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SSRN 2019年

作者： Tosh, Deepak K. Shetty, Sachin Sengupta, Shamik Kesan, Jay P. Kamhoua, Charles A. Dept. of Computer Science Norfolk State University VA United States Virginia Modeling Analysis and Simulation Center Old Dominion University VA United States Dept. of Computer Science and Engineering University of Nevada RenoNV United States College of Law University of Illinois Urbana ChampaignIL United States Cyber Assurance Branch Air Force Research Laboratory RomeNY United States

Critical infrastructure systems spanning from transportation to nuclear operations are vulnerable to cyber attacks. Cyber-insurance and cyber-threat information sharing are two prominent mechanisms to defend cybersecurity issues proactively. However, standardization and realization of these choices have many bottlenecks. In this paper, we discuss the benefits and importance of cybersecurity information sharing and cyber-insurance in the current cyber-warfare situation. We model a standard game theoretic participation model for cybersecurity information exchange (CYBEX) and discuss the applicability of economic tools in addressing important issues related to CYBEX and cyber-insurance. We also pose several open research challenges, which need to be addressed for developing a robust cyber-risk management capability. © 2019, The Authors. All rights reserved.

关键词： Cybersecurity

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Adjoint-based optimization to minimize scattered pressure fields

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The Journal of the Acoustical Society of America 2020年第4_SUPPLEMENT期148卷 2637-2637页

作者： Benjamin C. Treweek Timothy Walsh Clay M. Sanders Wilkins Aquino Samuel D. Parker Michael R. Haberman Computational Solid Mech. & Structural Dynam. Sandia National Labs. P.O. Box 5800 Albuquerque NM 87185 btrewee@sandia.gov Simulation Modeling Sci. Sandia National Labs. Albuquerque NM Dept. of Civil and Environ. Eng. Duke Univ. Durham NC Dept. of Mech. Eng. Univ. of Texas at Austin Austin TX

Acoustic and elastic metamaterials have shown potential for many different applications in wave manipulation and structural isolation. Minimization of a scattered pressure field has been identified as an important use for such materials, and a wide variety of designs have been proposed for this purpose. Several of these designs are difficult to manufacture (e.g., pentamode designs), and many others involve numerous tunable parameters and a large design parameter space (e.g., distributions of resonators). This presents a challenge for global search-based optimization and raises the need for gradient-based methods. In this work, adjoint-based, PDE-constrained optimization is used to improve the feasibility of minimizing a scattered pressure field via an optimal set of channels in an annulus surrounding a rigid scatterer. Two different optimization strategies are investigated: material identification, where the physical properties of each channel are treated as unknown, and force identification, where the magnitude and phase of the reflected field is treated as unknown at various patches on the annulus. Results are presented for both a single plane wave direction and multiple plane wave directions, as well as for both a single frequency and multiple frequencies. [S.N.L. is managed and operated by NTESS under DOE NNSA Contract No. DE-NA0003525.]

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Deep Learning for Image Sequence Classification of Astronomical Events

arXiv

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arXiv 2018年

作者： Carrasco-Davis, Rodrigo Cabrera-Vives, Guillermo Förster, Francisco Estévez, Pablo A. Huijse, Pablo Protopapas, Pavlos Reyes, Ignacio Martínez-Palomera, Jorge Donoso, Cristóbal Dept. of Electrical Engineering Universidad de Chile Dept. of Computer Science Universidad de Concepción Informatics Institute Universidad Austral de Chile Dept. of Astronomy Universidad de Chile Institute for Applied Computational Science Harvard University Center for Mathematical Modeling Universidad de Chile Millennium Institute of Astrophysics Chile

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference images. This is the first time that sequences of images are used directly for the classification of variable objects in astronomy. The second contribution of this work is the image simulation process. We generate synthetic image sequences that take into account the instrumental and observing conditions, obtaining a realistic, unevenly sampled, and variable noise set of movies for each astronomical object. The simulated dataset is used to train our RCNN classifier. This approach allows us to generate datasets to train and test our RCNN model for different astronomical surveys and telescopes. Moreover, using a simulated dataset is faster and more adaptable to different surveys and classification tasks. We aim at building a simulated dataset whose distribution is close enough to the real dataset, so that a fine tuning could match the distributions and solve the domain adaptation problem. To test the RCNN classifier trained with the synthetic dataset, we used real-world data from the High cadence Transient Survey (HiTS) obtaining an average recall of 85%, improved to 94% after performing fine tuning with 10 real samples per class. We compare the results of our RCNN model with those of a light curve random forest classifier. The proposed RCNN with fine tuning has a similar performance on the HiTS dataset compared to the light curve random forest classifier, trained on an augmented training set with 10 real samples per class. The RCNN approach presents several advantages in an alert stream classification scenario, such as a reduction of the data pre-processing, faster online evaluation and easier performance improvement using a few real data samples. The results obtained encourage us to use the proposed method for astronomical alert bro

