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Opportunities for nuclear physics & quantum information science

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arXiv 2019年

作者： Cloët, Ian C. Dietrich, Matthew R. Cloët, Ian C. Dietrich, Matthew R. Arrington, John Bazavov, Alexei Bishof, Michael Freese, Adam Gorshkov, Alexey V. Grassellino, Anna Hafidi, Kawtar Jacob, Zubin McGuigan, Michael Meurice, Yannick Meziani, Zein-Eddine Mueller, Peter Muschik, Christine Osborn, James Otten, Matthew Petreczky, Peter Polakovic, Tomas Poon, Alan Pooser, Raphael Roggero, Alessandro Saffman, Mark VanDevender, Brent Zhang, Jiehang Zohar, Erez Physics Division Argonne National Laboratory ArgonneIL60439 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Joint Quantum Institute NIST/University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST/University of Maryland College ParkMD20742 United States Fermi National Accelerator Laboratory Batavia IL60510 United States Northwestern University EvanstonIL60208 United States Birck Nanotechnology Center and Purdue Quantum Center School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo West Waterloo ON Canada ALCF Argonne National Laboratory ArgonneIL60439 United States Nanoscience and Technology Division Argonne National Laboratory ArgonneIL60439 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Physics and Material Science Divisions Argonne National Laboratory ArgonneIL60439 United States Institute for Nuclear and Particle Astrophysics and Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Information Science Group Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Institute for Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics University of Wisconsin MadisonWI53706 United States Pacific Northwest National Laboratory RichlandWA99354 United States Max Planck Institute of Quantum Opti

This whitepaper is an outcome of the workshop Intersections between Nuclear Physics and Quantum Information held at Argonne National laboratory on 28-30 March 2018 [***/npqi2018/]. The workshop brought together 116 national and international experts in nuclear physics and quantum information science to explore opportunities for the two fields to collaborate on topics of interest to the U.S. department of Energy (DOE) Office of science, Office of Nuclear Physics, and more broadly to U.S. society and industry. The workshop consisted of 22 invited and 10 contributed talks, as well as three panel discussion sessions. Topics discussed included quantum computation, quantum simulation, quantum sensing, nuclear physics detectors, nuclear many-body problem, entanglement at collider energies, and lattice gauge theories. Copyright © 2019, The Authors. All rights reserved.

关键词： Lattice theory

Transformative role of photovoltaics in phasing out alternating current based grid by local DC power networks for sustainable global economic growth 44

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Transformative role of photovoltaics in phasing out alternat...

44th IEEE Photovoltaic Specialist Conference, PVSC 2017

作者： Singh, Rajendra Asif, Amir A. Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

ISBN: (纸本)9781509056057

For sustainable global economic growth, eradication of global energy poverty and addressing climate challenges, free fuel based solar and wind energy sources are the only viable solution for electricity generation. Due to inherent advantages, photovoltaics has emerged as the major source of electrical power. Local generation of direct current (DC) power by PV and the use of batteries for storing DC electrical power have the potential of transforming global electricity infrastructure to address the problems faced by alternating current (AC) based centralized power generation and long haul transmission and distribution. © 2017 IEEE.

关键词： Electric power transmission networks

Stealthy malware traffic - not as innocent as it looks

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arXiv 2017年

作者： Zhong, Xingsi Fu, Yu Yu, Lu Brooks, Richard Kumar Venayagamoorthy, G. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Malware is constantly evolving. Although existing countermeasures have success in malware detection, corresponding counter-countermeasures are always emerging. In this study, a counter-countermeasure that avoids network-based detection approaches by camouflaging malicious traffic as an innocuous protocol is presented. The approach includes two steps: Traffic format transformation and side-channel massage (SCM). Format-transforming encryption (FTE) translates protocol syntax to mimic another innocuous protocol while SCM obscures traffic side-channels. The proposed approach is illustrated by transforming Zeus botnet (Zbot) Command and Control (C&C) traffic into smart grid Phasor Measurement Unit (PMU) data. The experimental results show that the transformed traffic is identified by Wireshark as synchrophasor protocol, and the transformed protocol fools current side-channel attacks. Moreover, it is shown that a real smart grid Phasor Data Concentrator (PDC) accepts the false PMU data. Copyright © 2017, The Authors. All rights reserved.

关键词： Phasor measurement units

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Optimal Planar Electric Dipole Antennas

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arXiv 2018年

作者： Capek, Miloslav Jelinek, Lukas Schab, Kurt Gustafsson, Mats Jonsson, B.L.G. Ferrero, Fabien Ehrenborg, Casimir Department of Electromagnetic Field Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague16627 Czech Republic Electrical Engineering Department Santa Clara University 500 El Camino Real Santa ClaraCA95053 United States Department of Electrical and Information Technology Lund University Box 118 LundSE-221 00 School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm100 44 Sweden Laboratory of Electronics Antennas and Telecommunications Cnrs Universite Cote D'Azur Sophia Antipolis06903 France

Considerable time is often spent optimizing antennas to meet specific design metrics. Rarely, however, are the resulting antenna designs compared to rigorous physical bounds on those metrics. Here we study the performance of optimized planar meander line antennas with respect to such bounds. Results show that these simple structures meet the lower bound on radiation Q-factor (maximizing single resonance fractional bandwidth), but are far from reaching the associated physical bounds on efficiency. The relative performance of other canonical antenna designs is compared in similar ways, and the quantitative results are connected to intuitions from small antenna design, physical bounds, and matching network design. Copyright © 2018, The Authors. All rights reserved.

