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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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A SPACE VIEW OF A SYMMETRICAL OBJECT

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COMPUTERS & MATHEMATICS WITH APPLICATIONS-PART B 1986年第1-2期12卷 477-485页

作者： LOWREY, BE Central Data Services Facility Code 633 National Space Science Data Center Space Data and Computing Division Goddard Space Flight Center Greenbelt MD 20771 U.S.A.

Symmetric features, both natural and man-made, exist on earth and are visible in space data. This paper closely examines one circularly symmetric object, the Robert F. Kennedy stadium in Washington, DC, which is contained in a Landsat-4 Thematic Mapper image. The effect of the space measurement on the appearance of the feature varies according to the band of the image. The human perception of the data is affected by the band and also by the magnification and context of the feature. The differences in interpretability of one object demonstrate the nature of the challenges involved in achieving computer vision. The future computing systems and related developments that may enable a machine to “understand” a symmetric object are described.

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AI-Powered Earth Disaster Management: A Collaborative Innovation Platform 24

AI-Powered Earth Disaster Management: A Collaborative Innova...

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8th International Conference on Innovation in Artificial Intelligence, ICIAI 2024

作者： Huang, Chung-I Sun, Chen-Kai Chang, Jih-Sheng Chang, Wen-Yi Lin, Fang-Pang Wu, Jyh-Horng Chen, Wei-Yu Data Science and Technology Division National Center for High Performance Computing Taiwan High Performance Computing Division National Center for High-performance Computing Taiwan

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The development of a blockchain-based business model simulator with an application to civic medical data management

Digital Business

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Digital Business 2025年第1期5卷

作者： Cunningham, James Davies, Nigel Devaney, Sarah Harding, Mike Holm, Søren Neumann, Victoria Ainsworth, John Division of Informatics Imaging & Data Sciences The University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester University Hospitals NHS Foundation Trust Manchester Academic Health Science Centre Manchester United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom Centre for Social Ethics and Policy The University of Manchester Manchester United Kingdom Centre for Medical Ethics University of Oslo Oslo Norway

Blockchain technology is reshaping business models by altering how transactions and data integrity are managed. This shift necessitates evaluating long-term profitability and operational efficiency across various blockchain frameworks. Our research describes the development and application of a business model simulator, beginning with use case analysis, followed by the simulator's design and application to the Civic data Identity Partnership project. We developed a transaction model, ran simulations, and analyzed outcomes to identify key cost drivers and assess the viability of different blockchain architectures. The results highlight the influence of execution costs, legal frameworks, and blockchain selection on financial outcomes, providing insights into the scalability and sustainability of blockchain applications in real-world scenarios. © 2024

关键词： Blockchain Business models Civic data Simulation

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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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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

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

ISBN: (纸本)9798350324174

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. © 2023 IEEE.

关键词： Information management

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Evaluating and Improving Optical Character Recognition (OCR) Efficiency in Recognizing Mandarin Phrases with Phonetic Symbols

Evaluating and Improving Optical Character Recognition (OCR)...

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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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MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories

arXiv

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arXiv 2025年

作者： Tirabassi, Natalie Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

We propose MORAL (a multimodal reinforcement learning framework for decision making in autonomous laboratories) that enhances sequential decision-making in autonomous robotic laboratories through the integration of visual and textual inputs. Using the Bridgedata V2 dataset, we generate fine-tuned image captions with a pretrained BLIP-2 vision-language model and combine them with visual features through an early fusion strategy. The fused representations are processed using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) agents. Experimental results demonstrate that multimodal agents achieve a 20% improvement in task completion rates and significantly outperform visual-only and textual-only baselines after sufficient training. Compared to transformer-based and recurrent multimodal RL models, our approach achieves superior performance in cumulative reward and caption quality metrics (BLEU, METEOR, ROUGE-L). These results highlight the impact of semantically aligned language cues in enhancing agent learning efficiency and generalization. The proposed framework contributes to the advancement of multimodal reinforcement learning and embodied AI systems in dynamic, real-world environments. © 2025, CC BY.

