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infotheory: A C++/Python package for multivariate information theoretic analysis

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arXiv 2019年

作者： Candadai, Madhavun Izquierdo, Eduardo J. Program in Cognitive Science Indiana University Bloomington United States School of Informatics Computing and Engineering Indiana University Bloomington United States

This paper introduces infotheory: a package written in C++ and usable from Python and C++, for multivariate information theoretic analyses of discrete and continuous data. This package allows the user to study the relationship between components of a complex system simply from the data recorded during its operation, using the tools of information theory. It implements widely used measures such as entropy and mutual information, as well as more recent measures that arise from multivariate extensions to information theory, specifically Partial Information Decomposition. It provides an easy-to-use and flexible tool for use in research as well as pedgogical purposes to introduce students to information theory. Copyright © 2019, The Authors. All rights reserved.

关键词： Information theory

Quantum computing hardware for HEP algorithms and sensing

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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

Automating the Practice of Science – Opportunities, Challenges, and Implications

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Widely Tunable Berry curvature in the Magnetic Semimetal Cr1+δTe2

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arXiv 2022年

作者： Fujisawa, Y. Pardo-Almanza, M. Hsu, C.H. Mohamed, A. Yamagami, K. Krishnadas, A. Chuang, F.C. Khoo, K.H. Zang, J. Soumyanarayanan, A. Okada, Y. Okinawa904-0495 Japan Department of Physics National Sun Yat-Sen University Kaohsiung80424 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Institute of High Performance Computing Agency for Science Technology and Research 138632 Singapore Department of Physics and Astronomy University of New Hampshire DurhamNH03824 United States Materials Science Program University of New Hampshire DurhamNH03824 United States Department of Physics National University of Singapore 117551 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research 138634 Singapore

Magnetic semimetals have increasingly emerged as lucrative platforms hosting spin-based topological phenomena in real- and momentum spaces. Cr1+δTe2 is a self-intercalated magnetic transition metal dichalcogenide (TMD), which exhibits topological magnetism, tunable electron filling, and magnetic frustration etc. While recent studies have explored real-space Berry curvature effects in this material, similar considerations of momentum-space Berry curvature have been lacking. Here, we systematically investigate the electronic structure and transport properties of epitaxial Cr1+δTe2 thin films over a wide range of doping, δ (0.33 - 0.71). Spectroscopic experiments reveal the presence of a characteristic semi-metallic band region near the Brillouin Zone edge, which shows a rigid band like energy shift as a function of δ. Transport experiments show that the intrinsic component of the anomalous Hall effect (AHE) is sizable, and undergoes a sign flip across δ. Finally, density functional theory calculations establish a causal link between the observed doping evolution of the band structure and AHE: the AHE sign flip is shown to emerge from the sign change of the Berry curvature, as the semi-metallic band region crosses the Fermi energy. Our findings underscore the increasing relevance of momentum-space Berry curvature in magnetic TMDs and provide a unique platform for intertwining topological physics in real and momentum spaces. © 2022, CC BY.

关键词： Density functional theory

Nonuniform 3D finite difference elastic wave simulation on staggered grids

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arXiv 2020年

作者： Gao, Longfei Ghattas, Omar Keyes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Oden Institute for Computational Engineering and Sciences Jackson School of Geosciences Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Applied Mathematics and Computational Science Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We present an approach to simulate the 3D isotropic elastic wave propagation using nonuniform finite difference discretization on staggered grids. Specifically, we consider simulation domains composed of layers of uniform grids with different grid spacings, separated by nonconforming interfaces. We demonstrate that this layer-wise finite difference discretization has the potential to significantly reduce the simulation cost, compared to its fully uniform counterpart. Stability of such a discretization is achieved by using specially designed difference operators, which are variants of the standard difference operators with adaptations near boundaries or interfaces, and penalty terms, which are appended to the discretized wave system to weakly impose boundary or interface conditions. Combined with specially designed interpolation operators, the discretized wave system is shown to preserve the energy conserving property of the continuous elastic wave equation, and a fortiori ensure the stability of the simulation. Numerical examples are presented to demonstrate the efficacy of the proposed simulation approach. © 2020, CC BY.

关键词： Elastic waves

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

Exploring Motion Vector Cost with Partial Distortion Elimination in Sum of Absolute Differences for HEVC Integer Motion Estimation

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Exploring Motion Vector Cost with Partial Distortion Elimina...

IEEE International New Circuits and Systems Conference

作者： Brunno Abreu Mateus Grellert Guilherme Paim Thomas Fontanari Leandro M. G. Rocha Eduardo A. C. da Costa Sergio Bampi Graduate Program in Microelectronics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Graduate Program in Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

Integer Motion Estimation (IME) is one of the most costly steps in the video coding process, being responsible for 60% of the total encoding time. Two parameters are considered when calculating the redundancies (distortion) between video blocks: Sum of Absolute Differences (SAD) and motion vector cost (MV_(cost)). Partial Distortion Elimination (PDE) techniques may be used to optimize the calculation of the SAD unit itself, in order to avoid the computation of candidates that will certainly not be selected in the IME. This paper proposes an optimization in the distortion calculation by taking advantage of the PDE technique in SAD operation by including MVcost, in order to save even more cycles when compared to standard PDE usage, considering the High Efficiency Video Coding (HEVC) standard. We found out that the inclusion of MV_(cost) in the distortion cost achieves a quality increase of up to 1.28%. We compare three versions: adding MV_(cost) before/after SAD using a multiplexer, and before SAD with a Carry-Save Adder (CSA). The use of PDE including MV_(cost) before SAD calculation using a multiplexer achieves an average reduction of 17.5% in the energy consumption when compared to accumulating MV_(cost) after SAD. When using the CSA, we obtain an additional average reduction of 1.94%.

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A Comparative Analysis between Particle Swarm Optimization and Mathematical programming for Optimal Location of Maintenance Facilities

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A Comparative Analysis between Particle Swarm Optimization a...

2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Filho, Paulo M. O. Borba, Bruno S. M. C. Ferreira, Vitor H. Tavares, Rafael V. Queiroga, Eduardo Silva, João Marcos P. Uchoa, Eduardo Bergiante, Níssia C. R. Salgado Moreira, Bernardo Coelho MacHado, Pedro Henrique Universidade Federal Fluminense Electrical Engineering Department Niterói Brazil Universidade Federal Fluminense Computing Institute Niterói RJ Brazil Univeridade Federal da Paraíba Industrial Engineering Graduate Program João Pessoa PB Brazil Universidade Federal Fluminense Industrial Engineering Department Niterói RJ Brazil Energisa Distribuição Cuiabá MT Brazil Energisa Tocantins Palmas TO Brazil

ISBN: (纸本)9781538695678

This paper addresses the problem of maintenance facilities (depot) location in electric power distribution systems with a focus on strategic planning. Power interruptions can be harmful for both society and distribution companies. Determining the optimal number and sites of depot location to handle power interruptions in the long term can improve the quality of service. Through a good depot location, the distance from depots to work orders (WO) is minimized, operational costs can be minimized and customer interruption duration is reduced, which is a good indicator of the reliability of the system. The problem was solved by a Particle Swarm Optimization (PSO) algorithm and a Mixed Integer Linear programming (MILP) model. In addition to the real instance, some synthetic ones were generated to compare both approaches in large-sized scenarios. The computational results show that the two approaches are complementary strategies, being presented the scenarios where each proposal could provide better results. © 2019 IEEE.

关键词： Electric power distribution