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Spatial computing: Concept, Applications, Challenges and Future Directions

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

Artificial intelligence advances for de novo molecular structure modeling in cryo-EM

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arXiv 2021年

作者： Si, Dong Nakamura, Andrew Tang, Runbang Guan, Haowen Hou, Jie Firozi, Ammaar Cao, Renzhi Hippe, Kyle Zhao, Minglei Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States Molecular Engineering and Sciences Institute University of Washington Seattle SeattleWA98105 United States Applied and Computational Math Sciences University of Washington Seattle SeattleWA98195 United States Department of Computer Science Saint Louis University Saint. LouisMO63103 United States Department of Computer Science Pacific Lutheran University TacomaWA98447 United States Department of Biochemistry and Molecular Biology University of Chicago ChicagoIL60637 United States

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field. © 2021, CC BY-NC-ND.

关键词： Electron microscopes

Parkinson's Disease Diagnosis Using Machine Learning and Voice

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Parkinson's Disease Diagnosis Using Machine Learning and Voi...

2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018

作者： Wroge, Timothy J. Özkanca, Yasin Demiroglu, Cenk Si, Dong Atkins, David C. Ghomi, Reza Hosseini Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Engineering Özyeǧin University Istanbul Turkey Division of Computing and Software Systems University of Washington SeattleWA United States Department of Psychiatry and Behavioral Sciences University of Washington SeattleWA United States

ISBN: (纸本)9781538659168

Biomarkers derived from human voice can offer in-sight into neurological disorders, such as Parkinson's disease (PD), because of their underlying cognitive and neuromuscular function. PD is a progressive neurodegenerative disorder that affects about one million people in the the United States, with approximately sixty thousand new clinical diagnoses made each year [1]. Historically, PD has been difficult to quantity and doctors have tended to focus on some symptoms while ignoring others, relying primarily on subjective rating scales [2]. Due to the decrease in motor control that is the hallmark of the disease, voice can be used as a means to detect and diagnose PD. With advancements in technology and the prevalence of audio collecting devices in daily lives, reliable models that can translate this audio data into a diagnostic tool for healthcare professionals would potentially provide diagnoses that are cheaper and more accurate. We provide evidence to validate this concept here using a voice dataset collected from people with and without PD. This paper explores the effectiveness of using supervised classification algorithms, such as deep neural networks, to accurately diagnose individuals with the disease. Our peak accuracy of 85% provided by the machine learning models exceed the average clinical diagnosis accuracy of non-experts (73.8%) and average accuracy of movement disorder specialists (79.6% without follow-up, 83.9% after follow-up) with pathological post-mortem examination as ground truth [3]. © 2018 IEEE.

关键词： Diagnosis

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

BrainGrid+Workbench: High-performance/high-quality neural simulation

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BrainGrid+Workbench: High-performance/high-quality neural si...

2017 International Joint Conference on Neural Networks, IJCNN 2017

作者： Stiber, Michael Kawasaki, Fumitaka Davis, Delmar B. Asuncion, Hazeline U. Lee, Jewel Yun-Hsuan Boyer, Destiny Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell BothellWA98011 United States

ISBN: (纸本)9781509061815

Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. This paper describes a new neural simulation and software/data provenance framework that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulations results are valid. © 2017 IEEE.

关键词： Computer software

Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

Rapid, quantitative therapeutic screening for alzheimer's enzymes enabled by optimal signal transduction with transistors

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arXiv 2019年

作者： Le, Son T. Morris, Michelle A. Cardone, Antonio Guros, Nicholas B. Klauda, Jeffery B. Sperling, Brent A. Richter, Curt A. Pant, Harish C. Balijepalli, Arvind Alternative Computing Group Nanoscale Device Characterization Division National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research La JollaCA92037 United States Biophysics Group Microsystems and Nanotechnology Division National Institute of Standards and Technology GaithersburgMD20899 United States Information Systems Group Software and Systems Division National Institute of Standards and Technology GaithersburgMD20899 United States University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Department of Chemical and Biomolecular Engineering University of Maryland College ParkMD20742 United States Chemical Process and Nuclear Measurements Group Chemical Sciences Division National Institute of Standards and Technology GaithersburgMD20899 United States National Institute of Neurological Disorders and Stroke National Institutes of Health BethesdaMD20892 United States

