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Reduction of oscillator dynamics on complex networks to dynamics on complete graphs through virtual frequencies

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Physical Review E 2020年第2期101卷 022302-022302页

作者： Jian Gao Konstantinos Efstathiou Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen P.O. Box 407 9700 AK Groningen The Netherlands Division of Natural and Applied Sciences and Zu Chongzhi Center for Mathematics and Computational Science Duke Kunshan University No. 8 Duke Avenue Kunshan 215316 China

We consider the synchronization of oscillators in complex networks where there is an interplay between the oscillator dynamics and the network topology. Through a remarkable transformation in parameter space and the introduction of virtual frequencies we show that Kuramoto oscillators on annealed networks, with or without frequency-degree correlation, and Kuramoto oscillators on complete graphs with frequency-weighted coupling can be transformed to Kuramoto oscillators on complete graphs with a rearranged, virtual frequency distribution and uniform coupling. The virtual frequency distribution encodes both the natural frequency distribution (dynamics) and the degree distribution (topology). We apply this transformation to give direct explanations to a variety of phenomena that have been observed in complex networks, such as explosive synchronization and vanishing synchronization onset.

关键词： Collective behavior in networks Coupled oscillators Synchronization Synchronization transition

Load balancing for multi-physics simulations 21

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Load balancing for multi-physics simulations

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Chunsen Li, Kenli Liu, Xu Li, Keqin College of Information Science and Engineering Hunan University Changsha China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science State University of New York New York United States

ISBN: (纸本)9781728120584

Multi-physics simulations are usually essential to simplify researches on complex physical phenomena. In this paper, we extend the rectangular partitioning from single-physics simulations to multi-physics simulations. This work presents the Single-rectangle Tightly-coupled algorithm (ST) and Multirectangle Tightly-coupled algorithm (MT) based on rectangular partitioning. The MT algorithm is more flexible and achieves higher load balancing efficiency than the ST algorithm by greedily assigning the remaining unallocated most loaded rectangle to the least loaded processor and assigning k rectangles to each of the processors. To study the effect of machine characteristics on load balancing, the experiments use two types of machine parameters based on Tianhe-2 supercomputer. Inspired by the roofline model, this paper analyzes the relationship among the choice of the load balancing algorithms, the load characteristics of multi-physics application and the features of high performance parallel computer through the comparison experiment of looselycoupled algorithm and tightly-coupled algorithm. In the case of LARED-P, MT algorithm can further reduce the total simulation time compared with the ST algorithm. Through experiments, we show that whether the load balancing algorithm is effective depends on both the load characteristics of multi-physics simulation and the features of high performance parallel computer. © 2019 IEEE.

关键词： Supercomputers

On deploying the artificial sport trainer into practice

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arXiv 2021年

作者： Fister, Iztok Iglesias, Andres Deb, Suash Fister, Iztok Galvez, Akemi Fister, Dušan Faculty of Electrical Engineering and Computer Science University of Maribor SI- Maribor2000 Slovenia Dpt. of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain IT & educational Consultant Ranchi Jharkhand India Faculty of Economics and Business University of Maribor MariborSI-2000 Slovenia

—computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that, typically, depend on the collection of past sport activities. However, monitoring the realization of the performed training sessions still represents a bottleneck in automating the process of sport training as a whole. The objective of this paper is to present a new low-cost and efficient embedded device for monitoring the realization of sport training sessions that is dedicated to monitor cycling training sessions. We designed and developed a new bike computer, i.e. the AST-Monitor, that can be mounted easily on almost every bicycle. The aforementioned bike computer is based on the Raspberry Pi device that supports different external sensors for capturing the data during the realization of sport training sessions. An adjusted GUI tailored to the needs of athletes is developed, along with the hardware. The proof of concept study, using the AST-Monitor in practice, revealed the potential of the proposed solution for monitoring of realized sport training sessions automatically. The new device also opens the door for the future utilization of Artificial Intelligence in a wide variety of sports. Copyright © 2021, The Authors. All rights reserved.

