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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seongwook Kim Gwangeun Byeon Seungeui Lee Youngseok Bae Seokin Hong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Advanced R&D Center Hanwha Systems Co. Ltd. Emerging Science and Technology Directorate Agency for Defense Development

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-delta-Immediate Compression (BdI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement,

关键词： deep learning Filters Artificial neural networks Performance gain Complexity theory Hardware acceleration

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Calabi-Yau Feynman integrals in gravity: ε-factorized form for apparent singularities

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Journal of High Energy Physics 2025年第2期2025卷 1-40页

作者： Frellesvig, Hjalte Morales, roger Pögel, Sebastian Weinzierl, Stefan Wilhelm, Matthias Niels Bohr International Academy Niels Bohr Institute Copenhagen University Blegdamsvej 17 Copenhagen Ø DK-2100 Denmark Paul Scherrer Institut Villigen PSI CH-5232 Switzerland PRISMA Cluster of Excellence Institut für Physik Johannes Gutenberg-Universität Mainz Mainz D-55099 Germany Center for Quantum Mathematics Department of Mathematics and Computer Science University of Southern Denmark Odense M DK-5230 Denmark

We study a recently identified four-loop Feynman integral that contains a three-dimensional Calabi-Yau geometry and contributes to the scattering of black holes in classical gravity at fifth post-Minkowskian and second self-force order (5PM 2SF) in the conservative sector. In contrast to previously studied Calabi-Yau Feynman integrals, the higher-order differential equation that this integral satisfies in dimensional regularization exhibits ε-dependent apparent singularities. We introduce an appropriate ansatz which allows us to bring such cases into an ε-factorized form. As a proof of principle, we apply it to the integral at hand. © The Author(s) 2025.

关键词： Black Holes differential and Algebraic Geometry Scattering Amplitudes

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MrI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MrI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MrI,aiming at solving the problems of MrI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic resonance in Medicine raw data(ISMrMrd)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MrI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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detection of Autism Spectrum disorder Using Quantum Support Vector Machines Algorithm

Detection of Autism Spectrum Disorder Using Quantum Support ...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Arshia Eftekhari Zadeh Ali Mikaeili Barzili Mohammad r. Nemati Mohammad Khoshnevisan Hamid Azadegan Behzad Moshiri School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Engineering Antalya Bilim University Antalya Turkey College of Science Northeastern University Boston MA USA Digital Solution Expert MCI R&D Center Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Ontario ON Canada

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Early detection of Autism Spectrum disorder (ASd) is crucial for initiating interventions that can significantly improve developmental outcomes. Traditional diagnostic methods are often subjective and time-consuming, leading to delays in diagnosis. While classical machine learning models have been applied to ASd detection with promising results, they face challenges in processing complex, high-dimensional medical data. Quantum computing utilizes superposition, and entanglement offers a novel approach to handle such data more efficiently. This study investigates the application of Quantum Support Vector Machines (QSVM) for ASd detection using the Autism Screening data for Toddlers (ASdTests) dataset. After preprocessing and feature selection, we trained a QSVM model. We compared its performance with classical machine learning models, including Support Vector Machine (SVM), random Forest (rF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We evaluated the models based on accuracy, precision, recall, and F1-score across multiple random subsets to assess robustness. The QSVM model achieved the highest mean accuracy (96.7 %), precision (97.2 %), and F1-score (95.9 %) with low variance, indicating strong and consistent performance. Statistical analyses using the Paired t-test and Friedman Test showed marginal improvements over the SVM model (p = 0.0495), with no significant difference compared to rF and NB models. These results suggest that QSVM can enhance the accuracy and robustness of ASd detection compared to classical models. Future work could explore hybrid models combining quantum and classical machine learning approaches to improve performance. Applying these methods to different ASd datasets may provide broader validation and insights into their generalizability.

关键词： Support vector machines radio frequency Autism Analytical models Quantum computing Accuracy Computational modeling Static VAr compensators Machine learning Nearest neighbor methods

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Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

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Physical review X 2025年第2期15卷 021017-021017页

作者： Vikram Kashyap Georgios Styliaris Sara Mouradian J. Ignacio Cirac rahul Trivedi Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Max-Planck-Institut für Quantenoptik Garching bei München 85748 Germany Munich Center for Quantum Science and Technology (MCQST) Schellingstraße 4 D-80799 München Germany

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly(ϵ−1) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP≠BPP. We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP≠BPP, there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator.

关键词： Quantum computers

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Securing the digital Frontier: An Analysis of Cybersecurity Strategies and Obstacles

Securing the Digital Frontier: An Analysis of Cybersecurity ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Saravanan, V. Indumathi, G.S. James, Sathish Kumar Pandi, V. Samuthira Shakila, J. Banumathi, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India BNY Mellon Center PittsburghPA15219 United States Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Jeppiaar Engineering College Rajiv Gandhi Salai Department of CSE Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India

ISBN: (纸本)9798331512965

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats. © 2025 IEEE.

关键词： Cyber attacks

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Construction and Application of Protein-Protein Interaction Knowledge Graph 9th

Construction and Application of Protein-Protein Interaction ...