关键词： Recurrent neural networks

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Fault tolerant variants of the fine-grained parallel incomplete LU factorization 25

Fault tolerant variants of the fine-grained parallel incompl...

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25th High Performance Computing Symposium, HPC 2017, Part of the 2017 Spring simulation Multi-Conference, SpringSim 2017

作者： Coleman, Evan Sosonkina, Masha Chow, Edmond Naval Surface Warfare Center Dahlgren Division 17320 Dahlgren Rd DahlgrenVA United States Dept. of Modeling Simulation and Visualization Engineering Old Dominion University 5115 Hampton Blvd NorfolkVA United States School of Computational Science and Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States

ISBN: (纸本)9781510838222

This paper presents an investigation into fault tolerance for the fine-grained parallel algorithm for computing an incomplete LU factorization. Results concerning the convergence of the algorithm with respect to the occurrence of faults, and the impact of any sub-optimality in the produced incomplete factors in Krylov subspace solvers are given. Numerical tests show that the simple algorithmic changes suggested here can ensure convergence of the fine-grained parallel incomplete factorization, and improve the performance of the use of the resulting factors as preconditioners in Krylov subspace solvers if faults do occur. ©2017 Society for modeling & simulation International (SCS).

关键词： Fault tolerance

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FAULT TOLERANT VARIANTS OF THE FINE-GRAINED PARALLEL INCOMPLETE LU FACTORIZATION 17

FAULT TOLERANT VARIANTS OF THE FINE-GRAINED PARALLEL INCOMPL...

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simulation Multiconference

作者： Evan Coleman Masha Sosonkina Edmond Chow Naval Surface Warfare Center Dahlgren Division Dept. of Modeling Simulation and Visualization Engineering Old Dominion University School of Computational Science and Engineering Georgia Institute of Technology

ISBN: (纸本)9781510838222

关键词： Fault tolerance Parallel preconditioning Incomplete factorization GPU acceleration Fault tolerance factorization Lutetium Converge faults Solvers Subspace

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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-dept. 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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Acoustic metasurfaces as sound diffusers and diffsorbers

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The Journal of the Acoustical Society of America 2020年第4_SUPPLEMENT期148卷 2576-2576页

作者： Janghoon Kang Samuel D. Parker Joe Skeens Benjamin C. Treweek Timothy Walsh Michael R. Haberman Walker Dept. of Mech. Eng. The Univ. of Texas at Austin Austin TX Mech. Eng. Univ. of Texas at Austin Austin TX Aerosp. Eng. and Eng. Mech. The Univ. of Texas at Austin Austin TX Computational Solid Mech. & Structural Dynam. Sandia National Labs. Albuquerque NM Simulation Modeling Sci. Sandia National Labs. Albuquerque NM Walker Dept. Mech. Eng. and Appl. Res. Labs. The Univ. of Texas at Austin 10000 Burnet Rd. Austin TX 78758 haberman@utexas.edu

Acoustic metasurfaces (AMS) may offer additional design space for the creation of sound diffusers and diffsorbers. We present the design and experimental characterization of an AMS created to diffuse sound fields using a treatment of uniform thickness. The AMS unit cell designs were chosen to minimize viscous losses while enabling control of reflected phase at each point of the AMS. The design geometry was then optimized with a parametric sweep of unit cell dimensions using finite element (FE) models where the target phase shifts are associated with a one- and two-dimensional quadratic residue diffuser (QRD). Samples were fabricated using additive manufacturing and impedance tube measurements were conducted to validate FE predictions. Modeled and measured reflection coefficients were shown to be in good agreement. The scattered field was then measured in an anechoic chamber using the logarithmic frequency modulated chirp excitation and the deconvolution methods to provide narrowband and broadband estimates of sound diffusion performance. Overall performance is presented for narrow-band and broadband considerations. The AMS response is compared and contrasted with traditional QRD designs, highlighting differences in frequency- and angle-dependence as well as temporal diffusion and absorption. [S.N.L. is managed and operated by NTESS under DOE NNSA Contract No. DE-NA0003525.]

