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Disease transmission in dynamic social networks constructed by reinforcement learning-driven preventive game

Chaos, Solitons & Fractals 2025年 199卷

作者： Yinuo Qian Dawei Zhao Chengyi Xia School of Control Science and Engineering Tiangong University Tianjin 300387 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan 250014 China School of Artificial Intelligence Tiangong University Tianjin 300387 China

Reinforcement learning delves into the intrinsic behaviors of multi-agents, while complex networks reveal their interactive relationships. In real life, an individual can be considered as a sophisticated multi-agent, whose behavioral choices are influenced by a myriad of factors. When contagious diseases penetrate a population, the adoption of preventive measures represents a manifestation of behavioral selection. This paper introduces reinforcement learning into the multi-agent decision within the network, leveraging the Q-learning algorithm to train individual decision-making. At each time step, individuals select the next preventive strategy based on their utility and strategies, and these choices subsequently alter the network structure, thereby influencing the prevalence of the disease. In addition, the incorporation of additional government incentives prompts individuals to opt for advanced preventive strategies, effectively containing disease transmission within the population. The construction of this model not only showcases the co-evolutionary process between network structure and disease transmission but also validates its efficacy through theoretical analysis and applications in real-world networks, offering a novel perspective towards the epidemic control.

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Intratumor Graph Neural network of Tumor-Associated Collagen Signatures from Multiphoton Microscopy Empowers Prognosis of 995 Breast Cancer Patients

Intratumor Graph Neural Network of Tumor-Associated Collagen...

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Microscopy Histopathology and Analytics, Microscopy 2022

作者： Qiu, Lida Kang, Deyong Wang, Chuan Guo, Wenhui Fu, Fangmeng Wu, Qingxiang Xi, Gangqin He, Jiajia Zheng, Liqin Zhang, Qingyuan Liao, Xiaoxia Li, Lianhuang Chen, Jianxin Tu, Haohua Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou350007 China College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Department of Pathology Fujian Medical University Union Hospital Fuzhou350001 China Department of Breast Surgery Fujian Medical University Union Hospital Fuzhou350001 China Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin150081 China National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana Urbana-Champaign United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We perform cancer prognosis based on 8 collagen signatures obtained by sampling a histological section with multiphoton microscopy. The model with intratumor graph neural network (IGNN) significantly outperforms that ... 详细信息

ISBN: (纸本)9781557528209

关键词： Collagen

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Hybrid Oscillator-Qubit Quantum Processors: Simulating Fermions, Bosons, and Gauge Fields

arXiv

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arXiv 2024年

作者： Crane, Eleanor Smith, Kevin C. Tomesh, Teague Eickbusch, Alec Martyn, John M. Kühn, Stefan Funcke, Lena DeMarco, Michael Austin Chuang, Isaac L. Wiebe, Nathan Schuckert, Alexander Girvin, Steven M. Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science University of Maryland NIST College ParkMD20742 United States Brookhaven National Laboratory UptonNY11973 United States Yale Quantum Institute PO Box 208 334 17 Hillhouse Ave New HavenCT06520-8263 United States Departments of Applied Physics and Physics Yale University New HavenCT06511 United States Department of Computer Science Princeton University PrincetonNJ08544 United States Infleqtion ChicagoIL60604 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CQTA Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany University of Bonn Nussallee 14-16 Bonn53115 Germany Department of Computer Science University of Toronto Canada Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Studies Toronto Canada

We develop a hybrid oscillator-qubit processor framework for quantum simulation of strongly correlated fermions and bosons that avoids the boson-to-qubit mapping overhead encountered in qubit hardware. This framework gives exact decompositions of particle interactions such as density-density terms and gauge-invariant hopping, as well as approximate methods based on the Baker-Campbell Hausdorff formulas including the magnetic field term for the U(1) quantum link model in (2 + 1)D. We use this framework to show how to simulate dynamics using Trotterisation, perform ancilla-free partial error detection using Gauss's law, measure non-local observables, estimate ground state energies using a oscillator-qubit variational quantum eigensolver as well as quantum signal processing, and we numerically study the influence of hardware errors in circuit QED experiments. To show the advantages over all-qubit hardware, we perform an end-to-end comparison of the gate complexity for the gauge-invariant hopping term and find an improvement of the asymptotic scaling with the boson number cutoff S from O(log(S)2) to O(1) in our framework as well as, for bosonic matter, a constant factor improvement of better than 104. We also find an improvement from O(log(S)) to O(1) for the U(1) magnetic field term. While our work focusses on an implementation in superconducting hardware, our framework can also be used in trapped ion, and neutral atom hardware. This work establishes digital quantum simulation with hybrid oscillator-qubit hardware as a viable and advantageous method for the study of qubit-boson models in materials science, chemistry, and high-energy physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework

arXiv

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arXiv 2025年

作者： Huang, Bin He, Binzhong Chen, Yanhan Liu, Zhili Wang, Xinyue Li, Binxuan Liu, Qiegen School of Mathematics and Computer Sciences Nanchang University Nanchang China Ji Luan Academy Nanchang University Nanchang China Department of Orthopedics The First Affiliated Hospital Jiangxi Medical College Nanchang University Nanchang China Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases Nanchang China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Information Engineering Nanchang University Nanchang China

Deep learning has significantly advanced PET image reconstruction, achieving remarkable improvements in image quality through direct training on sinogram or image data. Traditional methods often utilize masks for inpainting tasks, but their incorporation into PET reconstruction frameworks introduces transformative potential. In this study, we propose an advanced PET reconstruction framework called Diffusion tRansformer mEets rAndom Masks (DREAM). To the best of our knowledge, this is the first work to integrate mask mechanisms into both the sinogram domain and the latent space, pioneering their role in PET reconstruction and demonstrating their ability to enhance reconstruction fidelity and efficiency. The framework employs a high-dimensional stacking approach, transforming masked data from two to three dimensions to expand the solution space and enable the model to capture richer spatial relationships. Additionally, a mask-driven latent space is designed to accelerate the diffusion process by leveraging sinogram-driven and mask-driven compact priors, which reduce computational complexity while preserving essential data characteristics. A hierarchical masking strategy is also introduced, guiding the model from focusing on fine-grained local details in the early stages to capturing broader global patterns over time. This progressive approach ensures a balance between detailed feature preservation and comprehensive context understanding. Experimental results demonstrate that DREAM not only improves the overall quality of reconstructed PET images but also preserves critical clinical details, highlighting its potential to advance PET imaging technology. By integrating compact priors and hierarchical masking, DREAM offers a promising and efficient avenue for future research and application in PET imaging. The open-source code is available at: https://***/yqx7150/DREAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Masks

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Case data-mining analysis for patients with oesophageal cancer

Case data-mining analysis for patients with oesophageal canc...

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作者： Cao, Yanning Zhang, Xiaoshu Wang, Jin Network Information Center Binzhou Medical University Yantai264003 China School of Basic Medical Sciences Binzhou Medical University Yantai264003 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

We are in an era of digital medicine in which physicians can generate copious patient data, but tools to analyse these data are limited. Thus, we used case data from patients with oesophageal cancer from a medical institution, removed incomplete information, and quantified the textual data according to recommendations from the corresponding physicians. We used different classification algorithms to process the data, predict patient survival, and compare accuracies across algorithms. Our experimental results show that the BayesNet algorithm was highly accurate and precise, and, thus, may represent a promising data-mining tool. © 2020 Inderscience Enterprises Ltd.

关键词： Data mining

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A modified Particle Swarm Optimization algorithm with enhanced search quality and population using Hummingbird Flight patterns

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Decision Analytics Journal 2023年 7卷

