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arXiv 2022年

作者： Dai, X. Trappen, R. Yang, R. Disseler, S.M. Basham, J.I. Gibson, J. Melville, A.J. Niedzielski, B.M. Das, R. Kim, D.K. Yoder, J.L. Weber, S.J. Hirjibehedin, C.F. Lidar, D.A. Lupascu, A. Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Departments of Electrical & Computer Engineering Chemistry and Physics Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Flux tunability is an important engineering resource for superconducting circuits. Large-scale quantum computers based on flux-tunable superconducting circuits face the problem of flux crosstalk, which needs to be accurately calibrated to realize high-fidelity quantum operations. Typical calibration methods either assume that circuit elements can be effectively decoupled and simple models can be applied, or require a large amount of data. Such methods become ineffective as the system size increases and circuit interactions become stronger. Here we propose a new method for calibrating flux crosstalk, which is independent of the underlying circuit model. Using the fundamental property that superconducting circuits respond periodically to external fluxes, crosstalk calibration of N flux channels can be treated as N independent optimization problems, with the objective functions being the periodicity of a measured signal depending on the compensation parameters. We demonstrate this method on a small-scale quantum annealing circuit based on superconducting flux qubits, achieving comparable accuracy with previous methods. We also show that the objective function usually has a nearly convex landscape, allowing efficient optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Crosstalk

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Assessing depression recurrence, cognitive burden, and neurobiological homeostasis in late life: Design and rationale of the REMBRANDT Study

Journal of mood and anxiety disorders

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Journal of mood and anxiety disorders 2024年 5卷 100038页

作者： Warren D Taylor Olusola Ajilore Helmet T Karim Meryl A Butters Robert Krafty Brian D Boyd Layla Banihashemi Sarah M Szymkowicz Claire Ryan Jason Hassenstab Bennett A Landman Carmen Andreescu Center for Cognitive Medicine Department of Psychiatry and Behavioral Science Vanderbilt University Medical Center Nashville TN. Geriatric Research Education and Clinical Center Veterans Affairs Tennessee Valley Health System Nashville TN. Department of Psychiatry University of Illinois-Chicago Chicago IL. Department of Psychiatry University of Pittsburgh Medical Center Pittsburgh PA. Department of Biostatistics and Bioinformatics Emory University Atlanta GA. Departments of Neurology and Psychiatry Washington University in St. Louis St. Louis MO. Departments of Computer Science Electrical Engineering and Biomedical Engineering Vanderbilt University Department of Radiology and Radiological Sciences Vanderbilt University Medical Center.

Background:Late-life depression is characterized by disability, cognitive impairment and decline, and a high risk of recurrence following remission. Aside from past psychiatric history, prognostic neurobiological and clinical factors influencing recurrence risk are unclear. Moreover, it is unclear if cognitive impairment predisposes to recurrence, or whether recurrent episodes may accelerate brain aging and cognitive decline. The purpose of the REMBRANDT study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) is to better elucidate these relationships and identify phenotypic, cognitive, environmental, and neurobiological factors contributing to and predictive of depression recurrence. Methods:Across three sites, REMBRANDT will enroll 300 depressed elders who will receive antidepressant treatment. The goal is to enroll 210 remitted depressed participants and 75 participants with no mental health history into a two-year longitudinal phase focusing on depression recurrence. Participants are evaluated every 2 months with deeper assessments occurring every 8 months, including structural and functional neuroimaging, environmental stress assessments, deep symptom phenotyping, and two weeks of 'burst' ecological momentary assessments to elucidate variability in symptoms and cognitive performance. A broad neuropsychological test battery is completed at the beginning and end of the longitudinal study. Significance:REMBRANDT will improve our understanding of how alterations in neural circuits and cognition that persist during remission contribute to depression recurrence vulnerability. It will also elucidate how these processes may contribute to cognitive impairment and decline. This project will obtain deep phenotypic data that will help identify vulnerability and resilience factors that can help stratify individual clinical risk.

关键词： aging cognition depression longitudinal design methods study design

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iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv

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arXiv 2020年

作者： Fu, Wanyi Sharma, Shobhit Abadi, Ehsan Iliopoulos, Alexandros-Stavros Wang, Qi Lo, Joseph Y. Sun, Xiaobai Segars, William P. Samei, Ehsan Department of Electrical and Computer Engineering Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Physics Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Radiology Fourth Clinical Hospital of Hebei Medical University Heibei050011 China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Departments of Biomedical Engineering Medical Physics Graduate Program and Radiology Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Departments of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University DurhamNC27705 United States

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed Copyright © 2020, The Authors. All rights reserved.

关键词： Phantoms

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Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation

arXiv

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arXiv 2021年

作者： Ross, Shane D. Fish, Jeremie Moeltner, Klaus Bollt, Erik M. Bilyeu, Landon Fanara, Tracy Aerospace and Ocean Engineering Virginia Tech BlacksburgVA United States Electrical and Computer Engineering C3S2 the Clarkson Center for Complex Systems Science Clarkson University ClarksonNY United States Agricultural and Applied Economics Virginia Tech BlacksburgVA United States School of Plant and Environmental Sciences Virginia Tech BlacksburgVA United States Mote Marine Laboratory and Aquarium SarasotaFL United States National Oceanic and Atmospheric Administration WashingtonDC United States

An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011-2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches. © 2021, CC BY.