关键词： Reinforcement learning

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A Multi-Stage Deep Learning Pipeline for Crack Detection and Segmentation in High-Speed Rail Brake Discs

A Multi-Stage Deep Learning Pipeline for Crack Detection and...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ching-Heng Cheng Yu-Fan Lin Chia-Ming Lee Chih-Chung Hsu Yu-Lin Shih Ting Chin Department of Statistics Institute of Data Science National Cheng Kung Univ. Tainan Taiwan Department of Intelligent Technology Systems Institute of Miin Wu School of Computing National Cheng Kung Univ Tainan Taiwan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung Univ. Hsinchu Taiwan Innovation Logistics System Technology Dept. Smart Logistics & Supply Chain Integration Service Division Service Systems Technology Center Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The braking system of high-speed rail (HSR) is critical for passenger safety, but prolonged use of brake discs can lead to cracks, posing significant risks. Traditional manual inspection methods are time-consuming and labor-intensive. To address these challenges, we propose a multi-stage crack detection and segmentation system, a cascade mode to progressively identify cracks, leveraging deep learning techniques. The system includes automated circle detection, region cropping, and crack detection and segmentation to accurately identify cracks. Using a real-world dataset collected from HSR brake discs, our approach significantly improves data preprocessing efficiency and model performance. Furthermore, we evaluate the performance on un-seen data and compare our crack detection framework with other deep learning models. The experimental results demonstrate the system's effectiveness which is superior to other deep learning models, highlighting its potential for real-world deployment in HSR maintenance.

关键词： Deep learning Image segmentation Motion segmentation Computational modeling Pipelines Rail transportation data models Maintenance Safety Brakes

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Resolvability of classical-quantum channels

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IEEE Transactions on Information Theory 2025年

作者： Hayashi, Masahito Cheng, Hao-Chung Gao, Li Chinese University of Hong Kong School of Data Science Shenzhen Longgang District Shenzhen 518172 China Nagoya University Graduate School of Mathematics Nagoya 464-8602 Japan National Taiwan University Graduate Institute of Communication Engineering Department of Mathematics Institute of Applied Mathematical Sciences Center for Quantum Science and Engineering epartment of Electrical Engineering Taipei 10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei 10617 Taiwan Hon Hai (Foxconn) Quantum Computing Center New Taipei City 236 Taiwan Wuhan University School of Mathematics and Statistics Wuhan 430072 China

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and from the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from sequential hypothesis testing as it involves non-i.i.d. inputs. The strong converse of the worst-input setting is obtained via the connection to identification codes. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to prove the strong converse. © 1963-2012 IEEE.

关键词： Channel resolvability classical-quantum channel fixed-input formulation identification code worst-input formulation

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FedDAD: Federated Domain Adaptation for Object Detection

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IEEE Access 2023年 11卷 51320-51330页

作者： Lu, Peggy Joy Jui, Chia-Yung Chuang, Jen-Hui National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Center for High-Performance Computing Data Science and Technology Division Taichung40763 Taiwan

Training an object detection model often requires numerous annotated images on a centralized host, which may violate user privacy and data confidentiality. Federated learning (FL) resolves this issue by allowing multiple clients, e.g., cameras, to collaboratively train a model while protecting user privacy. However, models trained with FL may fail to be generalized for new target domain due to domain shift when the data between source and target domains are statistically different. In this work, we formulate a real-world object detection problem as a source-free multi-domain adaptation problem in FL architecture. Moreover, we propose an adaptive FL algorithm, called FedDAD (Federated Domain Adaptive Detector), which aggregates models with dynamic attention targeting the unsupervised domain on server, and utilize instance-level alignment to alleviate the effects of scene variation on clients. Experimental results show that FedDAD improves the average precision (AP) by up to 10.05% and 19.15% compared to the popular FedAvg for specific object classes in the KAIST and MI3 datasets, respectively. © 2013 IEEE.

关键词： Image annotation

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