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arXiv 2023年

作者： Ponton, Jose Luis Ceballos, Víctor Acosta, Lesly Ríos, Alejandro Monclús, Eva Pelechano, Nuria Research Center for Visualization Virtual Reality and Graphics Interaction - ViRVIG Universitat Politècnica de Catalunya Barcelona Spain Visual Computing Center - VCC King Abdullah University of Science and Technology Thuwal Saudi Arabia Estadística i Investigació Operativa - EIO Universitat Politècnica de Catalunya Barcelona Spain

In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers. © 2023, CC BY.

关键词： Inverse kinematics

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Learning orbital dynamics of binary black hole systems from gravitational wave measurements

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Physical Review Research 2021年第4期3卷 043101-043101页

作者： Brendan Keith Akshay Khadse Scott E. Field Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA

We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physics-informed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and noneccentric orbits. We show that the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a data-driven approach to studying the dynamics of binary black hole systems.

关键词： General relativity equations & solutions Gravitational waves

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Interplay between numerical relativity and perturbation theory: Finite size effects

arXiv

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arXiv 2023年

作者： Islam, Tousif Khanna, Gaurav Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics Center for Computational Research University of Rhode Island KingstonRI02881 United States

We investigate the interplay between numerical relativity (NR) and point-particle black hole perturbation theory (ppBHPT) in the comparable mass ratio regime. In the ppBHPT framework, the secondary black hole is treated as a point particle, neglecting its finite size. Our study focuses on addressing the missing finite size effect in the ppBHPT framework and proposing a method for incorporating the size of the secondary into the perturbation theory framework. We demonstrate that by considering the secondary as a finite size object, the BHPT waveforms closely match NR waveforms. Additionally, we revisit the α-β scaling technique, which was previously introduced by Islam et al. [1], as a means to effectively match ppBHPT waveforms to NR waveforms. We further analyze the scaling procedure and decompose it into different components, attributing them to various effects, including the corrections arising from the finite size of the secondary black hole. Copyright © 2023, The Authors. All rights reserved.

关键词： Stars

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Gravitational wave surrogate model for spinning, intermediate mass ratio binaries based on perturbation theory and numerical relativity

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Physical Review D 2024年第12期110卷 124069-124069页

作者： Katie Rink Ritesh Bachhar Tousif Islam Nur E. M. Rifat Kevin González-Quesada Scott E. Field Gaurav Khanna Scott A. Hughes Vijay Varma Department of Physics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics and Weinberg Institute for Theoretical Physics University of Texas at Austin Austin Texas 78712 USA Department of Physics and Center for Computational Research East Hall University of Rhode Island Kingston Rhode Island 02881 USA Kavli Institute for Theoretical Physics University of California Santa Barbara Kohn Hall Lagoon Road Santa Barbara California 93106 USA Kavli Institute for Theoretical Physics University of California Santa Barbara Santa Barbara California 93106 USA Department of Physics University of Virginia 1827 University Avenue Charlottesville Virginia 22903 USA Department of Physics and Center for Education and Research in Cosmology and Astrophysics (CERCA) Case Western Reserve University Cleveland Ohio 44106 USA Department of Mathematics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

We present BHPTNRSur2dq1e3, a reduced order surrogate model of gravitational waves emitted from binary black hole (BBH) systems in the comparable to large mass ratio regime with aligned spin (χ1) on the heavier mass (m1). We trained this model on waveform data generated from point particle black hole perturbation theory (ppBHPT) with mass ratios varying from 3≤q≤1000 and spins from −0.8≤χ1≤0.8. The waveforms are 13,500 m1 long and include all ℓ≤4 spin-weighted spherical harmonic modes except the (4,1) and m=0 modes. We find that, for binaries with χ1≲−0.5, retrograde quasinormal modes are significantly excited, thereby complicating the modeling process. To overcome this issue, we introduce a domain decomposition approach to model the inspiral and merger-ringdown portion of the signal separately. The resulting model can faithfully reproduce ppBHPT waveforms with a median time-domain mismatch error of 8×10−5. We then calibrate our model with numerical relativity (NR) data in the comparable mass regime (3≤q≤10). By comparing with spin-aligned BBH NR simulations at q=15, we find that the dominant quadrupolar (subdominant) modes agree to better than ≈10−3 (≈10−2) when using a time-domain mismatch error, where the largest source of calibration error comes from the transition-to-plunge and ringdown approximations of perturbation theory. Mismatch errors are below ≈10−2 for systems with mass ratios between 6≤q≤15 and typically get smaller at larger mass ratio. Our two models—both the ppBHPT waveform model and the NR-calibrated ppBHPT model—will be publicly available through gwsurrogate and the black hole perturbation toolkit packages.