关键词： Dipole antennas

On buildings that compute. A proposal

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arXiv 2018年

作者： Adamatzky, Andrew Szacilowski, Konrad Przyczyna, Dawid Konkoli, Zoran Sirakoulis, Georgios C. Werner, Liss C. Unconventional Computing Laboratory UWE Bristol United Kingdom AGH University of Science and Technology Academic Centre for Materials and Nanotechnology Kraków Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Chalmers University of Technology Department of Microtechnology and Nanoscience Göthenburg Sweden Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute of Architecture Technical University of Berlin Germany

We present ideas aimed at bringing revolutionary changes on architectures and buildings of tomorrow by radically advancing the technology for the building material concrete and hence building components. We propose that by using nanotechnology we could embed computation and sensing directly into the material used for construction. Intelligent concrete blocks and panels advanced with stimuli-responsive smart paints are the core of the proposed architecture. In particular, the photo-responsive paint would sense the buildings internal and external environment while the nano-materialconcrete composite material would be capable of sensing the building environment and implement massive-parallel information processing resulting in distributed decision making. A calibration of the proposed materials with in-materio suitable computational methods and corresponding building in-formation modelling, computer-aided design and digital manufacturing tools could be achieved via models and prototypes of information processing at nano-level. The emergent technology sees a building as high-level massiveparallel computer - assembled of computing concrete blocks. Based on the generic principles of neuromorphic computation and reservoir computing we envisage a single building or an urban quarter to turn into a large-scale sensing substrate. It could behave as a universal computer, collecting and processing environmental information in situ enabling appropriate data fusion. The broad range of spatio-temporal effects include infrastructural and human mobility, energy, bio-diversity, digital activity, urban management, art and socializing, robustness with regard to damage and noise or real-time monitoring of environmental changes. The proposed intelligent architectures will increase sustainability and viability in digitised urban environments by decreasing information transfer bandwidth by e.g, utilising 5G networks. The emergence of socio-cultural effect will create a cybernetic relationsh

关键词： Decision making

Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

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Event reconstruction using pattern spectra and convolutional...

7th Heidelberg International Symposium on High-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Breen, S. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

Large-Scale object detection of images from network cameras in variable ambient lighting conditions

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arXiv 2018年

作者： Tung, Caleb Kelleher, Matthew R. Schlueter, Ryan J. Xu, Binhan Lu, Yung-Hsiang Thiruvathukal, George K. Chen, Yen-Kuang Lu, Yang Purdue University School of Electrical and Computer Engineering West LafayetteIN United States Loyola University Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Argonne Leadership Computing Facility ArgonneIL United States Intel Corporation Santa ClaraCA United States Facebook Corporation Menlo ParkCA United States

computer vision relies on labeled datasets for training and evaluation in detecting and recognizing objects. The popular computer vision program, YOLO ("You Only Look Once"), has been shown to accurately detect objects in many major image datasets. However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e.g. ambient lighting) changes. This paper describes a novel effort to evaluate YOLO's consistency for large-scale applications. It does so by working (a) at large scale and (b) by using consecutive images from a curated network of public video cameras deployed in a variety of real-world situations, including traffic intersections, national parks, shopping malls, university campuses, etc. We specifically examine YOLO's ability to detect objects in different scenarios (e.g., daytime vs. night), leveraging the cameras' ability to rapidly retrieve many successive images for evaluating detection consistency. Using our camera network and advanced computing resources (supercomputers), we analyzed more than 5 million images captured by 140 network cameras in 24 hours. Compared with labels marked by humans (considered as "ground truth"), YOLO struggles to consistently detect the same humans and cars as their positions change from one frame to the next;it also struggles to detect objects at night time. Our findings suggest that state-of-the art vision solutions should be trained by data from network camera with contextual information before they can be deployed in applications that demand high consistency on object detection. Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection

Laser damage attack against optical attenuators in quantum key distribution

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arXiv 2019年

作者： Huang, Anqi Li, Ruoping Egorov, Vladimir Tchouragoulov, Serguei Kumar, Krtin Makarov, Vadim Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Greatwall Quantum Laboratory China Greatwall Technology Changsha410205 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Faculty of Photonics and Optical Information ITMO University 199034 Kadetskaya line 3b St. Petersburg Russia QGLex Incorporated 105 Schneider Road Suite 111 OttawaONK2K 1Y3 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

Many quantum key distribution systems employ a laser followed by an optical attenuator to prepare weak coherent states in the source. Their mean photon number must be pre-calibrated to guarantee the security of key distribution. Here we experimentally show that this calibration can be broken with a high-power laser attack. We have tested four fiber-optic attenuator types used in quantum key distribution systems, and found that two of them exhibit a permanent decrease in attenuation after laser damage. This results in higher mean photon numbers in the prepared states and may allow an eavesdropper to compromise the key. Copyright © 2019, The Authors. All rights reserved.

关键词： Photons