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Assessing the Needs of the Quantum Industry

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arXiv 2021年

作者： Hughes, Ciaran Finke, Doug German, Dan-Adrian Merzbacher, Celia Vora, Patrick M. Lewandowski, Heather J. Fermi National Accelerator Laboratory BataviaIL60510 United States Quantum Computing Report TustinCA92780 United States Luddy School of Informatics Indiana University BloomingtonIN47408 United States Quantum Science and Engineering Center Indiana University BloomingtonIN47408 United States SRI International 1100 Wilson Blvd Suite 2800 ArlingtonVA22209 United States Department of Physics and Astronomy George Mason University FairfaxVA22030 United States Quantum Science and Engineering Center George Mason University FairfaxVA22030 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States

Quantum information science and technology (QIST) has progressed significantly in the last decade, such that it is no longer solely in the domain of research labs, but is now beginning to be developed for, and applied in, industrial applications and products. With the emergence of this new quantum industry, a new workforce trained in QIST skills and knowledge is needed. To help support education and training of this workforce, universities and colleges require knowledge of the type of jobs available for their students and what skills and degrees are most relevant for those new jobs. Additionally, students need to know how to tailor their degrees to best align with the current needs of the quantum industry. We report on the results from a survey of 57 companies in the quantum industry, with the goal of elucidating the jobs, skills, and degrees that are relevant for this new workforce. We find a range of job opportunities from highly specific jobs, such as quantum algorithm developer and error correction scientist, to broader jobs categories within the business, software, and hardware sectors. These broader jobs require a range of skills, most of which are not quantum related. Further, except for the highly specific jobs, companies that responded to the survey are looking for a range of degree levels to fill these new positions, from bachelors to masters to PhDs. With this knowledge, students, instructors, and university administrators can make informed decisions about how to address the challenge of increasing the future quantum workforce. © 2021, CC BY.

关键词： Surveys

An Approach of Handling Verbal Inflections of Bengali Text: Conversion of Sadhu to Cholito Form of Language

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An Approach of Handling Verbal Inflections of Bengali Text: ...

International Conference on Computer and Information technology (ICCIT)

作者： Mohammad Masudur Rahman Ashraful Islam Md. Faisal Kabir Mohammad Nurul Huda Department of CSE Bangladesh University of Engineering and Technology Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana USA Department of CS North Dakota State University North Dakota USA Department of CSE United International University Dhaka Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

Bengali language follows Subject-Object-Verb (SOV) structure for constructing sentences where verb plays a crucial role. Machine translation for Bengali becomes easier if the verb is identified correctly in a semantically valid sentence. However, Bengali verbs are affected by various complex inflections while using in a sentence. Those inflections are determined according to tense (time of occurring event), person (nature of the subject), Sadhu and Cholito forms (historical evolution of writing styles of Bengali) of language. In this study, we propose an algorithm to identify different inflections of Bengali verbs to retrieve verb roots and present an approach to convert Sadhu to Cholito form of writing Bengali sentences. The methodology shows satisfying accuracy and fluency in the context of language evaluation.

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

Disentangling high-order mechanisms and high-order behaviours in complex systems

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

Common Limitations of Image Processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

Terahertz On-Chip Antenna Based on Metasurface and SIW with Stacked Layers of Resonators on GaAs Substrate 8

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Terahertz On-Chip Antenna Based on Metasurface and SIW with ...

8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Alibakhshikenari, Mohammad Virdee, Bal S. See, Chan H. Fishlock, Sam Jeffery Soin, Navneet McLaughlin, James Abd-Alhameed, Raed A. Limiti, Ernesto University of Rome "tor Vergata" Electronic Engineering Department Rome00133 Italy London Metropolitan University Center for Communications Technology Mathematics School of Computing Digital Media LondonN7 8DB United Kingdom Edinburgh Napier University School of Engineering and the Built Environment EdinburghEH10 5DT United Kingdom BT37 0QB United Kingdom University of Bradford Faculty of Engineering Informatics BradfordBD7 1DP United Kingdom

ISBN: (纸本)9781665400541

This paper presents a 100 μm GaAs-based 0.45-0.50 THz on-chip antenna based on metasurface and substrate integrated waveguide (SIW) technologies to realize a high-performance antenna. The antenna design consists of 2×4 array of metallic patches with circular slot embedded in rectangular resonator segments in a horizontal arrangement. The resonator segments are separated from each other by a narrow channel to create a coplanar waveguide which is used to excite the structure. This antenna structure, which is constructed on GaAs substrate, reduces substrate loss and surface waves. In addition, the metasurface essentially enlarges the effective aperture area of the antenna to enhance the gain and radiation efficiency of the antenna. The dimensions of the metasurface on-chip antenna is 0.8×0.8×0.13 mm3. The antenna exhibits an average gain and efficiency of 6.9dBi and 61.82%, respectively, which makes it a promising candidate for packaging in terahertz components. © 2019 IEEE.

