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Symplectic lattice gauge theories in the grid framework: Approaching the conformal window

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Physical Review D 2023年第9期108卷 094508-094508页

作者： Ed Bennett Peter A. Boyle Luigi Del Debbio Niccolò Forzano Deog Ki Hong Jong-Wan Lee Julian Lenz C.-J. David Lin Biagio Lucini Alessandro Lupo Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University (Bay Campus) Fabian Way SA1 8EN Swansea Wales United Kingdom School of Physics and Astronomy University of Edinburgh Edinburgh EH9 3FD United Kingdom Physics Department Brookhaven National Laboratory Upton New York 11973 USA Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh Peter Guthrie Tait Road Edinburgh EH9 3FD United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Korea Institute for Extreme Physics Pusan National University Busan 46241 Korea Particle Theory and Cosmology Group Center for Theoretical Physics of the Universe Institute for Basic Science (IBS) Daejeon 34126 Korea Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Centre for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University (Bay Campus) Fabian Way SA1 8EN Swansea Wales United Kingdom Centre for Mathematical Sciences University of Plymouth Plymouth PL4 8AA United Kingdom

Symplectic gauge theories coupled to matter fields lead to symmetry enhancement phenomena that have potential applications in such diverse contexts as composite Higgs, top partial compositeness, strongly interacting dark matter, and dilaton-Higgs models. These theories are also interesting on theoretical grounds, for example in reference to the approach to the large-N limit. A particularly compelling research aim is the determination of the extent of the conformal window in gauge theories with symplectic groups coupled to matter, for different groups and for field content consisting of fermions transforming in different representations. Such determination would have far-reaching implications, but requires overcoming huge technical challenges. Numerical studies based on lattice field theory can provide the quantitative information necessary to this endeavor. We developed new software to implement symplectic groups in the Monte Carlo algorithms within the Grid framework. In this paper, we focus most of our attention on the Sp(4) lattice gauge theory coupled to four (Wilson-Dirac) fermions transforming in the 2-index antisymmetric representation, as a case study. We discuss an extensive catalog of technical tests of the algorithms and present preliminary measurements to set the stage for future large-scale numerical investigations. We also include the scan of parameter space of all asymptotically free Sp(4) lattice gauge theories coupled to varying number of fermions transforming in the antisymmetric representation.

关键词： Lattice field theory Lattice gauge theory Monte Carlo methods

Why is the Winner the Best?

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Why is the Winner the Best?

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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A Compact Scheme for Coupled Stochastic Nonlinear Schrodinger Equations

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Communications in computational Physics 2017年第1期21卷 93-125页

作者： Chuchu Chen Jialin Hong Lihai Ji Linghua Kong Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Institute of Applied Physics and ComputationalMathematics Beijing 100094China School of Mathematics and Information Science Jiangxi Normal UniversityNanchangJiangxi 330022China

In this paper,we propose a compact scheme to numerically study the coupled stochastic nonlinear Schrodinger *** prove that the compact scheme preserves the discrete stochastic multi-symplectic conservation law,discrete charge conservation law and discrete energy evolution law almost *** experiments confirm well the theoretical analysis ***,we present a detailed numerical investigation of the optical phenomena based on the compact *** numerical experiments for various amplitudes of noise,we find that the noise accelerates the oscillation of the soliton and leads to the decay of the solution amplitudes with respect to *** particular,if the noise is relatively strong,the soliton will be totally ***,we observe that the phase shift is sensibly modified by the ***,the numerical results present inelastic interaction which is different from the deterministic case.

关键词： Coupled stochastic nonlinear Schrodinger equations compact scheme stochastic multi-symplectic conservation law energy evolution law charge conservation law soliton evolution soliton interaction

How “backtest overfitting” in finance leads to false discoveries

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Significance 2021年第6期18卷

作者： David H. Bailey Marcos López de Prado Recently retired from the Lawrence Berkeley National Laboratory where he was a senior scientist. He is a research associate at the University of California Davis and has published over 300 research articles in the fields of high-performance scientific computing computational mathematics and financial mathematics. Professor of practice at Cornell University's School of Engineering in New York NY USA and global head of quantitative research and development at the Abu Dhabi Investment Authority the sovereign wealth fund of Abu Dhabi UAE.

Financial investment strategies are often designed and tested using historical market data. But this can frequently give rise to “optimal” strategies that are statistical mirages and perform poorly out in the real w...

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Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

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arXiv 2019年

作者： Shaik, Feroz H. Lange, Jacob Field, Scott E. O'Shaughnessy, Richard Varma, Vijay Kidder, Lawrence E. Pfeiffer, Harald P. Wysocki, Daniel Department of Physics Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Center for Computational Relativity and Gravitation Rochester Institute of Technology RochesterNY14623 United States Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts Dartmouth DartmouthMA02747 United States Theoretical Astrophysics California Institute of Technology PasadenaCA91125 United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Am Mühlenberg 1 Potsdam14476 Germany

Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently-developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with detector-frame masses and signal amplitudes consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. For asymmetric binaries with mass ratios q > 1, higher modes are critical, and their omission usually produces substantial parameter biases. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

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arXiv 2020年

作者： Papamarkou, Theodore Guy, Hayley Kroencke, Bryce Miller, Jordan Robinette, Preston Schultz, Daniel Hinkle, Jacob Pullum, Laura Schuman, Catherine Renshaw, Jeremy Chatzidakis, Stylianos Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Mathematics North Carolina State University RaleighNC United States Department of Computer Science University of California DavisCA United States Center for Cognitive Ubiquitous Computing Arizona State University TempeAZ United States Presbyterian College ClintonSC United States Innovative Computing Laboratory University of Tennessee KnoxvilleTN United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN United States Electric Power Research Institute Palo AltoCA United States Reactor and Nuclear Systems Division Oak Ridge National Laboratory Oak RidgeTN United States

