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arXiv 2022年

作者： Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Blühdorn, Johannes Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Eikeland, Viljar Feofilov, Gregory Garth, Christoph Gauger, Nicolas R. Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Kortus, Tobias Kusch, Lisa Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Pochampalli, Rohit Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Sagebaum, Max Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Zillien, Sebastian Chair for Scientific Computing University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway University of Applied Sciences Worms Worms Germany Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary FSN Department ENEA Frascati Research Center Frascati00044 Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

引用

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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Impacts of prior parameter distributions on Bayesian evaluation of groundwater model complexity

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Water Science and Engineering 2018年第2期11卷 89-100页

作者： Saeideh Samani Ming Ye Fan Zhang Yong-zhen Pei Guo-ping Tang Ahmed Elshall Asghar A.Moghaddam Department of Earth Sciences University of Tabriz Department of Scientific Computing Florida State University School of Computer Science and Software Engineering Tianjin Polytechnic University Department of Earth Ocean and Atmospheric Science Florida State University Key Laboratory of Tibetan Environment Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences Environmental Sciences Division Oak Ridge National Laboratory

This study used the marginal likelihood and Bayesian posterior model probability for evaluation of model complexity in order to avoid using over-complex models for numerical simulations. It focused on investigation of the impacts of prior parameter distributions(involved in calculating the marginal likelihood) on the evaluation of model complexity. We argue that prior parameter distributions should define the parameter space in which numerical simulations are made. New perspectives on the prior parameter distribution and posterior model probability were demonstrated in an example of groundwater solute transport modeling with four models, each simulating four column experiments. The models had different levels of complexity in terms of their model structures and numbers of calibrated parameters. The posterior model probability was evaluated for four cases with different prior parameter distributions. While the distributions substantially impacted model ranking, the model ranking in each case was reasonable for the specific circumstances in which numerical simulations were made. For evaluation of model complexity, it is thus necessary to determine the parameter spaces for modeling, which can be done by conducting numerical simulation and usineg engineering judgment based on understanding of the system being studied.

关键词： Marginal likelihood Posterior model probability Advection-dispersion equation Mobile-immobile model Groundwater model

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Efficient parallel simulation of blood flows in abdominal aorta

arXiv

引用

arXiv 2019年

作者： Qin, Shanlin Chen, Rongliang Wu, Bokai Liu, Jia Shiu, Wen-Shin Yan, Zhengzheng Cai, Xiao-Chuan Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Computer Science University of Colorado BoulderCO80309-0430 United States

It is known that the maximum diameter for the rupture-risk assessment of the abdominal aortic aneurysm is a generally good method, but not sufficient. Alternative features obtained with computational modeling may provide additional useful criteria. Though computational approaches are noninvasive, they are often time-consuming because of the high computational complexity. In this paper, we present a highly parallel algorithm for the numerical simulation of unsteady blood flows in the patient-specific abdominal aorta. We model the blood flow with the unsteady incompressible Navier-Stokes equations, and solve the discretized system with a highly scalable domain decomposition method. With this approach, the complete flow field can be obtained in less than an hour, instead of days with older methods. We show experimentally that the proposed method offers accurate solutions of the pressure, the velocity and the wall shear stress, and the parallel efficiency is higher than 70% on a parallel computer with more than 1, 000 processor cores. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

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A Sampling-Based Framework for Transductive Classification in Information Networks

A Sampling-Based Framework for Transductive Classification i...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Brucce N. dos Santos Ricardo M. Marcacini Solange O. Rezende Institute of Mathematical and Computer Sciences University of Sao Paulo Sao Carlos SP Brazil Laboratory of Scientific Computing (LivES) Federal University of Mato Grosso do Sul Tres Lagoas MS Brazil Institute of Mathematical and Computer Sciences University of Sao Paulo S??o Carlos SP Brazil

Knowledge extraction from large information networks has received increasing attention in recent years. Among existing methods for knowledge extraction, transductive classification is a well-known semi-supervised learning method, where both labeled and unlabeled vertices are used in the learning process. However, transductive classification tasks become impractical in large information networks and the use of network sampling techniques in the transductive classification setting is not a trivial task, since it is required that all the vertices of the original network be classified during the transductive learning - and not only the vertices of the sample. In this paper, we present a framework called TCSN (Transductive Classification for Sampled Networks). TCSN allows the use of various network sampling techniques, as well as enables the use of various methods of transductive classification for information networks. We present a variation of the Chernoff Bounds method to calculate the minimum size of a sampled network, thereby bounding sampling error within a pre-specified tolerance level. Moreover, TCSN extends the concept of evidence accumulation to combine the results of several rounds of transductive classification into a final classification. Experimental results from different information networks reveals that TCSN statistically outperformed the classification performance in the whole original network. These promising results show that the TCSN enables transductive classification in large information networks without loss of quality in the knowledge extraction process.