关键词： Sports

Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines

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arXiv 2023年

作者： Tran, Khoa Vu, Hai-Canh Pham, Lam Boudaoud, Nassim University of Science and Technology The University of Danang 54 Nguyen Luong Bang Hoa Khanh Bac Lien chieu Da Nang Danang550000 Viet Nam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam AIT Austrian Institute of Technology GmbH Giefinggasse 4 1210 Wien Vienna1210 Austria Roberval Laboratory Department of Mechanical Engineering University of Technology of Compiègne Rue Roger Couttolenc Compiègne60200 France

In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and Operating Condition (OC) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data;(2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data;(3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

Dynamic network analysis improves protein 3D structural classification

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arXiv 2021年

作者： Newaz, Khalique Piland, Jacob Clark, Patricia L. Emrich, Scott J. Li, Jun Milenković, Tijana Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Chemistry and Biochemistry University of Notre Dame Notre DameIN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN-based protein features, which performed better than or comparable to state-of-the-art sequence or other 3D structure-based approaches in the task of PSC. However, existing PSN-based PSC approaches model the whole 3D structure of a protein as a static PSN. Because folding of a protein is a dynamic process, where some parts of a protein fold before others, modeling the 3D structure of a protein as a dynamic PSN might further help improvethe existing PSC performance. Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches). Indeed, we confirm this on 71 datasets spanning ∼44,000 protein domains from CATH and SCOPe. © 2021, CC BY.

关键词： Proteins

DIFFUSION CURVATURE FOR ESTIMATING LOCAL CURVATURE IN HIGH DIMENSIONAL DATA

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arXiv 2022年

作者： Bhaskar, Dhananjay MacDonald, Kincaid Fasina, Oluwadamilola Thomas, Dawson Rieck, Bastian Adelstein, Ian Krishnaswamy, Smita Department of Genetics Yale School of Medicine New HavenCT United States Department of Mathematics Yale University New HavenCT United States Program for Applied Mathematics Yale University New HavenCT United States Department of Physics Yale University New HavenCT United States Institute of AI for Health Helmholtz Pioneer Campus Munich Germany Department of Computer Science Yale University New HavenCT United States Program for Computational Biology and Bioinformatics Yale University New HavenCT United States

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and define local curvature based on the laziness of a random walk starting at a point or region of the data. We show that this laziness directly relates to volume comparison results from Riemannian geometry. We then extend this scalar curvature notion to an entire quadratic form using neural network estimations based on the diffusion map of point-cloud data. We show applications of both estimations on toy data, single-cell data and on estimating local Hessian matrices of neural network loss landscapes. © 2022, CC BY.

关键词： Geometry

Some New Approaches to Comparative Evaluation of Algorithms for Calculating Distances Between Genomic Sequences 4th

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Some New Approaches to Comparative Evaluation of Algorithms ...

4th computational Methods in Systems and Software, CoMeSySo 2020

作者： Melnikov, Boris Trenina, Marina Nichiporchuk, Anastasia Melnikova, Elena Abramyan, Mikhail Department of Computational Mathematics and Cybernetics Shenzhen MSU–BIT University International University Park Road 1 Dayun ShenzhenGuangdong517182 China Department of Applied Mathematics Togliatti State University Belorusskaya 14 Togliatti445667 Russia Department of Information Technologies Russian State Social University Wilhelm Pieck 4 Moscow129226 Russia Institute of Mathematics Mechanics and Computer Science Southern Federal University Bolshaya Sadovaya 105/42 Rostov on Don344006 Russia

