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IEEE Conference on Aerospace

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

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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DeepTracer-Denoising: Deep Learning for 3D Electron Density Map Denoising

DeepTracer-Denoising: Deep Learning for 3D Electron Density ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haowen Guan Dong Si Center of Data Science New York University New York City US Division of Computing and Software Systems University of Washington Bothell Bothell US

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Although many approaches have been proposed to improve the signal-to-noise ratio (SNR) for cryo-EM image denoising, the noises are still presented after 3D reconstruction and can obstruct the analysis and visualization of the 3D density map. Here we present DeepTracer-Denoising, a method for 3D electron density map denoising. We employ a 3D Neural Network to learn the pattern of noises and the biological structure from density maps. Our method is designed to work on medium to high-resolution maps ranging from 2.5 A to 10.0A. It is configurated with two modes to tackle both background noise and structural noise in a 3D density map. Our method can correctly identify 97.70% background noise while preserving 96.46% density of the native structure. For the maps that contain structural noise, DeepTracer-Denoising achieves an overall accuracy of 98.95%.

关键词： Training Visualization Three-dimensional displays Noise reduction Distance measurement Background noise Convolutional neural networks

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Driving Road Safety Forward: Video data Privacy Task at MediaEval 2021

Driving Road Safety Forward: Video Data Privacy Task at Medi...

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MediaEval 2021 Workshop, MediaEval 2021

作者： Liu, Alex Boka, Andrew Baragchizadeh, Asal Muthukumar, Chandini Huang, Victoria Sarup, Arjun Ferrell, Regina Friedland, Gerald Karnowski, Thomas P. Lee, Meredith M. O’Toole, Alice J. Division of Computing Data Science and Society University of California Berkeley United States School of Behavioral and Brain Sciences The University of Texas Dallas United States Oak Ridge National Laboratory United States

This paper gives an overview of the Driving Road Safety Forward: Video data Privacy Task organized as part of the Benchmarking Initiative for Multimedia Evaluation (MediaEval) 2021. The goal of this video data task is to explore methods for obscuring driver identity in driver-facing video recordings while preserving human behavioral information. Copyright 2021 for this paper by its authors.

关键词： data privacy

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Tensor neural networks for high-dimensional Fokker-Planck equations

arXiv

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arXiv 2024年

作者： Wang, Taorui Hu, Zheyuan Kawaguchi, Kenji Zhang, Zhongqiang Karniadakis, George Em Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science National University of Singapore 119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of several selected radial basis functions. The use of tensor feedforward networks allows us to efficiently exploit auto-differentiation (in physical variables) in major Python packages while using radial basis functions can fully avoid auto-differentiation, which is rather expensive in high dimensions. We then use the physics-informed neural networks and stochastic gradient descent methods to learn the tensor networks. One essential step is to determine a proper bounded domain or numerical support for the Fokker-Planck equation. To better train the tensor radial basis function networks, we impose some constraints on parameters, which lead to relatively high accuracy. We demonstrate numerically that the tensor neural networks in physics-informed machine learning are efficient for steady-state Fokker-Planck equations from two to ten dimensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

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A Method to Simultaneously Facilitate All Jet Physics Tasks

arXiv

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arXiv 2025年

作者： Mikuni, Vinicius Nachman, Benjamin National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system (pp versus ep), for generative models, for likelihood ratio estimation, and for anomaly detection. We consider, our OmniLearn approach thus as a jet-physics foundation model. It is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure. © 2025, CC BY.

关键词： Jets

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Predicting risk of dementia with machine learning and survival models using routine primary care records

Predicting risk of dementia with machine learning and surviv...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Langham, John Stamate, Daniel Wu, Charlotte A. Murtagh, Fionn Morgan, Catharine Reeves, David Ashcroft, Darren Kontopantelis, Evan McMillan, Brian University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom The University of Manchester NIHR School for Primary Care Research Division of Population Health Health Services Research Primary Care School of Health Sciences Manchester United Kingdom

ISBN: (纸本)9781665401265

Worldwide, it is forecasted that 131.5 million people will suffer from dementia by 2050, and the annual cost of care will increase from 818 billion USD in 2016 to 2 trillion USD by 2030, with burgeoning social consequences. Given a timely prediction of a dementia outcome in patients, appropriate mitigating interventions can be applied to reduce risk. However such prediction facilities need to be made available to wider populations, and these facilities cannot rely on specialised, costly and invasive testing (such as neuroimaging, cerebrospinal fluid collection, etc which constitute important instruments used in diagnosis), for interventions to have a meaningful quantitative impact. Hence an emerging need exists for the wider application of prognostic measures which can be deployed using lower cost data sources such as longitudinal records routinely collected by general practices. This paper proposes an efficient prediction modelling approach to the risk of dementia, using CPRD data collected from GP practices in UK, and based on machine learning in particular the Gradient Boosting Machines model combined with a survival model such as the Cox Proportional Hazard, encapsulated in a semi-supervised learning and model calibration methodology. © 2021 IEEE.

关键词： Machine learning

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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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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System 4

ANFIS Model for Cost Analysis in a Dual Source Multi-Destina...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Okwu, M.O. Tartibu, L.K. Ojo, E.O. Adume, S. Gidiagba, J.O. Fadeyi, J. Department of Mechanical Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling United Kingdom Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo United States

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Soft computing

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Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Lévy Equations

arXiv

引用

arXiv 2024年

作者： Hu, Zheyuan Zhang, Zhongqiang Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We introduce an innovative approach for solving high-dimensional Fokker-Planck-Lévy (FPL) equations in modeling non-Brownian processes across disciplines such as physics, finance, and ecology. We utilize a fractional score function and Physical-informed neural networks (PINN) to lift the curse of dimensionality (CoD) and alleviate numerical overflow from exponentially decaying solutions with dimensions. The introduction of a fractional score function allows us to transform the FPL equation into a second-order partial differential equation without fractional Laplacian and thus can be readily solved with standard physics-informed neural networks (PINNs). We propose two methods to obtain a fractional score function: fractional score matching (FSM) and score-fPINN for fitting the fractional score function. While FSM is more cost-effective, it relies on known conditional distributions. On the other hand, score-fPINN is independent of specific stochastic differential equations (SDEs) but requires evaluating the PINN model's derivatives, which may be more costly. We conduct our experiments on various SDEs and demonstrate numerical stability and effectiveness of our method in dealing with high-dimensional problems, marking a significant advancement in addressing the CoD in FPL equations. © 2024, CC BY.

关键词： Cost effectiveness

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