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arXiv 2022年

作者： Chung, Joaquin Eastman, Ely M. Kanter, Gregory S. Kapoor, Keshav Lauk, Nikolai Peña, Cristián Plunkett, Robert Sinclair, Neil Thomas, Jordan M. Valivarthi, Raju Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States The University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt Llc 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metropolitan scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-Nodes) representing the communication parties in a quantum network. Q-Nodes generate or measure quantum signals such as entangled photons and communicate the measurement results via standard, classical signals and conventional networking processes. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via transparent optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages softwaredefined networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-Nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely implemented in the classical domain. We also discuss the IEQNET processes that address issues associated with synchronization, calibration, network monitoring, and scheduling. An important goal of IEQNET is to demonstrate the extent to which the control plane classical signals can co-propagate with the data plane quantum signals in the same fiber lines (quantum-classical signal "coexistence"). This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Corrigendum to “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models” [Robotics and Autonomous Systems Volume 157, November 2022, 104234]

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Robotics and Autonomous Systems 2024年 178卷

作者： Arodh Lal Karn Sudhakar Sengan Ketan Kotecha Irina V Pustokhina Denis A Pustokhin V Subramaniyaswamy Dharam Buddhi Department of Financial and Actuarial Mathematics School of Science Xi'an Jiaotong-Liverpool University China Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune Maharashtra 412115 India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics 117997 Moscow Russia Department of Logistics State University of Management 109542 Moscow Russia School of Computing SASTRA Deemed University Thanjavur 613401 Tamil Nadu India Division of Research & Innovation Uttaranchal University Uttarakhand 248007 Dehradun India

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Open hardware solutions in quantum technology

APL Quantum

引用

APL Quantum 2024年第1期1卷

作者： Nathan Shammah Anurag Saha Roy Carmen G. Almudever Sébastien Bourdeauducq Anastasiia Butko Gustavo Cancelo Susan M. Clark Johannes Heinsoo Loïc Henriet Gang Huang Christophe Jurczak Janne Kotilahti Alessandro Landra Ryan LaRose Andrea Mari Kasra Nowrouzi Caspar Ockeloen-Korppi Guen Prawiroatmodjo Irfan Siddiqi William J. Zeng Unitary Fund San Francisco California 94104 USA Qruise GmbH 66113 Saarbruecken Germany Technical University of Valencia Valencia Spain M-Labs Limited North Point Hong Kong Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Sandia National Laboratories Albuquerque New Mexico 87123 USA IQM Quantum Computers Espoo 02150 Finland PASQAL 7 rue Leonard de Vinci 91300 Massy France Accelerator Technology and Applied Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Quantonation 75010 Paris France Center for Quantum Computing Science and Engineering Michigan State University East Lansing Michigan 48824 USA Microsoft Quantum Redmond Washington 98052 USA

Quantum technologies, such as communication, computing, and sensing, offer vast opportunities for advanced research and development. While an open-source ethos currently exists within some quantum technologies, especially in quantum computer programming, we argue that there are additional advantages in developing open quantum hardware (OQH). Open quantum hardware encompasses open-source software for the control of quantum devices in labs, blueprints, and open-source toolkits for chip design and other hardware components, as well as openly accessible testbeds and facilities that allow cloud-access to a wider scientific community. We provide an overview of current projects in the OQH ecosystem, identify gaps, and make recommendations on how to close them at present. More open quantum hardware would accelerate technology transfer to and growth of the quantum industry and increase accessibility in science.

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On integrals involving quotients of hyperbolic functions

arXiv

引用

arXiv 2019年

作者： Dar, S.A. Paris, R.B. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia New Delhi110025 India Division of Computing and Mathematics Abertay University DundeeDD1 1HG United Kingdom

We evaluate some integrals over [0,∞) of quotients of powers of the hyperbolic functions sinh x and cosh x using a hypergeometric approach. Some of these results appear to be new but several verify the entries in the table of integrals of Gradshteyn and *** Codes 33C05, 33C20, 44A10, 33B15, 68N30 Copyright © 2019, The Authors. All rights reserved.