ISBN: (纸本)9783030633219

We continue to describe a new approach to the comparative evaluation of algorithms for calculating distances between DNA sequences. We compare the algorithms of calculation of distances, and we give our estimates of the comparative quality of these algorithms. We compare the total badness values for all triangles formed in the matrix and consider more acceptable the one of the initial algorithms of formation of the distance between two DNA sequences, for which the lower value of badness is obtained. In this paper, we apply a new approach. In it, we additionally assume that a "good" algorithm for counting the distances between a pair of given DNA sequences must yield a value close to some optimal number;of course, we do not know this number in advance. Therefore, we believe that considering all the resulting triangles (recall that they are formed quite a lot, a few thousand for the original matrices with a polarity of about 30), we have them in order of increasing the value of badness;and therefore we believe that for two "good" distance calculation algorithms should get a relatively large value of the pair correlation. In almost all computational experiments, our estimates of the comparative quality of algorithms for calculating distances between strings obtained by the two methods described are close to each other #COMESYSO1120. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA sequences

Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks

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arXiv 2024年

作者： Fagin, Joshua Vernardos, Georgios Tsagkatakis, Grigorios Pantazis, Yannis Shajib, Anowar J. O’Dowd, Matthew The Graduate Center The City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Astrophysics American Museum of Natural History Central Park West and 79th Street NY10024-5192 United States Department of Physics and Astronomy Lehman College The CUNY BronxNY10468 United States Institute of Computer Science FORTH HeraklionGR-70013 Greece Institute of Applied and Computational Mathematics FORTH HeraklionGR-70013 Greece Department of Astronomy & Astrophysics University of Chicago ChicagoIL606374 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States

Strong gravitational lensing can be used as a tool for constraining the substructure in the mass distribution of galaxies. In this study we investigate the power spectrum of dark matter perturbations in a population of 23 Hubble Space Telescope images of strong galaxy-galaxy lenses selected from The Sloan Lens ACS (SLACS) survey. We model the dark matter substructure as a Gaussian Random Field perturbation on a smooth lens mass potential, characterized by power-law statistics. We expand upon the previously developed machine learning framework to predict the power-law statistics by using a convolutional neural network (CNN) that accounts for both epistemic and aleatoric uncertainties. For the training sets, we use the smooth lens mass potentials and reconstructed source galaxies that have been previously modelled through traditional fits of analytical and shapelet profiles as a starting point. We train three CNNs with different training set: the first using standard data augmentation on the best-fitting reconstructed sources, the second using different reconstructed sources spaced throughout the posterior distribution, and the third using a combination of the two data sets. We apply the trained CNNs to the SLACS data and find agreement in their predictions. Our results suggest a significant substructure perturbation favoring a high frequency power spectrum across our lens population. © 2024, CC BY.

关键词： Convolutional neural networks

Efficient numerical computation of the basic reproduction number for structured populations

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arXiv 2020年

作者： Breda, Dimitri Florian, Francesco Ripoll, Jordi Vermiglio, Rossana CDLab - Computational Dynamics Laboratory Department of Mathematics Computer Science and Physics University of Udine via delle scienze 206 Udine33100 Italy Institute of Mathematics-University of Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland CDLab - Computational Dynamics Laboratory Department of Computer Science Applied Mathematics and Statistics University of Girona Campus Montilivi Girona17003

As widely known, the basic reproduction number plays a key role in weighing birth/infection and death/recovery processes in several models of population dynamics. In this general setting, its characterization as the spectral radius of next generation operators is rather elegant, but simultaneously poses serious obstacles to its practical determination. In this work we address the problem numerically by reducing the relevant operators to matrices through a pseudospectral collo-cation, eventually computing the sought quantity by solving finite-dimensional eigenvalue problems. The approach is illustrated for two classes of models, re-spectively from ecology and epidemiology. Several numerical tests demonstrate experimentally important features of the method, like fast convergence and influ-ence of the smoothness of the models' coefficients. Examples of robust analysis of instances of specific models are also presented to show potentialities and ease of *** Codes 65Pxx, 65L03, 65L15, 65M70, 65J10, 92D25, 92D30, 92D40, 47D06, 47A75 Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra