 7, and behavioral problems were assessed at ages 7 and 11 via the Strengths and Difficulties Questionnaire (SDQ). General linear models were used to examine how ST at 7 years of age was cross-sectionally and longitudinally associated with externalizing and internalizing, while ethnicity, SDQ scores, parental psychological distress, highest parental education, household poverty, and moderate-to-vigorous physical activity (MVPA) were statistically controlled. These analyses are followed by an analysis investigating sex-related differences. Results In the cross-sectional analyses, more ST was associated with fewer externalizing problems at 7 years among boys (β: −0.008, 95 % CI: −0.013, −0.003) and girls (β: −0.006, 95 % CI: −0.011, −0.001) without controlling for MVPA. More ST at 7 years was associated with fewer externalizing problems (β: −0.010, 95 %CI: −0.015, −0.004) only among boys when adjusting for MVPA. In the longitudinal analysis, more ST at 7 years was associated with fewer externalizing problems at 11 years for boys (β: −0.006, 95 %CI: −0.010, −0.001) and girls (β: −0.007, 95 %CI: −0.012, −0.002) without controlling for MVPA. When adjusting for MVPA at 7 years, more ST at 7 years was associated with fewer externalizing problems at 11 years for girls (β: −0.006, 95 %CI: −0.013, 0.000). Conclusions In overweight/obese children, device-measured ST at the

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186 – Inefficient neural network during spatial working memory task in subjects at high genetic-risk for schizophrenia

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Schizophrenia Research 2008年 98卷 110-110页

作者： Han, J.Y. Park, J.Y. Kang, D.H. Choi, J.S. Jung, M.H. Choi, C.H. Jung, Y.S. Lee, J.M. Kwon, J.S. Interdisciplinary Program in Cognitive and Brain Science Seoul National University Seoul Republic of Korea Department of Psychiatry Seoul National University College of Medicine Seoul Republic of Korea Clinical Cognitive Neuroscience Center Neuroscience Institute SNU-MRC Seoul Republic of Korea Department of Radiology National Medical Center Seoul Republic of Korea Department of Biomedical Engineering Hanyang University School of Medicine Seoul Republic of Korea

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Influence of Magnetic Fields on Electrochemical Reactions of Redox Cofactor Solutions

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Angewandte Chemie 2021年第33期133卷

作者： Jimin Park Florian Koehler Georgios Varnavides Marc-Joseph Antonini Prof. Dr. Polina Anikeeva Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Research Laboratory of Electronics and McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA Harvard/MIT Health Science & Technology Graduate Program Cambridge MA 02139 USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA

Redox cofactors mediate many enzymatic processes and are increasingly employed in biomedical and energy applications. Exploring the influence of external magnetic fields on redox cofactor chemistry can enhance our understanding of magnetic-field-sensitive biological processes and allow the application of magnetic fields to modulate redox reactions involving cofactors. Through a combination of experiments and modeling, we investigate the influence of magnetic fields on electrochemical reactions in redox cofactor solutions. By employing flavin mononucleotide (FMN) cofactor as a model system, we characterize magnetically induced changes in Faradaic currents. We find that radical pair intermediates have negligible influence on current increases in FMN solution upon application of a magnetic field. The dominant mechanism underlying the observed current increases is the magneto-hydrodynamic effect. We extend our analyses to other diffusion-limited electrochemical reactions of redox cofactor solutions and arrive at similar conclusions, highlighting the opportunity to use this framework in redox cofactor chemistry.

