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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (ai) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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CT Scan Synthesis for Promoting computer-aided Diagnosis Capacity of COVID-19 16th

CT Scan Synthesis for Promoting Computer-Aided Diagnosis Cap...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on ai-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct ai-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Prediction of concrete compressive strength employing machine learning techniques

引用

Materials Today: Proceedings 2023年

作者： Keshav Malhotra Dhaneshwar Mishra Charanjeet Singh Tumrate Department of Computer Science (AI and ML) Manipal University Jaipur India Department of Mechanical Engineering Manipal University Jaipur India Multiscale Simulation Research Center (MSRC) Manipal University Jaipur India Department of Civil Engineering Manipal University Jaipur India

Machine learning (ML) methods have proven to be reliable techniques in estimating, classifying, and predicting material strength based on varying material properties. It is observed that the nature of the problem and the data at hand governs the selection of an appropriate ML method. Therefore, to determine the accuracy of ML models to predict concrete compressive strength , fifteen different ML algorithms were applied to a given concrete compressive strength dataset. Among all the ML algorithms used, the prediction accuracy of the Support Vector Regressor (SVR) was the highest (88.18%) due to its effective performance when dealing with continuous target variables and nonlinear relationships among the features and the target.

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Resource estimations for the Hamiltonian simulation in correlated electron materials

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Physical Review A 2022年第1期106卷 012612-012612页

作者： Shu Kanno Suguru Endo Takeru Utsumi Tomofumi Tada Department of Materials Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8552 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama Kanagawa 226-8501 Japan Kyushu University Platform of Inter/Transdisciplinary Energy Research (Q-PIT) Kyushu University Fukuoka 819-0395 Japan

Correlated electron materials, such as superconductors and magnetic materials, are regarded as fascinating targets in quantum computing. However, the quantitative resources, specifically the number of quantum gates and qubits, required to perform a quantum algorithm to simulate correlated electron materials remain unclear. In this study, we estimate the resources required for the Hamiltonian simulation algorithm for correlated electron materials, specifically for organic superconductors, iron-based superconductors, binary transition-metal oxides, and perovskite oxides, using the fermionic swap network. The effective Hamiltonian derived using the ab initio downfolding method is adopted for the Hamiltonian simulation, and a procedure for the resource estimation by using the fermionic swap network for the effective Hamiltonians including the exchange interactions is proposed. For example, in the system for the 102 unit cells, the estimated numbers of gates per Trotter step and qubits are approximately 107 and 103, respectively, on average for the correlated electron materials. Furthermore, our results show that the number of interaction terms in the effective Hamiltonian, especially for the Coulomb interaction terms, is dominant in the gate resources when the number of unit cells constituting the whole system is up to 102, whereas the number of fermionic swap operations is dominant when the number of unit cells is more than 103.

关键词： Quantum algorithms Strongly correlated systems Ab initio calculations Density functional theory

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Determination of photonuclear reaction cross-sections on stable p-shell nuclei by using deep neural networks

arXiv

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arXiv 2020年

作者： Akkoyun, Serkan Kaya, Hüseyin Seker, Abdulkadir Yesilyurt, Saliha Department of Physics Faculty of Science Sivas Cumhuriyet University Sivas Turkey Department of Computer Engineering Sivas Cumhuriyet University Faculty of Engineering Sivas Turkey Artificial Intelligence Systems and Data Science Application and Research Center Sivas Cumhuriyet University Sivas58140 Turkey

The photonuclear reactions which is induced by high-energetic photon are one of the important type of reactions in the nuclear structure studies. In this reaction, a target material is bombarded by photons with the energies in the range of gamma-ray energy scale and the photons can statistically be absorbed by a nucleus in the target material. In order to get rid of the excess energies of the excited target nuclei, it can first emit protons, neutrons, alphas and light particles according to the separation energy thresholds. After this emitting process, generally an unstable nucleus can be formed. By the investigation of this products forming after photonuclear reactions, nuclear structure information can be obtained. In the present work, (γ, n) photonuclear reaction cross-sections on stable p-shell nuclei have been estimated by using neural network method. The main purpose of this study is to find neural network structures that give the best estimations on the cross-sections and to compare them with each other and available literature data. According to the results, the method is convenient for this task. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Wearable Biosensors in Congenital Heart Disease: Needs to Advance the Field

JACC: Advances

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JACC: Advances 2023年第2期2卷 100267-100267页

作者： Tandon, Animesh Nguyen, Hoang H. Avula, Sravani Seshadri, Dhruv R. Patel, Akash Fares, Munes Baloglu, Orkun Amdani, Shahnawaz Jafari, Roozbeh Inan, Omer T. Drummond, Colin K. Department of Pediatric Cardiology Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Cleveland Clinic Children's Center for Artificial Intelligence (C4AI) Cleveland Clinic Children's Cleveland OH United States Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic Cleveland OH United States Department of Biomedical Engineering Case School of Engineering at Case Western Reserve University Cleveland OH United States Division of Cardiology Department of Pediatrics UT Southwestern Medical Center Dallas TX United States Department of Orthopaedics University Hospitals Cleveland Medical Center Cleveland OH United States Department of Critical Care Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Departments of Biomedical Engineering Computer Science and Electrical Engineering Texas A&M University College Station TX United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD;how clinical and research uses of wearable biosensors must consider various CHD physiologies;the technical challenges in developing wearable biosensors for CHD;and special considerations for digital biomarkers in CHD. © 2023 The Authors

关键词： artificial intelligence congenital heart disease wearable biosensors wearables

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving data Fusion in Healthcare Internet of Things

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul02841 Korea Republic of

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： Sensitive data

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Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

arXiv

引用

arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

arXiv

引用

arXiv 2022年

作者： Zhang, Jiayi Zhang, Jing Björnson, Emil ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering Linköping University Linköping Sweden The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Power control

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