关键词： Beaches

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Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning

Classification of Cognitive Load and Expertise for Adaptive ...

引用

International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Pritam Sarkar Kyle Ross Aaron J. Ruberto Dirk Rodenbura Paul Hungler Ali Etemad Department of Electrical and Computer Engineering Departments of Emergency Medicine and Critical Care Medicine Faculty of Engineering and Applied Science Queen's University Kingston Canada

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train different learners regardless of their existing skills, expertise, and ability to handle cognitive load. To address this problem, we propose an end-to-end framework for a trauma simulation that actively classifies a participant's level of cognitive load and expertise for the development of a dynamically adaptive simulation. To facilitate this solution, trauma simulations were developed for the collection of electrocardiogram (ECG) signals of both novice and expert practitioners. A multitask deep neural network was developed to utilize this data and classify high and low cognitive load, as well as expert and novice participants. A leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness of our model, achieving an accuracy of 89.4% and 96.6% for classification of cognitive load and expertise, respectively.

关键词： Load modeling Electrocardiography Feature extraction Adaptation models Task analysis Neural networks Electromyography

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General dynamic neural networks for explainable PID parameter tuning in control engineering: An extensive comparison

arXiv

引用

arXiv 2019年

作者： Günther, Johannes Reichensdörfer, Elias Pilarski, Patrick M. Diepold, Klaus Departments of Computing Science and Medicine University of Alberta Canada Department of Electrical and Computer Engineering Technical University of Munich Germany

Automation, the ability to run processes without human supervision, is one of the most important drivers of increased scalability and productivity in technology. Modern automation largely relies on forms of closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. Despite an increase in the use of machine learning for process control, most deployed controllers still are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but are not robust enough to be appropriate for more complex controlled processes. As a main contribution of the present paper, we examine the utility of extending standard PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN);we show that GDNN (neural) PID controllers perform well on a range of complex control systems and highlight what is needed to make them a stable, scalable, and interpretable option for modern control systems. To do so, we provide a comprehensive study using four different benchmark processes. All four control environments are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second main contribution of this work, we address the Achilles heel that prevents neural networks from being used in real-world control processes so far: lack of interpretability. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, thus making them understandable for human engineers. This combination of rigorous evaluation paired with better explainability is an important step towards the acceptance of neural-network-based control approaches for real-world systems. It is furthermore an important step towards explainable and safe applied artificial intelligence. Co

关键词： Controllers

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

arXiv

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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

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Two-phase heat transfer phenomena in a natural circulation loop during LOHS

引用

AIP Conference Proceedings 2022年第1期2501卷

作者： Solomon Bello Puzhen Gao Samuel A. Olatubosun OdunAyo Imoru Dominic Osoro 1Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Harbin 150001 China 2Nigeria Atomic Energy Commission Abuja 900231 Nigeria 3Departments of Mechanical and Aerospace Engineering The Ohio State University Columbus OH 43210 USA 4Department of Electrical & Computer Engineering University of Namibia (JEDS Campus) Ongwediva Namibia

The secondary loop (SL) of a Natural Circulation (NC) System serves as the coolant supply channel to the Heat Exchanger (HX) for effective passive heat removal by the NC system. Insufficient coolant or total loss of coolant along the cold leg of the SL will lead to a loss of heat sink accident (LOHS). This phenomenon negatively affects the heat removal capability of the HX. The experiment has now been carried out to investigate the loss of secondary loop coolant as it affects flow characteristics in the primary system of a natural circulation system under two-phase conditions carried out in a well-scaled down dimensioned NC loop having a single heated channel. Induced power of 9.0 KW and a 0.5 MPa system pressure were predetermined and maintained. Transient was activated by shutting down the inlet of the secondary loop (cold leg) and then NC flow behavior was observed in each test section along the flow centered on the predetermined conditions. When HX outlet temperature upsurges to around its inlet temperature, data recording is halted, and the experiment is brought to an end. The acquired data were analyzed at the inlet and outlet of both the heated sections and the HX in various flow regimes. While the transient scenario was activated, there exists transient stability in the NC loop with temperature increasing steadily and consequently descents into the instability region. Thereafter, NC flow drips into a stable two-phase flow, and subsequently, outlet temperature surges, and flow loses stability and purges into the two-phase instability region leading to CHF occurrence. This experiment is useful for more understanding of NC fluid behavior and further exploitation of the close NC rectangular loop in the event of LOHS accident.

关键词：

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Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning

arXiv

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arXiv 2019年

作者： Sarkar, Pritam Ross, Kyle Ruberto, Aaron J. Rodenburg, Dirk Hungler, Paul Etemad, Ali Department of Electrical and Computer Engineering Departments of Emergency Medicine and Critical Care Medicine Faculty of Engineering and Applied Science Department of Chemical Engineering Queen’s University Kingston Canada

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train different learners regardless of their existing skills, expertise, and ability to handle cognitive load. To address this problem, we propose an end-to-end framework for a trauma simulation that actively classifies a participant’s level of cognitive load and expertise for the development of a dynamically adaptive simulation. To facilitate this solution, trauma simulations were developed for the collection of electrocardiogram (ECG) signals of both novice and expert practitioners. A multitask deep neural network was developed to utilize this data and classify high and low cognitive load, as well as expert and novice participants. A leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness of our model, achieving an accuracy of 89.4% and 96.6% for classification of cognitive load and expertise, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrocardiography

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