We show that simple, commercially sourced n-channel silicon field-effect transistors (nFETs) operating under closed loop control exhibit an;«3-fold improvement in pH readout resolution to (7.2±0.3)×10-3at a bandwidth of 10 Hz when compared with the open loop operation commonly employed by integrated ion-sensitive field-effect transistors (ISFETs). We leveraged the improved nFET performance to measure the change in solution pH arising from the activity of a pathological form of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, and showed quantitative agreement with previous measurements. The improved pH resolution was realized while the devices were operated in a remote sensing configuration with the pH sensing element off-chip and connected electrically to the FET gate terminal. We compared these results with those measured by using a custom-built dual-gate 2D field-effect transistor (dg2DFET) fabricated with 2D semi-conducting MoS2 channels and a moderate device gain, a=8. Under identical solution conditions the pH resolution of the nFETs was only 2-fold worse than the dg2DFETs pH resolution of (3.9±0.7)×10-3. Finally, using the nFETs, we demonstrated the effectiveness of a custom polypeptide, p5, as a therapeutic agent in restoring the function of Cdk5. We expect that the straight-forward modifications to commercially sourced nFETs demonstrated here will lower the barrier to widespread adoption of these remote-gate devices and enable sensitive bioanalytical measurements for high throughput screening in drug discovery and precision medicine applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Metals

CP2K: An electronic structure and molecular dynamics software package quickstep: Efficient and accurate electronic structure calculations

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arXiv 2020年

作者： Kühne, Thomas D. Iannuzzi, Marcella Ben, Mauro Del Rybkin, Vladimir V. Seewald, Patrick Stein, Frederick Laino, Teodoro Khaliullin, Rustam Z. Schütt, Ole Schiffmann, Florian Golze, Dorothea Wilhelm, Jan Chulkov, Sergey Bani-Hashemian, Mohammad Hossein Weber, Valéry Borštnik, Urban Taillefumier, Mathieu Jakobovits, Alice Shoshana Lazzaro, Alfio Pabst, Hans Müller, Tiziano Schade, Robert Guidon, Manuel Andermatt, Samuel Holmberg, Nico Schenter, Gregory K. Hehn, Anna Bussy, Augustin Belleflamme, Fabian Tabacchi, Gloria Glöß, Andreas Lass, Michael Bethune, Iain Mundy, Christopher J. Plessl, Christian Watkins, Matt VandeVondele, Joost Krack, Matthias Hutter, Jürg Dynamics of Condensed Matter and Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry University of Zurich Winterthurerstrasse 190 ZürichCH-8057 Switzerland Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States IBM Research - Zurich Säumerstrasse 4 RüschlikonCH-8803 Switzerland Department of Chemistry McGill University CH-801 Sherbrooke St. West MontrealQCH3A 0B8 Canada Department of Materials ETH Zürich ZürichCH-8092 Switzerland Centre of Policy Studies Victoria University Melbourne Australia Department of Applied Physics Aalto University Otakaari 1 EspooFI-02150 Finland Institute of Theoretical Physics University of Regensburg Universitätsstraße 31 RegensburgD-93053 Germany School of Mathematics and Physics University of Lincoln Brayford Pool Lincoln United Kingdom Integrated Systems Laboratory ETH Zürich ZürichCH-8092 Switzerland Scientific IT Services ETH Zürich Zürich Switzerland ETH Zürich HPE Switzerland GmbH Basel Switzerland Intel Extreme Computing Software and Systems Zürich Switzerland Department of Computer Science and Paderborn Center for Parallel Computing Paderborn University Warburger Str. 100 PaderbornD-33098 Germany Department of Chemistry and Materials Science Aalto University P.O. Box 16100 Aalto00076 Finland Physical Science Division Pacific Northwest National Laboratory P. O. Box 999 RichlandWA99352 United States Department of Science and High Technology University of Insubria INSTM via Valleggio 9 ComoI-22100 Italy BASF SE Carl-Bosch-Straße 38 Ludwigshafen am RheinD-67056 Germany Hartree Centre Science and Technology Facilities Council United Kingdom Laboratory for Scientific Computing and Modelling Paul Scherrer Institute Villigen PSICH-5232 Switzerland

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic structure

BrainGrid+Workbench: High-Performance/High-Quality Neural Simulation

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BrainGrid+Workbench: High-Performance/High-Quality Neural Si...

International Joint Conference on Neural Networks

作者： Michael Stiber Fumitaka Kawasaki Delmar B. Davis Hazeline U. Asuncion Jewel Yun-Hsuan Lee Destiny Boyer Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell Bothell Washington 98011

ISBN: (纸本)9781509061839

关键词： Graphics processing units Brain modeling Neurons Data models Computational modeling Hardware