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

On the incorporation of obstacles in a fluid flow problem using a Navier-Stokes-Brinkman penalization approach

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arXiv 2020年

作者： Fuchsberger, Jana Karabelas, Elias Aigner, Philipp Niederer, Steven Plank, Gernot Schima, Heinrich Haase, Gundolf Institute of Mathematics and Scientific Computing University of Graz Heinrichstraße 36 GrazA-8010 Austria Gottfried Schatz Research Center for Cell Signaling Metabolism and Aging Biophysics Medical University of Graz Neue Stiftingtalstraße 6/D04 GrazA-8010 Austria Department of Biomedical Engineering School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom Center for Medical Physics and Biomedical Engineering Medical University Vienna Währinger Gürtel 18-20 ViennaA-1090 Austria Ludwig Boltzmann Institute for Cardiovascular Research Währinger Guertel 18-20 ViennaA-1090 Austria BioTechMed-Graz Graz Austria

Simulating the interaction of fluids with immersed moving solids is playing an important role for gaining a better quantitative understanding of how fluid dynamics is altered by the presence of obstacles and, vice versa, which forces are exerted on the solids by the moving fluid. Such problems appear in various contexts, ranging from numerous technical applications such as e.g. turbines to medical problems such as the regulation of cardiovascular hemodyamics by valves. Typically, the numerical treatment of such problems is posed within a fluid structure interaction (FSI) framework. General FSI models are able to capture bidirectional interactions, but are challenging to solve and computationally expensive. Simplified methods offer a possible remedy by achieving better computational efficiency to broaden the scope to demanding application problems with focus on understanding the effect of solids on altering fluid dynamics. In this study we report on the development of a novel method for such applications. In our method rigid moving obstacles are incorporated in a fluid dynamics context using concepts from porous media theory. Based on the Navier-Stokes-Brinkman equations which augments the Navier-Stokes equation with a Darcy drag term our method represents solid obstacles as time-varying regions containing a porous medium of vanishing permeability. Numerical stabilization and turbulence modeling is dealt with by using a residual based variational multiscale (RBVMS) formulation. The additional Darcy drag term and its respective stabilization are easily accomodated in any existing finite-element based Navier-Stokes solver. The key advantages of our approach - computational efficiency and ease of implementation - are demonstrated by solving a standard benchmark problem of a rotating blood pump posed by the Food and Drug Administration Agency (FDA). Validity is demonstrated by conducting a mesh convergence study and by comparison against the extensive set of experimental

关键词： Large eddy simulation

A Family of-Stable Optimized Hybrid Block Methods for Integrating Stiff Differential Systems

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Mathematical Problems in engineering 2022年第1期2022卷

作者： Rajat Singla Gurjinder Singh Higinio Ramos V. Kanwar Department of Mathematical Sciences I. K. Gujral Punjab Technical University Jalandhar Main Campus Kapurthala-144603 Punjab India Department of Mathematics Akal University Bathinda-151302 India Scientific Computing Group Universidad de Salamanca Plaza de la Merced 37008 Salamanca Spainusal.es Escuela Politécnica Superior de Zamora Campus Viriato 49022 Zamora Spain University Institute of Engineering and Technology Panjab University Chandigarh-160014 Indiapuchd.ac.in

In this article, a family of one-step hybrid block methods having two intrastep points is developed for solving first-order initial value stiff differential systems that occur frequently in science and engineering. In each method of the family, an intrastep point controls the order of the main method and a second one has a control over the stability features of the method. The approach used to develop the class of -stable methods is based on interpolation and collocation procedures. The methods exhibit hybrid nature and produce numerical solutions at several points simultaneously. These methods can also be formulated as Runge-Kutta (RK) methods. Comparisons between the RK and block formulations of the proposed methods reveal a better performance of the block formulation in terms of computational efficiency. Furthermore, the efficiency of the methods is improved when they are formulated as adaptive step-size solvers using an error-control approach. Some methods of the proposed class have been tested to solve some well-known stiff differential systems. The numerical experiments show that the proposed family of methods performs well in comparison with some of the existing methods in the scientific literature.

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Electron spin mediated distortion in metallic systems

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arXiv 2019年

作者： Anand, G. Eisenbach, Markus Goodall, Russell Freeman, Colin L. Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology-Shibpur Howrah WB India Department of Materials Science and Engineering University of Sheffield United Kingdom Warwick Centre for Predictive Modelling University of Warwick United Kingdom Scientific Computing Group Centre for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States

The deviation of positions of atoms from their ideal lattice sites in crystalline solid state systems causes distortion and can lead to variation in structural [1] and functional properties [2]. Distortion in molecular systems has been traditionally understood in the term of Jahn-Teller distortion [3], while for the one-dimensional chain of metals, the Peierls distortion mechanism has been proposed [4]. In real three dimensional metallic systems, a fundamental description of the distortion is missing, which we need to design alloys with attractive structural properties. The present investigation presents the evolution of distortion in metallic systems in terms of magnetovolume effects arising due to magnetic ground-state of the system. Particularly the significant distortion in the case of Cr due to presence of other transition metals is seen. Cr with transition metals as a candidate system to study the correlation between charge disproportion, spin-flip, magnetovolume effects and Fermi surface nesting. This study provides a novel explanation of strengthening effect of Cr observed in alloys due to its unique magnetic properties. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic moments