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Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI

Medical Image Analysis 2025年 104卷

作者： Nicolás Gaggion Benjamin A. Matheson Yan Xia Rodrigo Bonazzola Nishant Ravikumar Zeike A. Taylor Diego H. Milone Alejandro F. Frangi Enzo Ferrante Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK Christabel Pankhurst Institute The University of Manchester Manchester UK Centre for Computational Imaging and Modelling in Medicine (CIMIM) Department of Computer Science School of Engineering and Division of Informatics Imaging and Data Science School of Health Sciences The University of Manchester Manchester UK NIHR Manchester Biomedical Research Centre Manchester Academic Health Sciences Centre University of Manchester Manchester UK Medical Imaging Research Centre (MIRC) Department of Cardiovascular Sciences KU Leuven Leuven Belgium

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images and then reconstructing meshes, making them time-consuming and prone to error propagation. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multi-view HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity meshes from CMR images. Multi-view HybridVNet outperforms the state-of-the-art, achieving improvements of up to ∼ 27% reduction in Mean Contour Distance (from 1.86 mm to 1.35 mm for the LV Myocardium), up to ∼ 18% improvement in Hausdorff distance (from 4.74 mm to 3.89 mm, for the LV Endocardium), and up to ∼ 8% in Dice Coefficient (from 0.78 to 0.84, for the LV Myocardium), highlighting its superior accuracy.

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Asymptotically optimal probabilistic embedding algorithms for supporting tree structured computations in hypercubes

Asymptotically optimal probabilistic embedding algorithms fo...

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Frontiers of Massively Parallel Computation

作者： Keqin Li J.E. Dorband Department of Mathematics and Computer Science State University of New York New Paltz NY USA Space Data and Computing Division-Code 934 NASA Goddard Space Flight Center Greenbelt MA USA

We show two asymptotically optimal probabilistic tree embedding algorithms in hypercubes with constant dilation. These algorithms are slight extension of the random walk algorithm. The first algorithm allows a tree node to have a stay option during each step of a random walk. The second algorithm permits varying length of random walks. Numerical data are given to demonstrate performance improvement.

关键词： Embedded computing Hypercubes Concurrent computing Mathematics Computer science NASA Uniform resource locators World Wide Web HTML Heuristic algorithms

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Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

arXiv

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arXiv 2023年

作者： Hu, Zheyuan Yang, Zhouhao Wang, Yezhen Karniadakis, George Em Kawaguchi, Kenji Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Computer Science National University of Singapore Singapore119077 Singapore Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine

关键词： Stochastic systems

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Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks

arXiv

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arXiv 2023年

作者： Hu, Zheyuan Shi, Zekun Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computational cost associated with automatic differentiation in the residual loss function calculation. Herein, we address the limitations of PINNs in handling high-dimensional and high-order PDEs by introducing the Hutchinson Trace Estimation (HTE) method. Starting with the second-order high-dimensional PDEs, which are ubiquitous in scientific computing, HTE is applied to transform the calculation of the entire Hessian matrix into a Hessian vector product (HVP). This approach not only alleviates the computational bottleneck via Taylor-mode automatic differentiation but also significantly reduces memory consumption from the Hessian matrix to an HVP's scalar output. We further showcase HTE's convergence to the original PINN loss and its unbiased behavior under specific conditions. Comparisons with the Stochastic Dimension Gradient Descent (SDGD) highlight the distinct advantages of HTE, particularly in scenarios with significant variability and variance among dimensions. We further extend the application of HTE to higher-order and higher-dimensional PDEs, specifically addressing the biharmonic equation. By employing tensor-vector products (TVP), HTE efficiently computes the colossal tensor associated with the fourth-order high-dimensional biharmonic equation, saving memory and enabling rapid computation. The effectiveness of HTE is illustrated through experimental setups, demonstrating comparable convergence rates with SDGD under memory and speed constraints. Additionally, HTE proves valuable in accelerating the Gradient-Enhanced PINN (gPINN) version as well as the Biharmonic equation. Overall, HTE opens up a new capability in scientific machine learning for tackl

关键词： Stochastic systems

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Score-based generative models for calorimeter shower simulation

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Physical Review D 2022年第9期106卷 092009-092009页

作者： Vinicius Mikuni Benjamin Nachman National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA

Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce caloscore, a score-based generative model for collider physics applied to calorimeter shower generation. Three different diffusion models are investigated using the Fast Calorimeter Simulation Challenge 2022 dataset. caloscore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.