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CV4FEE: Flood Extent Estimation Using Consensus Voting in Ensemble of Methods for Change Detection in Sentinel-1 GRD SAR Images

CV4FEE: Flood Extent Estimation Using Consensus Voting in En...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Ragesh Thangavel Jaya Sreevalsan-Nair Center for Information Technology and Public Policy IIIT Bangalore Bangalore India Graphics-Visualization-Computing Lab IIIT Bangalore Bangalore India

ISBN: (纸本)9781728173344

There are several methods for finding flooded areas using change detection from C-band Sentinel-l temporal GRDH Synthetic Aperture Radar (SAR) images. Using these methods, an automatic flood extent mapping is obtained through differences between multiple time instances of SAR images, which itself is processed to remove noise and isolated pixels. While a few selected methods give comparable results using similar approaches, it is difficult to determine the best result amongst them. Hence, we propose CV4FEE, which is a consensus voting (CV) of outcomes from an ensemble of flood extent estimation (FEE) methods. We select three different state-of-the-art FEE methods, based on a specific criterion. We then use different consensus voting functions to compute deterministic as well as probabilistic flood extent maps, thus accounting for uncertainty in computation in selected methods. We demonstrate our results of flood area estimation in two different flooding events in India, namely the Hyderabad flood and the Nivar cyclone, in 2020. We show that the probabilistic maps give flood extent estimation that confirms the GEE4FLOOD results, qualitatively. Our method successfully demonstrates the uncertainty in the estimation process.

关键词： Uncertainty Systematics Estimation Lakes Probabilistic logic Reservoirs Radar polarimetry

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Gravitational-wave template banks for novel compact binaries

arXiv

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arXiv 2023年

作者： Schmidt, Stefano Gadre, Bhooshan Caudill, Sarah Nikhef Science Park 105 Amsterdam1098 XG Netherlands Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo and Kagra data. We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few hours to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Matched filters

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Comparing Community Detection Methods in Brain Functional Connectivity Networks 4th

Comparing Community Detection Methods in Brain Functional Co...

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4th International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2019

作者： Vangimalla, Reddy Rani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center International Institute of Information Technology - Bangalore 26/C Electronics City Bangalore560100 India

ISBN: (纸本)9789811596995

Brain functional networks are essential for understanding the functional connectome. computing the temporal dependencies between the regions of brain activities from the functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Optimizing post-Newtonian parameters and fixing the BMS frame for numerical-relativity waveform hybridizations

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Physical Review D 2024年第10期110卷 104076-104076页

作者： Dongze Sun Michael Boyle Keefe Mitman Mark A. Scheel Leo C. Stein Saul A. Teukolsky Vijay Varma Theoretical Astrophysics 350-17 California Institute of Technology 1200 E California Boulevard Pasadena California 91125 USA Cornell Center for Astrophysics and Planetary Science Cornell University Ithaca New York 14853 USA Department of Physics and Astronomy University of Mississippi University Mississippi 38677 USA Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Dartmouth Massachusetts 02747 USA Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany

Numerical relativity (NR) simulations of binary black holes provide precise waveforms, but are typically too computationally expensive to produce waveforms with enough orbits to cover the whole frequency band of gravitational-wave observatories. Accordingly, it is important to be able to hybridize NR waveforms with analytic, post-Newtonian (PN) waveforms, which are accurate during the early inspiral phase. We show that to build such hybrids, it is crucial to both fix the Bondi-Metzner-Sachs (BMS) frame of the NR waveforms to match that of PN theory, and optimize over the PN parameters to mitigate the error caused by the discrepancy between NR and PN parameters. We test such a hybridization procedure including all spin-weighted spherical harmonic modes with |m|≤ℓ for ℓ≤8, using 29 NR waveforms with mass ratios q≤10 and spin magnitudes |χ1|,|χ2|≤0.8. We find that for spin-aligned systems, the PN and NR waveforms agree very well. The difference is limited by the small nonzero orbital eccentricity of the NR waveforms, or equivalently by the lack of eccentric terms in the PN waveforms. To maintain full accuracy of the simulations, the matching window for spin-aligned systems should be at least 5 orbits long and end at least 15 orbits before merger. For precessing systems, the errors are larger than for spin-aligned cases. The errors are likely limited by the absence of precession-related spin-spin PN terms. Using 105M long NR waveforms, we find that there is no optimal choice of the matching window within this time span, because the hybridization result for precessing cases is always better if using earlier or longer matching windows. We provide the mean orbital frequency of the smallest acceptable matching window as a function of the target error between the PN and NR waveforms and the black hole spins.