关键词： Knowledge acquisition Feature extraction Pattern classification Semisupervised learning Sampling methods

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Analytical gradients and nonadiabatic couplings for the state-average density matrix renormalization group self-consistent field method

arXiv

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arXiv 2019年

作者： Freitag, Leon Ma, Yingjin Baiardi, Alberto Knecht, Stefan Reiher, Markus ETH Zürich Laboratorium für Physikalische Chemie Vladimir-Prelog-Weg 2 Zürich8093 Switzerland Department of High Performance Computing Computer Network Information Center Chinese Academy of Sciences Beijing100190 China Center of Scientific Computing Applications & Research Chinese Academy of Sciences Beijing100190 China

We present analytical gradients and nonadiabatic couplings for a state-average density matrix renormalization group self-consistent-field (SA-DMRG-SCF) wavefunction. Our formalism follows closely the state-average complete active space self-consistent-field (SA-CASSCF) ansatz, which employs a Lagrangian, and the corresponding Lagrange multipliers are obtained from a solution of the coupled-perturbed CASSCF (CP-CASSCF) equations. We introduce a definition of the MPS Lagrange multipliers based on the mixed-canonical form of the MPS, such that the sweep procedure is avoided in the solution of the CP-CASSCF equations. We employ our implementation for the optimization of a conical intersection in 1,2-dioxetanone, where we are able to fully reproduce the SA-CASSCF result up to arbitrary accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Lagrange multipliers

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DRIFT-PRESERVING NUMERICAL INTEGRATORS FOR STOCHASTIC HAMILTONIAN SYSTEMS

arXiv

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arXiv 2019年

作者： Chen, Chuchu Cohen, David D'Ambrosio, Raffaele Lang, Annika State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Mathematics and Mathematical Statistics Umeå University Umeå90187 Sweden Department of Engineering and Computer Science and Mathematics University of L'Aquila AQ L'Aquila Italy Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Göteborg41296 Sweden

The paper deals with numerical discretizations of separable nonlinear Hamiltonian systems with additive noise. For such problems, the expected value of the total energy, along the exact solution, drifts linearly with time. We present and analyze a time integrator having the same property for all times. Furthermore, strong and weak convergence of the numerical scheme along with efficient multilevel Monte Carlo estimators are studied. Finally, extensive numerical experiments illustrate the performance of the proposed numerical *** Codes 65C20. 65C30. 60H10. 60H35 Copyright © 2019, The Authors. All rights reserved.

关键词： Additive noise

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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

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scientific reports 2025年第1期15卷 19929页

作者： Shailendra Bhandari Pedro Lencastre Rujeena Mathema Alexander Szorkovszky Anis Yazidi Pedro G Lind Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. shailendra.bhandari@oslomet.no. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. shailendra.bhandari@oslomet.no. Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo 0164 Norway. Department of Informatics University of Oslo Oslo Norway. School of Economics Innovation and Technology Kristiania University of Applied Sciences Kirkegata 24-26 N-0153 Oslo Norway.

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM-trained under two conditions: using only adversarial loss ([Formula: see text]) and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs' ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds

关键词： Eye-gaze trajectories Generative Adversarial Networks Hidden Markov Models Stochastic Processes

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Middle East and North African Health Informatics Association (MENAHIA)

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Yearbook of Medical Informatics 2021年第1期30卷 328-334页

作者： Mahmood Tara Taweel Adel Alhuwail Dari Dena A. Al Thani Eiman Al-Jafar Eman Abu Hamra Hassan Ghazal Hoda Wahba Kambiz Bahaadinbeigy Khalid AlKhalifa Leena Joseph Mohamed Zeroug Mohammad Bani Odeh Mohammed I. Alhefzi Mounir Hamdi Mowafa Househ Najeeb Al-Shorbaji Osama El Hassan Salim Abdullah Al-Salmi Tamer Amara Zoubir Sari Fellow of the International Academy of Health Sciences Informatics Member of the Iranian Medical Informatics Association Department of Medical Informatics Mashhad University of Medical Sciences Mashhad Iran Department of Computer Science Birzeit University Birzeit Palestine Department of Information Science College of Life Sciences Kuwait University Kuwait Health Informatics Association Kuwait Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Master of Public Health - Al Quds University Palestine Moroccan Society for Telemedicine and eHealth Morocco & National Center for Scientific and Technical Research Rabat Morocco Ain Shams University Virtual Hospital Cairo Egypt President of the Iranian Medical Informatics Association Health Information Technology Department Kerman University of Medical Sciences Kerman Iran National Health Informatics Committee Supreme Council of Health Manama Bahrain Oman College of Health Sciences Ministry of Health Muscat Oman Société Algérienne de Télémédecine et de eSanté SATeS Algeria Palestine National Institute of Public Health Ramallah Palestine Vice President Saudi Association for Health Informatics Saudi Arabia College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Fellow of the International Academy of Health Sciences Informatics Hamad Bin Khalifa University Doha Qatar Fellow of the International Academy of Health Sciences Informatics President of the eHealth Development Association Amman Jordan Health Informatics & Smart Health Department Dubai Health Authority Dubai UAE Ain Sham University Virtual Hospital Cairo Egypt

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Perspectives on adaptive dynamical systems

arXiv

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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