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A characterization of cybersecurity simulation scenarios 19

A characterization of cybersecurity simulation scenarios

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19th Communications and Networking Symposium, CNS 2016, Part of the 2016 Spring simulation Multi-Conference, SpringSim 2016

作者： Kavak, Hamdi Padilla, Jose J. Vernon-Bido, Daniele Dept. of Modeling Simulation and Visualization Engineering Old Dominion University 1300 Eng. and Comp. Sciences NorfolkVA23529 United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Boulevard SuffolkVA23435 United States

ISBN: (纸本)9781510824256

The rate and sophistication of cyber-attacks are ever increasing forcing organizations to test their systems and assess their risks for specific situations (e.g., data breach). simulation is an emerging field in testing and assessing cybersecurity risk as it allows modeling cyber systems, their interdependencies, and interactions between cyber systems and people who are using, policing, and even attacking these systems. With cybersecurity scenarios, we help describe and formulate these complex interdependencies and relationships. In this paper, we first characterize cybersecurity scenarios along (1) the nature of cyber systems with considerations for design and (2) the type of actor with considerations of abilities. This characterization provides a more clear distinction compared to military oriented LVC (Live-Virtual-Constructive) simulation characterization. We then review examples from the literature based on our characterization. According to our review, we note that cyber system representation has been widely explored while actor representation is often realized at the technical-level dismissing social and cognitive aspects. Thus, we believe that paying more attention to social and cognitive aspects of actor representation, especially as developing general-purpose tools, will benefit the modeling and simulation community. © 2016 Society for modeling & simulation International (SCS).

关键词： Cybersecurity

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Towards modeling factors that enable an attacker 48

Towards modeling factors that enable an attacker

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48th Summer Computer simulation Conference, SCSC 2016, Part of the 2016 Summer simulation Multi-Conference, SummerSim 2016

作者： Daniele, Vernon-Bido Padilla, Jose J. Diallo, Saikou Y. Kavak, Hamdi Gore, Ross J. Dept. of Modeling Simulation and Visualization Engineering Old Dominion University 1300 Eng. and Comp. Sciences NorfolkVA23529 United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Boulevard SuffolkVA23435 United States

ISBN: (纸本)9781510824256

This paper presents an initial baseline model of factors that give rise to an attacker. It explores the impact of successful rate of attack and social learning on the decision to become an attacker. The model utilizes rational choice theory, routine activity theory, social learning theory, and perceived behavioral control from the theory of planned behavior to examine factors that moves an individual from user to attacker. An agent-based model is used to depict the effect of interactions of users and attacks of all users., successful and unsuccessful, have on an individual's decision to become an attacker. Preliminary results suggest that opportunity has a stronger influence than the rate of successful attacks or the size of the associated group. © 2016 Society for modeling & simulation International (SCS).

关键词： Cybersecurity

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Using simulation games for teaching & learning discrete-event simulation

Using simulation games for teaching & learning discrete-even...

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2016 Winter simulation Conference, WSC 2016

作者： Padilla, Jose J. Lynch, Christopher J. Diallo, Saikou Y. Gore, Ross J. Barraco, Anthony Kavak, Hamdi Jenkins, Bakari Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd. SuffolkVA23435 United States Modeling Simulation and Visualization Engineering Dept. Old Dominion University 1300 Eng. and Computational Sciences Building NorfolkVA23529 United States Pruden Center for Industry and Technology 4169 Pruden Blvd. SuffolkVA23434 United States

ISBN: (纸本)9781509044863

Capturing and retaining the attention of students while learning complex topics like modeling and simulation is a critical task. In discrete-event simulation (DES), educators rely on examples like queueing systems in fast food restaurants or manufacturing organizations to provide the necessary context for learning. In many instances, these examples fall short in capturing the attention of students, especially at the middle and high school levels. One approach for learning complex topics, like creating simulations, is through gaming. This paper reports on the creative use of regular simulation tools to develop simulation games with entertainment content aimed towards engaging young learners. Two games are presented: one focuses on the use of decision nodes while the second focuses on the use of batch/separator nodes. As part of future work, we propose to use these games to evaluate how much knowledge transfers from an entertainment context to one using simulations for real-life situations. © 2016 IEEE.

关键词： Discrete event simulation

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