关键词： Calorimeters Deep learning Electromagnetic calorimeters Machine learning

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Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System

arXiv

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arXiv 2024年

作者： Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex Data Science and Learning Division Argonne National Laboratory LemontIL United States Mathematics and Computer Science Argonne National Laboratory LemontIL United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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The Vertebrate Breed Ontology: Toward Effective Breed data Standardization

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Journal of Veterinary Internal Medicine 2025年第4期39卷 e70133页

作者： Kathleen R. Mullen Imke Tammen Nicolas A. Matentzoglu Marius Mather James P. Balhoff Elizabeth Esdaile Gregoire Leroy Carissa A. Park Halie M. Rando Nadia T. Saklou Tracy L. Webb Nicole A. Vasilevsky Christopher J. Mungall Melissa A. Haendel Frank W. Nicholas Sabrina Toro Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Sydney School of Veterinary Science The University of Sydney Sydney New South Wales Australia Semanticly Ltd Athens Greece Sydney Informatics Hub The University of Sydney Sydney New South Wales Australia Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Veterinary Genetics Laboratory School of Veterinary Medicine University of California Davis California USA Animal Production and Health Division Food and Agriculture Organization of the United Rome Italy Department of Animal Science Iowa State University Ames Iowa USA Department of Computer Science Smith College Northampton Massachusetts USA Department of Clinical Sciences Colorado State University Fort Collins Colorado USA Data Collaboration Center Critical Path Institute Tucson Arizona USA Lawrence Berkeley National Laboratory Berkeley California USA

Background Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Hypothesis/Objectives A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals No live animals were used. Methods The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence–readiness. Results VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and Clinical Importance The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.

关键词： animal population groups biological ontologies breeding genetics precision medicine theriogenology

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Geometrically Tailored Skyrmions at Zero Magnetic Field in Multilayered Nanostructures

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Physical Review Applied 2019年第2期11卷 024064-024064页

作者： Pin Ho Anthony K.C. Tan S. Goolaup A.L. Gonzalez Oyarce M. Raju L.S. Huang Anjan Soumyanarayanan C. Panagopoulos Data Storage Institute Agency for Science Technology and Research (A*STAR) Singapore 138634 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research (A*STAR) Singapore 138634 Singapore Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological University Singapore 637371 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore

Magnetic skyrmions are chiral spin structures recently observed at room temperature in multilayer films. Their topological stability will enable high scalability in confined geometries—a sought-after attribute for device applications. Despite numerous theoretical studies examining sub-100-nm Néel skyrmions in nanostructures, in practice their ambient stability and evolution with confinement and their magnetic parameters remain to be established. Here we present the zero-field stabilization of sub-100-nm room-temperature Néel-textured skyrmions confined in Ir/Fe(x)/Co(y)/Pt nanodots over a wide range of magnetic and geometric parameters. The zero-field skyrmion size, here as small as approximately 50 nm, can be tailored by a factor of 4 with variation of dot size and magnetic interactions. Crucially, skyrmions with differing thermodynamic stability exhibit an unexpected dichotomy in confinement phenomenologies. These results establish skyrmion phenomenology in multilayer nanostructures, and prompt the synergistic use of magnetic and geometric parameters to achieve desired properties in devices.

关键词： Magnetic interactions Skyrmions Spin-orbit coupling Spintronics Magnetic multilayers Nanostructures Magnetic force microscopy Micromagnetic modeling

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Comparison of Multi-Phase Power Converters and Power Delivery Networks for Next-Generation Space Architectures

Comparison of Multi-Phase Power Converters and Power Deliver...

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IEEE Conference on Aerospace

作者： Nicholas Franconi Thomas Cook Christopher Wilson Alan D. George NASA Goddard Space Flight Center Science Data Processing Branch Code 587 Software Engineering Division Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh

Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the current generation of radiation-hardened processors are inferior compared to commercial-off-the-shelf alternatives in terms of the computational performance required by modern AI applications. To address this disparity, space-system designers have started employing novel radiation-tolerant architectures combining both commercial and radiation-hardened components to mitigate radiation effects at a system level. Unfortunately, developing single-board computers with radiation-tolerant, high-performance processors is challenging because designers must balance the sparce selection of radiation-hardened power converters and high-reliability decoupling capacitors with SmallSat/CubeSat area constraints and limited thermal conduction. Consequently, the next-generation of space processors, including the AMD-Xilinx Versal Adaptive Compute Acceleration Platform require demanding power solutions capable of supplying core rails with 0.8V ± 18mV and currents up to 150 A. In this paper, we present a multifaceted analysis of the power system and decoupling network for a future Versal-based design. We developed preliminary power estimates based on expected processor and FPGA resource utilization for common AI processing applications. These estimates drive the power system requirements and a comparative analysis of single-phase integrated converters and multi-phase discrete converters for high-current FPGA supplies. We develop a tradespace between the number of phases, input voltage, load current, switching frequency, power efficiency, and printed circuit board area. Finally, we design and simulate four power delivery networks based on commercial, high-reliability, and flight-qualified capacitors to compare the efficacy of 0201 decoupling capacitors in flight missions.

关键词： Rails Program processors Capacitors Voltage Power systems Resource management Artificial intelligence

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Wavelength-Selective Distribution of Polarization Entanglement over Deployed Fiber

Wavelength-Selective Distribution of Polarization Entangleme...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Ramesh, Anirudh Chung, Joaquin Kanter, Gregory Peña, Cristián Plunkett, Robert K. Xie, Si Spentzouris, Panagiotis Kettimuthu, Rajkumar Kumar, Prem Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States NuCryptLLC 1460 Renaissance Dr #205 Park RidgeIL60068 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL United States

ISBN: (纸本)9781957171258

We demonstrate distribution of polarization entangled photon-pairs across multiple channels in a 6 km loop of deployed fiber. We use an all-optical networking switch to route Bell-state photons in three different wavelength pairs and verify entanglement distribution in all channel pairs by measuring two-photon interference fringes. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Photons

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