关键词： General relativity Gravitation Gravitational waves Astronomical black holes Numerical relativity Numerical simulations in gravitation & astrophysics

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ES-GP: An Ensemble Surrogate-Assisted Genetic Programming Approach to Image Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Fan, Qinglan Zhang, Yunfeng Yao, Xunxiang Bi, Ying Xue, Bing Zhang, Mengjie Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Key Laboratory of Lightweight Intelligent Computing and Visualization for Digital Economy Jinan250014 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000 China Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Genetic Programming (GP) is a promising evolutionary machine learning technique for image classification, known for its ability to evolve flexible, effective, and interpretable models. However, the high computational cost of fitness evaluations in evolutionary learning restricts its practical applications. While Surrogate models offer efficient approximations for costly fitness evaluations, their application in GP-based image classification remains in its early stages, facing challenges such as handing flexible tree-based representations with variable lengths, designing an effective surrogate, and the limited performance of a single surrogate across various image classification tasks. To address these issues, this paper proposes an ensemble surrogate-assisted GP approach to image classification. The new approach constructs one global surrogate model to explore broad areas and three local surrogate models within specific subspaces to exploit local regions, enabling more accurate predictions of GP individuals' fitness. Moreover, a dynamic weighting strategy is developed to assign different weights to the base surrogate models in the ensemble, improving prediction accuracy. Additionally, the proposed approach refines the surrogate training set1 construction method, previously limited to single-tree GP, enabling it to accelerate both single-tree and multi-tree GP 2 for image classification. Experimental results on five datasets of varying difficulty demonstrate that the new ensemble surrogate method significantly reduces the number of expensive fitness evaluations of both single-tree and multi-tree GP-based image classification methods while achieving competitive performance. The comparisons with other state-of-the-art methods also confirm its effectiveness. © 1997-2012 IEEE.

关键词： Genetic algorithms

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Gravitational wave surrogate model for spinning, intermediate mass ratio binaries based on perturbation theory and numerical relativity

arXiv

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arXiv 2024年

作者： Rink, Katie Bachhar, Ritesh Islam, Tousif Rifat, Nur E.M. González-Quesada, Kevin Field, Scott E. Khanna, Gaurav Hughes, Scott A. Varma, Vijay Department of Physics Center for Scientific Computing & Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics Center for Gravitational Physics University of Texas AustinTX78712 United States Department of Physics Center for Computational Research University of Rhode Island East Hall KingstonRI02881 United States Kavli Institute for Theoretical Physics University of California Santa Barbara Kohn Hall Lagoon Rd Santa BarbaraCA93106 United States Kavli Institute for Theoretical Physics University of California Santa BarbaraCA93106 United States Department of Physics University of Virginia 1827 University Avenue CharlottesvilleVA22903 United States Case Western Reserve University ClevelandOH44106 United States Department of Mathematics Center for Scientific Computing & Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

We present BHPTNRSur2dq1e3, a reduced order surrogate model of gravitational waves emitted from binary black hole (BBH) systems in the comparable to large mass ratio regime with aligned spin (χ1) on the heavier mass (m1). We trained this model on waveform data generated from point particle black hole perturbation theory (ppBHPT) with mass ratios varying from 3 ≤ q ≤ 1000 and spins from −0.8 ≤ χ1 ≤ 0.8. The waveforms are 13,500 m1 long and include all ≤ 4 spin-weighted spherical harmonic modes except the (4, 1) and m = 0 modes. We find that for binaries with χ1 −0.5, retrograde quasi-normal modes are significantly excited, thereby complicating the modeling process. To overcome this issue, we introduce a domain decomposition approach to model the inspiral and merger-ringdown portion of the signal separately. The resulting model can faithfully reproduce ppBHPT waveforms with a median time-domain mismatch error of 8 × 10−5. We then calibrate our model with numerical relativity (NR) data in the comparable mass regime (3 ≤ q ≤ 10). By comparing with spin-aligned BBH NR simulations at q = 15, we find that the dominant quadrupolar (subdominant) modes agree to better than ≈ 10−3 (≈ 10−2) when using a time-domain mismatch error, where the largest source of calibration error comes from the transition-to-plunge and ringdown approximations of perturbation theory. Mismatch errors are below ≈ 10−2 for systems with mass ratios between 6 ≤ q ≤ 15 and typically get smaller at larger mass ratio. Our two models – both the ppBHPT waveform model and the NR-calibrated ppBHPT model – will be publicly available through gwsurrogate and the Black Hole Perturbation Toolkit packages. © 2024, CC BY.

关键词： Perturbation techniques

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