关键词： Hyperbolic functions

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Nektar++: Enhancing the capability and application of high-fidelity spectral/hp element methods

引用

Computer Physics Communications 2020年第0期249卷 107110-000页

作者： Moxey, David Cantwell, Chris D. Bao, Yan Cassinelli, Andrea Castiglioni, Giacomo Chun, Sehun Juda, Emilia Kazemi, Ehsan Lackhove, Kilian Marcon, Julian Mengaldo, Gianmarco Serson, Douglas Turner, Michael Xu, Hui Peiró, Joaquim Kirby, Robert M. Sherwin, Spencer J. College of Engineering Mathematics and Physical Sciences University of Exeter United Kingdom Department of Aeronautics Imperial College London United Kingdom Department of Civil Engineering Shanghai Jiao Tong University Shanghai China Underwood International College Yonsei University Korea Republic of School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China Department of Energy and Power Plant Technology Technische Universität Darmstadt Germany Division of Engineering and Applied Science. California Institute of Technology United States Scientific Computing and Imaging Institute University of Utah United States

Nektar++ is an open-source framework that provides a flexible, high-performance and scalable platform for the development of solvers for partial differential equations using the high-order spectral/hp element method. In particular, Nektar++ aims to overcome the complex implementation challenges that are often associated with high-order methods, thereby allowing them to be more readily used in a wide range of application areas. In this paper, we present the algorithmic, implementation and application developments associated with our Nektar++ version 5.0 release. We describe some of the key software and performance developments, including our strategies on parallel I/O, on in situ processing, the use of collective operations for exploiting current and emerging hardware, and interfaces to enable multi-solver coupling. Furthermore, we provide details on a newly developed Python interface that enables a more rapid introduction for new users unfamiliar with spectral/hp element methods, C++ and/or Nektar++. This release also incorporates a number of numerical method developments – in particular: the method of moving frames (MMF), which provides an additional approach for the simulation of equations on embedded curvilinear manifolds and domains;a means of handling spatially variable polynomial order;and a novel technique for quasi-3D simulations (which combine a 2D spectral element and 1D Fourier spectral method) to permit spatially-varying perturbations to the geometry in the homogeneous direction. Finally, we demonstrate the new application-level features provided in this release, namely: a facility for generating high-order curvilinear meshes called NekMesh;a novel new AcousticSolver for aeroacoustic problems;our development of a ‘thick’ strip model for the modelling of fluid–structure interaction (FSI) problems in the context of vortex-induced vibrations (VIV). We conclude by commenting on some lessons learned and by discussing some directions for future code developmen

关键词： Numerical methods

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette 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 Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems 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 Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal 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 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 Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Certain results on Euler-type integrals and their applications

arXiv

引用

arXiv 2019年

作者： Jabee, S. Shadab, M. Paris, R.B. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia University New Delhi110025 India Division of Computing and Mathematics Abertay University DundeeDD1 1HG United Kingdom

This paper deals with the evaluation of some definite Euler-type integrals in terms of the Wright hypergeometric function. We obtain a theorem on the Wright hypergeometric function and then use this theorem to evaluate some definite integrals. Further, we derive some results as applications of these evaluations. Multi-variable cases of the derived results of this paper are also briefly discussed.33C20, 33E12, 33C05, 33B99 Copyright © 2019, The Authors. All rights reserved.

关键词： Function evaluation

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Novel deep hybrid model for electricity price prediction based on dual decomposition

引用

applied Energy 2025年 395卷

作者： Sujan Ghimire Thong Nguyen-Huy Ravinesh C. Deo David Casillas-Pérez A.A. Masrur Ahmed Sancho Salcedo-Sanz Artificial Intelligence Applications Laboratory School of Mathematics Physics and Computing University of Southern Queensland Springfield QLD 4300 Australia Centre for Applied Climate Sciences University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Information Technology Thanh Do University Kim Chung Hoai Duc Ha Noi 100000 Viet Nam Department of Signal Processing and Communications Universidad Rey Juan Carlos Fuenlabrada 28942 Madrid Spain Science Economics and Insights Division Department of Climate Change Energy the Environment and Water NSW Government Sydney 2141 New South Wales Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares 28805 Madrid Spain