关键词： Resonators

RIS-assisted SATINs with RSMA and DRL: A Trade-off between Spectral, Secrecy, and Energy Efficiency

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IEEE Transactions on Communications 2025年

作者： Wu, Min Guo, Kefeng Li, Xingwang Lin, Zhi Yang, Liang Tsiftsis, Theodoros A. Yuen, Chau Beihang University Institute of Artificial Intelligence Beijing100191 China Beihang University Bejing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China Space Engineering University School of Space Information Beijing101407 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China National University of Defense Technology College of Electronic Engineering China Anhui Province Key Laboratory of Electronic Restriction Hefei230037 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Nanyang Technological University School of Electrical and Electronics Engineering 50 Nanyang Ave 639798 Singapore

Given the rapid growth of diverse communication demands, future large-scale satellite-aerial-terrestrial integrated networks (SATINs) need to simultaneously provide services to users while guaranteeing spectral efficiency, secrecy and energy efficiency. This paper addresses the problem of maximising secrecy energy efficiency (SEE) in SATINs, which can accurately describe the effective trade-off between security, spectral efficiency and transmit power. Particularly, we investigate a secure beamforming (BF) scheme in cognitive SATINs that employs rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) in the presence of multiple eavesdroppers (Eves) in a UAV-aided secondary network (SN). To optimize the SEE for secondary vehicle users while satisfying the constraints of primary users (PUs), we utilize deep reinforcement learning (DRL) to address the coupling between different optimized parameters based on the improved long short-term memory proximal policy optimization (LSTM-PPO) algorithm. The main innovation of this paper is to design a sophisticated reward function, action space, and state space according to each constraint to speed up the convergence. In addition, simulation results show that the proposed DRL-based optimization scheme exhibits significant advantages in terms of SEE compared with benchmark schemes, validating the effectiveness of this work. © 1972-2012 IEEE.

关键词： Reinforcement learning

Amalgamation of Metamaterial and SIW Technologies for Realizing Wide-Bandwidth and High-Radiation Properties of On-Chip Antennas for Application in Packaging of Terahertz Components 8

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Amalgamation of Metamaterial and SIW Technologies for Realiz...

8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

ISBN: (纸本)9781665400541

This paper shows that by employing a combination of metamaterial (MTM) and substrate integrated waveguide (SIW) technologies, the drawbacks of narrow-bandwidth and low radiation properties encountered in terahertz on-chip antennas can be overcome. In addition, an effective feeding mechanism is introduced to excite the on-chip antenna. The proposed antenna is constructed on the three stacked layers comprising Silicon-metal-Silicon substrates. Dimensions of on-chip antenna are 1×1×0.265 mm3. The on-chip antenna is shown to have an average impedance match, gain, and efficiency parameters of-35dB, 8.5dBi, and 67.5%, respectively, over a wide frequency range of 0.20-0.22 THz. © 2019 IEEE.

关键词： Metamaterials

Seamless Mobility Management in Heterogeneous 5G Networks: A Coordination Approach among Distributed SDN Controllers

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Seamless Mobility Management in Heterogeneous 5G Networks: A...

IEEE Vehicular technology Conference

作者： Ali Saeed Dayem Alfoudi S. H. Shah Newaz Rudy Ramlie Gyu Myoung Lee Thar Baker Department of Computer Science Al-Qadisiyah University Iraq KAIST Institute for Information Technology Convergence Daejeon South Korea School of Computing and Informatics Universiti Teknologi Brunei (UTB) Gadong Brunei Darussalam Department of Computer Science Liverpool John Moores University UK

The major objective of evolution towards 5G networks is to support increasing number of end devices with stringent latency and high bandwidth requirements. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are paving the way in supporting such requirements of 5G networks. In this paper, we propose a seamless mobility management for the users when they move from a SDN controller coverage to another in 5G heterogeneous networks. Our proposed solution utilizes the key-value Distributed Hash Table (DHT) to catch users' mobility in a distributed SDN controller to address scalability and seamlessness, where mobile device may join and leave between different associated SDN controllers. The proposed solution allows to select an appropriate AP with cooperation of mobile devices and controllers, so that network performance can be maximized and users' demand can be met in a dynamically changing of network condition. The performance evaluated using OMNeT++ simulator imparts that the solution introduced in this paper can successfully reduce handover latency around 50% compared to the conventional mobility management solutions.

关键词： 5G network LTE WiFi SDN NFV Mobility management