作者： Zare, Mohsen Akbari, Mohammad-Amin Azizipanah-Abarghooee, Rasoul Malekpour, Mostafa Mirjalili, Seyedali Abualigah, Laith Department of Electrical Engineering Faculty of engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering University of Cyprus Cyprus Green energy and mobility department RINA Tech UK Ltd Manchester United Kingdom Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane 4006 QLD Australia University Research and Innovation Center Obuda University Budapest 1034 Hungary Computer Science Department Prince Hussein Bin Abdullah College for Information Technology Al AlBayt University Mafraq 130040 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position changes are made step by step throughout the flight, (3) Flight energy is conserved during the nectar-searching process, (4) Hummingbirds do not fly in large groups in confined spaces, and (5) Simultaneous position changes in all directions are not realistic. A comprehensive study on two CEC-2010 and CEC-2013 benchmark suites is conducted to verify the effectiveness of the proposed PSO-HBF algorithm. The proposed algorithm is also evaluated and compared to other well-known PSO algorithms using shifted and rotated CEC 2005 and CEC 2014 benchmark functions. Four cases in economic dispatch, the 10-unit reserve constraint, and the 30-unit dynamic economic dispatch (DED) are further examined. The last two cases investigate how the proposed PSO-HBF deals with large-scale practical problems. The results demonstrated that the PSO-HBF algorithm is superior to seven other modified algorithms, improving eight and ten functions on the 2010 and 2013 benchmarks, respectively. Furthermore, achieving the third rank among the nineteen improved PSO algorithms based on the 2005 functions confirms the effectiveness of the proposed algorithm. Moreover, in two cases of the DED problem, the results of PSO-HBF show significant improvement over previously published papers. The PSO-HBF algorithm's source code can be accessed publicly at http://***/projects/psohbf. © 2023 The Author(s)

关键词： Algorithm Becnhmark Dynamic economic dispatch Economic dispatch Evolutionary algorithm Hummingbird flight Large-scale optimization Optimization Particle swarm optimization

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REPRESENTATION-FINITE TENSOR PRODUCT OF SIMPLY CONNECTED ALGEBRAS

arXiv

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arXiv 2021年

作者： Miyamoto, Kengo Wang, Qi Department of Computer and Information Science Graduate School of Science and Engineering Ibaraki University Ibaraki Hitachi316-8511 Japan Yau Mathematical Sciences Center Tsinghua University Beijing100084 China

We determine the representation-finiteness of A ⊗ B, where both A and B are simply connected algebras with at least three simple *** Codes 16G10, 16G60, 16D80, 16S10 Copyright © 2021, The Authors. All rights rese... 详细信息

关键词： Tensors

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Default mode network static-dynamic functional signatures in first-episode drug-naive major depressive disorder

Comprehensive Psychoneuroendocrinology

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Comprehensive Psychoneuroendocrinology 2025年 23卷

作者： Xiaoyun Liu Kaimo Ding Fan Li Xiaotong Zhang Zhenghua Hou Yingying Yin Youyong Kong Yonggui Yuan Department of Psychosomatics and Psychiatry Zhongda Hospital School of Medicine Jiangsu Provincial Key Laboratory of Brain Science and Medicine Southeast University Nanjing China Zhenjiang Mental Health Center Jiangsu Province Zhenjiang China Lab of Image Science and Technology School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

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Automatic Generation of Medical Report with Knowledge Graph 2021

Automatic Generation of Medical Report with Knowledge Graph

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2021 10th International Conference on Computing and Pattern Recognition

作者： Haifeng Zhao Jie Chen Lili Huang Tingting Yang Wanhai Ding Chuanfu Li School of Computer Science and Technology Anhui University China School of Computer Engineering Anhui Wenda University of Information Engineering China Department of Neurosurgery Shanghai Jiao Tong University Affiliated Sixth People's Hospital China and School of Medicine Shanghai Jiao Tong University China The First Affiliated Hospital of Anhui University of Chinese Medicine Institute of Artificial Intelligence China and Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

ISBN: (纸本)9781450390439

As an important part of medical diagnosis, medical images are widely used in the diagnosis and treatment of diseases. Radiologists need to write reports for a large number of medical images every day, which usually occupies most of the radiologists’ work time. Automatic medical report generation becomes an urgent problem to be solved. Most of the existing works in the domain of medical report generation have some problems. The first is abnormalities are not well represented and the connection between them is usually ignored. Second, there is no or no good fusion between image and semantic information. We present to establish abnormality graph with prior knowledge to represent abnormalities and their relationships. And we use a multimodal interaction module to fuse image and semantic information. Experimental results on two datasets, a public English medical reports dataset and a Chinese medical reports dataset conducted by ourselves, show that our method outperforms the state-of-art methods.

关键词： Multimodal interaction Knowledge Graph Medical Report Generation

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

arXiv

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arXiv 2022年

作者： Horváth, Dániel Bocsi, Kristóf Erdős, Gábor Istenes, Zoltán Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eötvös Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data. © 2022, CC BY-NC-ND.

关键词： Cyber Physical System

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