Electricity price ( E P ) forecasting is vital for effective market operation, strategic planning, and risk management in deregulated energy systems. However, the inherent volatility and complexity of electricity prices, shaped by demand supply dynamics, weather variability, and regulatory interventions, pose substantial challenges to accurate prediction. This study introduces a novel hybrid framework designed to improve forecasting accuracy by leveraging both signal decomposition and deep learning techniques. Specifically, the method integrates Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) for noise reduction and feature extraction, followed by a Multi Resolution Convolution (MRC) layer and a Bidirectional Long Short Term Memory (BiLSTM) network to capture multiscale temporal patterns in electricity price data. The model is applied to half hourly electricity price data from South Australia spanning January 2018 to December 2022. Its performance is benchmarked against a suite of traditional and hybrid models using a comprehensive set of twelve evaluation metrics. The results reveal that the proposed hybrid model consistently outperforms all baselines across seasons and forecast horizons. Notably, during the spring period, it achieved a Normalized Root Mean Square Error of ≈ 4.87 % , a Mean Absolute Percentage Error of ≈ 12.09 % , and a Global Performance Index of ≈ 3.22 . These improvements demonstrate the model’s ability to effectively handle the non-linear and nonstationary nature of E P . Overall, the findings underscore the potential of combining advanced decomposition methods with deep learning architectures to deliver more accurate and robust E P forecasts, thereby offering valuable support for decision making in complex and evolving energy markets.

关键词： 0000 1111 0000 1111 Deep learning Convolutional neural network Variational mode decomposition Empirical wavelet transform Residual connection Bayesian optimization

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Quantum hamiltonian-based models & the variational quantum thermalizer algorithm

arXiv

引用

arXiv 2019年

作者： Verdon, Guillaume Marks, Jacob Nanda, Sasha Leichenauer, Stefan Hidary, Jack Moonshot Factory Mountain ViewCA United States Institute for Quantum Computing University of Waterloo ON Canada Department of Applied Mathematics University of Waterloo ON Canada Department of Physics Stanford University StanfordCA United States Division of Physics Mathematics and Astronomy California Institute of Technology CA United States

We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning, where we efficiently decompose the tasks of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition, we introduce the Variational Quantum Thermalizer (VQT) for generating the thermal state of a given Hamiltonian and target temperature, a task for which QHBMs are naturally well-suited. The VQT can be seen as a generalization of the Variational Quantum Eigensolver (VQE) to thermal states: we show that the VQT converges to the VQE in the zero temperature limit. We provide numerical results demonstrating the efficacy of these techniques in illustrative examples. We use QHBMs and the VQT on Heisenberg spin systems, we apply QHBMs to learn entanglement Hamiltonians and compression codes in simulated free Bosonic systems, and finally we use the VQT to prepare thermal Fermionic Gaussian states for quantum simulation. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

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Nonlinear multigrid based on local spectral coarsening for heterogeneous difiusion problems

arXiv

引用

arXiv 2020年

作者： Leea, Chak Shing Hamon, Francois Castelletto, Nicola Vassilevski, Panayot S. White, Joshua Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Total EandP Research and Technology HoustonTX77002 United States Atmospheric Earth and Energy Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Energy Resources Engineering Stanford University StanfordCA94305 United States Fariborz Maseeh Department of Mathematics and Statistics Portland State University PortlandOR97201 United States

This work develops a nonlinear multigrid method for difiusion problems discretized by cell-centered finite volume methods on general unstructured grids. The multigrid hierarchy is constructed algebraically using aggregation of degrees of freedom and spectral decomposition of reference linear operators associated with the aggregates. For rapid convergence, it is important that the resulting coarse spaces have good approximation properties. In our approach, the approximation quality can be directly improved by including more spectral degrees of freedom in the coarsening process. Further, by exploiting local coarsening and a piecewise-constant approximation when evaluating the nonlinear component, the coarse level problems are assembled and solved without ever re-visiting the fine level, an essential element for multigrid algorithms to achieve optimal scalability. Numerical examples comparing relative performance of the proposed nonlinear multigrid solvers with standard single-level approaches-Picard's and Newton's methods-are presented. Results show that the proposed solver consistently outperforms the single-level methods, both in effciency and robustness. Copyright © 2020, The Authors. All rights reserved.

关键词： Finite volume method

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