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9th International Symposium on Artificial Intelligence and robotics, ISAIr 2024

作者： Xiong, Jing Wang, Xiaohong dou, Fangkun Cao, Haowei Hong, Tenglong Li, Zhiwei Jing, Xiaoli School of Computer Science Qufu Normal University Rizhao276827 China Shandong Foreign Trade Vocational College Qingdao266100 China Rizhao-Qufu Normal University Joint Technology Transfer Center Rizhao276827 China Institute of Oceanology Chinese Academy of Sciences Qingdao266071 China Jinan250101 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250101 China China Everbright Bank Qingdao R&D Center Qingdao266071 China Rizhao Medical Insurance Service Center Rizhao276826 China Department of Information Research Qingdao Marine Science and Technology Center Qingdao266237 China

ISBN: (纸本)9789819629107

With the rapid advancement of emerging technologies such as high-throughput sequencing, cryo-electron microscopy, mass spectrometry, and artificial intelligence, our understanding of protein interactions has deepened significantly, leading to an increasing demand for effective data integration. This study tackles the complexity of protein interaction networks and the challenges associated with data integration by employing a top-down approach. It utilizes a protein ontology as the semantic framework, integrates resources from the BioGrId and UniProt databases, and constructs a comprehensive protein-protein interaction knowledge graph (PPIKG). This knowledge graph not only provides an intuitive, graphical interface for visualizing intricate protein interactions but also serves as a centralized platform for downloading protein interaction data. It acts as a one-stop solution for scientists aiming to explore and comprehend the complex landscape of protein interactions. With its user-friendly visualization and efficient download tools, the knowledge graph simplifies the data acquisition process, thereby fostering a deeper, holistic understanding of protein interaction networks among researchers. This innovative approach to data integration is set to accelerate scientific discovery and enhance our comprehension of biological systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Linearized Gait Phase Estimation Using BiLSTM

Linearized Gait Phase Estimation Using BiLSTM

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Yonghyun Lee dongbin Shin Younghoon Ji Hyeyoun Jang Changsoo Han Yeonjoon Lee Department Of Applied Artificial Intelligence (Major In Bio Artificial Intelligence) Hanyang University Seoul Korea R&D Center Hexar Humancare Ansan Korea Research&Development Hexar Humancare Ansan Korea CEO Hexar Humancare Ansan Korea Computer Science Hanyang University Ansan Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper presents a novel deep learning-based approach to address time-delay challenges arising from nonlinear transformations in Gait Phase Estimation, a critical factor in analyzing human gait for lower limb exoskeleton robots. The proposed model leverages hip joint angles and angular velocities as feature data, organized using a sliding window technique, to predict the Linearized Gait Phase (LGP)‖a continuous repre-sentation of gait progression between Heel Strikes. Motivated by the need for real-time and precise gait phase estimation to enhance exoskeleton control, a Bidirectional LSTM network was employed to reduce delays and improve accuracy. The model's effectiveness was validated through comprehensive performance evaluations using metrics such as Mean Absolute Error (MAE), root Mean Square Error (rMSE), Mean Square Error (MSE), and r2 score, alongside a temporal analysis of delay reduction. The results demonstrated superior performance, with an MAE of 2.8±0.8%, rMSE of 3.9±1.2%, MSE of 0.1±0.1%, and an r2 score of 98.1±1.8%. Particularly, temporal analysis revealed a marked improvement, achieving an MAE of 0%, compared to 2.74±0.92% reported in prior studies. These findings underscore the proposed model's potential for real-time gait phase estimation, offering significant implications for enhancing the responsiveness and adaptability of lower limb exoskeleton robots.

关键词： Performance evaluation Limbs Phase estimation Exoskeletons Bidirectional long short term memory Predictive models real-time systems delays root mean square robots

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Self-adaptive vision-language model for 3d segmentation of pulmonary artery and vein

arXiv

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arXiv 2025年

作者： Guo, Xiaotong Yang, deqian Wang, dan Zhao, Haochen Li, Yuan Sui, Zhilin Zhou, Tao Zhang, Lijun Meng, Yanda State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Thoracic Surgery National Clinical Research Center for Cancer Cancer Hospital Shenzhen Hospital China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China R&D Center Guangxi Huayi Artificial Intelligence Medical Technology Co. Ltd. China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Cardiovascular & Metabolic Medicine University of Liverpool Liverpool United Kingdom Guangzhou Jiayi Software Technology Co. Ltd. China

Accurate segmentation of pulmonary structures is crucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3d CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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SWING: Weakly Supervised Table Question Answering with Self-training via reinforcement Learning

SWING: Weakly Supervised Table Question Answering with Self-...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Sanghyun Cho Hye-Lynn Kim Jung-Hun Lee Hyuk-Chul Kwon Department of Information Convergence Engineering Pusan National University Busan South Korea R&D Center AI LAB HDC LABS Seoul South Korea Department of Artificial Intelligence Dong-Eui University Busan South Korea Department of Computer Science and Engineering Pusan National University Busan South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Previous works with auto-regressive pre-trained language model achieved state-of-art results in semantic parsing tasks. On the other hand, these models generate answers directly, it is difficult to get selected cells for generated answers and answers that require numerical reasoning. In this paper, we present a self-training framework to generate logical forms with weakly supervised question answering. Self-training framework composed of the training procedure and pseudo label correction procedure. In the training procedure, we train models to generate column and row positions of answer cells with supervised training data whose tagged answer cells are a subset of the input table cells. In the pseudo label correction procedure, we correct the tagged dataset for training the model. Furthermore, to enhance the correcting accuracy, we employed reinforcement learning, incorporating a reward function that yields a greater reward as the computed correct answer approaches the benchmark correct answer. Ultimately, we observed an improvement in accuracy from 47.1 to 54.06, a 6.89 increase, using the tapex-base model. Furthermore, we managed to outperform the existing performance, achieving 64.17, using the OmniTab model. Thus, our proposed method, SWING, a self-learning table parsing framework, has shown improved performance in numerical reasoning cases, and can predict rationale cells that allow auto-regressive PLM to compute answers without the need for logical form.

关键词： Training Computational modeling Semantics Training data reinforcement learning Tagging Question answering (information retrieval) Cognition data models Numerical models

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