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2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

作者： Lo, Shi Wei Chou, Hsiu-Mei National Center for High-Performance Computing Data Science and Technology Division Hsinchu Taiwan

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

The development of tools and materials for those people who are visually impaired is a crucial topic in the research area of assistive technologies. In mandarin learning, most printing materials for K-4 children have phonetic symbols to assist students in learning pronunciation. One challenge in the research area is recognizing the mandarin phrases in those printing materials and transforming them into audiobooks or Braille. To our knowledge, this is the first study examining the Optical Character Recognition (OCR) performance toward phonetic symbols in mandarin. In this study, we conduct experiments on recognizing images with mandarin phrases with phonetic symbols by the side using the OCR system. We propose candidate methods to improve recognition efficiency in the future based on preliminary results. © 2022 IEEE.

关键词： Optical character recognition

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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data-Adaptive Low-Rank Sparse Subspace Clustering

arXiv

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arXiv 2025年

作者： Kopriva, Ivica Division of Computing and Data Science Ruđer Bošković Institute Zagreb Croatia

Low-rank sparse subspace clustering (LRSSC) algorithms built on self-expressive model effectively capture both the global and local structure of the data. However, existing solutions, primarily based on proximal operators associated with Sp / l p , p∈{0, 1/2, 2/3, 1}, norms are not data-adaptive. In this work, we propose an LRSSC algorithm incorporating a data-adaptive surrogate for the S0 / l 0 quasi-norm. We provide a numerical solution for the corresponding proximal operator in cases where an analytical expression is unavailable. The proposed LRSSC algorithm is formulated within the proximal mapping framework, and we present theoretical proof of its global convergence toward a stationary point. We evaluate the performance of the proposed method on three well known datasets, comparing it against LRSSC algorithms constrained by Sp / l p , p∈{0, 1/2, 2/3, 1}, *** Codes 68W05, 68U10, 65Y99 © 2025, CC BY.

关键词： data assimilation

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Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System 6

Advances in Privacy Preserving Federated Learning to Realize...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

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

ISBN: (纸本)9798350386745

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. © 2024 IEEE.

关键词： Federated learning

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Interpretable label-free self-guided subspace clustering

arXiv

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arXiv 2024年

作者： Kopriva, Ivica Division of Computing and Data Science Ruđer Bošković Institute Zagreb Croatia

Majority subspace clustering (SC) algorithms depend on one or more hyperparameters that need to be carefully tuned for the SC algorithms to achieve high clustering performance. Hyperparameter optimization (HPO) is often performed using grid-search, assuming that some labeled data is available. In some domains, such as medicine, this assumption does not hold true in many cases. One avenue of research focuses on developing SC algorithms that are inherently free of hyperparameters. For hyperparameters-dependent SC algorithms, one approach to label-independent HPO tuning is based on internal clustering quality metrics (if available), whose performance should ideally match that of external (label-dependent) clustering quality metrics. In this paper, we propose a novel approach to label-independent HPO that uses clustering quality metrics, such as accuracy (ACC) or normalized mutual information (NMI), that are computed based on pseudo-labels obtained from the SC algorithm across a predefined grid of hyperparameters. Assuming that ACC (or NMI) is a smooth function of hyperparameter values it is possible to select subintervals of hyperparameters. These subintervals are then iteratively further split into halves or thirds until a relative error criterion is satisfied. In principle, the hyperparameters of any SC algorithm can be tuned using the proposed method. We demonstrate this approach on several single- and multi-view SC algorithms, comparing the achieved performance with their oracle versions across six datasets representing digits, faces and objects. The proposed method typically achieves clustering performance that is 5% to 7% lower than that of the oracle versions. We also make our proposed method interpretable by visualizing subspace bases, which are estimated from the computed clustering partitions. This aids in the initial selection of the hyperparameter search *** Codes 68T10, 68U10, 68T09 © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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Empowering Scientific Discovery Through computing at the Advanced Photon Source

Empowering Scientific Discovery Through Computing at the Adv...

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2023 International Conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Parraga, Hannah Hammonds, John Henke, Steven Veseli, Siniša Allcock, William Côté, Benoit Chard, Ryan Narayanan, Suresh Schwarz, Nicholas X-ray Science Division Argonne National Laboratory LemontIL United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798400707858

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication. © 2023 ACM.

关键词： Information management

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Feasibility Study on Restoring Chinese Documents by Convolutional Autoencoder

Feasibility Study on Restoring Chinese Documents by Convolut...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shi Wei Lo Hsiu-Mei Chou Data Science and Technology Division National Center for High-Performance Computing Hsinchu Taiwan

Digitalizing documents has become a crucial aspect of modern information management. However, various factors, such as manual handwriting, stains, and degradation, can significantly impact the quality of digitalized documents. This paper proposes a autoencoder-based network cleaning technology to completely erase manual writing, stains, colors, and background noise from paper documents to enhance their digitalized quality. Our proposed approach utilizes machine learning algorithms to identify and isolate unwanted elements from the document and then replace them with clean and transparent backgrounds. The experimental results demonstrate that our proposed approach significantly improves the digitalized quality of paper documents, making them more suitable for long-term storage and retrieval. Our proposed technology has the potential to revolutionize the field of paper document digitalization and improve the efficiency of information management.

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Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

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Physical Review D 2024年第5期110卷 056004页

作者： Radha Mastandrea Benjamin Nachman Tilman Plehn Department of Physics Physics Division Berkeley Institute for Data Science Institut für Theoretische Physik Interdisciplinary Center for Scientific Computing (IWR)

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

关键词： Effective field theory Extensions of Higgs sector Signatures with Higgs bosons Artificial neural networks Bayesian methods Machine learning

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Protein Generation via Genome-scale Language Models with Bio-physical Scoring

Protein Generation via Genome-scale Language Models with Bio...

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2023 International Conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Dharuman, Gautham Ward, Logan Ma, Heng Setty, Priyanka V. Gokdemir, Ozan Foreman, Sam Emani, Murali Hippe, Kyle Brace, Alexander Keipert, Kristopher Gibbs, Thomas Foster, Ian Anandkumar, Anima Vishwanath, Venkatram Ramanathan, Arvind Data Science and Learning Division United States Argonne Leadership Computing Facility Argonne National Laboratory United States Computer Science Department University of Chicago United States Nvidia Inc. United States California Institute of Technology United States

ISBN: (纸本)9798400707858

Large language models (LLMs) trained on vast biological datasets can learn biological motifs and correlations across the evolutionary landscape of natural proteins. LLMs can then be used for de novo design of novel proteins with specific structures, functions, and physicochemical properties. We employ a pre-trained genome-scale language model that uses codons as tokens and integrate it into a workflow for targeted generation of sequences. Our framework suggests new gene sequences that are ranked for downstream evaluation by metrics that collectively capture extensive sequence-specific, biophysical, and biochemical properties. We demonstrate our integrated workflow to design novel variants of the enzyme, malate dehydrogenase (MDH), that exhibit more favorable activation energies than their natural counterparts (reduction of 4.01 kJ/mol) with sustained sequence generation rates of ~104/hr and simulation rates of ~102/hr on 64 nodes of Polaris with about 99.7 system utilization during the run. © 2023 ACM.

关键词： Physicochemical properties

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