关键词： electrochemistry magnetic field effects magneto-hydrodynamic effect radicals redox cofactors

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An Avida-ED digital evolution curriculum for undergraduate biology

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Evolution: Education and Outreach 2016年第1期9卷 1-11页

作者： Smith, James J. Johnson, Wendy R. Lark, Amy M. Mead, Louise S. Wiser, Michael J. Pennock, Robert T. Michigan State University Lyman Briggs College 919 E. Shaw Lane East Lansing 48825-3804 MI United States Department of Entomology Michigan State University 288 Farm Lane East Lansing 48824 MI United States Department of Integrative Biology Michigan State University 288 Farm Lane East Lansing 48824-1115 MI United States BEACON Center for the Study of Evolution in Action Michigan State University 567 Wilson Road-BPS East Lansing 48824 MI United States Program in Ecology Evolutionary Biology and Behavior Michigan State University East Lansing 48824 MI United States Department of Teacher Education Michigan State University Erickson Hall 620 Farm Lane East Lansing 48824 MI United States Department of Cognitive and Learning Sciences Michigan Technological University 1400 Townsend Drive Houghton 49931 MI United States Department of Philosophy Michigan State University 503 S. Kedzie Hall East Lansing 48824 MI United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East Lansing 48824 MI United States

We present an inquiry-based curriculum based on the digital evolution platform Avida-ED (http://***). We designed an instructional sequence and lab book consisting of an introduction to Avida-ED and a set of three lessons focused on specific evolutionary concepts. These served to familiarize students with experimental evolution and Avida-ED. Students then developed independent Avida-ED research projects to test their own questions. Curriculum design and implementation occurred over the course or two semesters, with a pilot implementation in the first semester, followed by curriculum revision and full implementation in the second semester. The curriculum was implemented in an undergraduate Introductory Cell and Molecular Biology course at a major research university. Full implementation of the curriculum in semester two involved the use of Avida-ED mainly in the teaching lab in parallel with a bacterial antibiotic resistance experimental research stream, allowing students to draw connections between Avidian digital evolution and the evolution of antibiotic resistance in microbial populations. After carrying out the introductory exercises, students developed independent Avida-ED projects to test their own research questions, and presented their data to researchers in the NSF-funded BEACON Center for the Study of Evolution in Action. Preliminary results of our studies to assess the impacts of an Avida-ED curriculum indicate a positive effect on student learning of evolutionary concepts, particularly in increasing the level of complexity of student explanations about the random nature of mutation. © 2016 The Author(s).

关键词： Artificial life Avida Avida-ED Digital evolution Evolution education Introductory biology

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Consciousness in Artificial Intelligence: Insights from the science of Consciousness

arXiv

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Relationships Between Music and Empathic Decision Making in Healthy Young Adults

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Music and science 2021年 4卷

作者： Colverson, Aaron Lamb, Damon Garvan, Cyndi Toh, Kok-Ben Porges, Eric Tremura, Welson Williamson, John Musicology/Ethnomusicology Program School of Music University of Florida United States Center for OCD and Anxiety Related Disorders Department of Psychiatry McKnight Brain Institute College of Medicine University of Florida United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida United States Department of Neuroscience College of Medicine University of Florida United States Department of Biomedical Engineering College of Engineering University of Florida United States Brain Rehabilitation and Research Center Malcom Randall VAMC Gainesville FL United States Department of Anesthesiology Institute of Food and Agricultural Science University of Florida United States Department of Ecology College of Medicine University of Florida United States Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida United States Center for Latin American Studies University of Florida United States

Music and empathy are components of social experience. Similar and adjacent functional brain systems are required in the production and understanding of music, the processing of emotion, and engagement in social behavior. Activity in these brain systems is often reflected in autonomic features, including dynamic behavior of the parasympathetic and sympathetic nervous systems. Music may influence prosocial behavior and this effect may be reflected by the behavior of the autonomic nervous system. This experiment was designed to evaluate these relationships. Healthy undergraduate students (N = 60) participated in Cyberball, a task sensitive to differences in prosocial behavior, while listening to or not listening to different types of music. Results indicated that music positively affects prosocial behavior, but autonomic activity does not reflect the degree of music’s effect on prosocial behavior. © The Author(s) 2021.

关键词： Autonomic nervous system empathic decision making empathy music social engagement

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