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14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Ma, Chuangui Ge, Aijun Zhang, Jie Army Aviation Institute Beijing China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030142339

In this paper, we introduce a new multi-authority ciphertext policy attribute-based encryption (MA-CP-ABE) system. In our system, there are multiple central authorities (CAs) and attribute authorities (AAs). The CAs will not need to coordinate or even be aware of each other, and so do the AAs. In particular, we present two constructions that will be proved secure in the standard model. Our first scheme is fully secure under static assumptions in composite-order bilinear group, and can work for any monotone access structure. The second one achieves constant size ciphertexts for AND-gate policy in prime-order group. The security can be proved under the decisional linear (DLIN) assumption. © 2019, Springer Nature Switzerland AG.

关键词： Cryptography

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Anti-disturbance control based on uncertain data 20

Anti-disturbance control based on uncertain data

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3rd International Conference on Software engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd International Conference on Big Data and Smart computing, ICBDSC 2020

作者： Cheng, Peng Pan, Feng Yin, Yanyan Kong, Lingshuang Wang, Song Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia College of Electrical and Information Engineering Hunan University of Technology Zhuzhou412007 China

ISBN: (纸本)9781450376907

In this paper, we describe a novel method for constructing probabilistic robust disturbance rejection control for systems contain uncertain data in which a scenario optimization method is used to deal with the nonlinear and unbounded uncertainties. For anti-disturbance, a reduced order disturbance observer is considered and a state-feedback controller is designed. Sufficient conditions are presented to ensure that the resulting closed-loop system is stable and a prescribed H performance index is satisfied. A numerical example is presented to illustrate the effectiveness of the techniques proposed and analyzed. © 2020 Association for computing Machinery.

关键词： Closed loop systems

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Indirect reciprocity in the public goods game with collective reputations

arXiv

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arXiv 2023年

作者： Wei, Ming Wang, Xin Liu, Longzhao Zheng, Hongwei Jiang, Yishen Hao, Yajing Zheng, Zhiming Fu, Feng Tang, Shaoting School of Mathematical Sciences Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100085 China Department of Mathematics Dartmouth College HanoverNH03755 United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth LebanonNH03756 United States

Indirect reciprocity unveils how social cooperation is founded upon moral systems. Within the frame of dyadic games based on individual reputations, the "leading-eight" strategies distinguish themselves in promoting and sustaining cooperation. However, in the real-world societies, there are widespread interactions at the group level, where individuals need to make a singular action choice when facing multiple individuals with different reputations. Here, through introducing the assessment of collective reputations, we develop a framework that embeds group-level reputation structure into public goods game to study the evolution of group-level indirect reciprocity. We show that changing the criteria of group assessment destabilize the reputation dynamics of leading-eight strategies. In a particular range of social assessment criteria, all leading-eight strategies can break the social dilemma in public goods games and sustain cooperation. Specifically, there exists an optimal, moderately set assessment criterion that is most conducive to promoting cooperation. Moreover, in the evolution of assessment criteria, the preference of the leading-eight strategies for social strictness is inversely correlated with the payoff level. Our work reveals the impact of social strictness on prosocial behavior, highlighting the importance of group-level interactions in the analysis of evolutionary games and complex social dynamics. © 2023, CC BY.

关键词： Game theory

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Cascaded adaptive aberration-eliminating multimode fiber imaging

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advanced Photonics 2025年第2期 135-143页

作者： Zhong Wen Qilin Deng Quanzhi Li Yizhou Tan Jingshan Zhong Chiming Zhang Jiahe Zhang Clemens F.Kaminski Ying Gu Xu Liu Qing Yang Zhejiang University College of Optical Science and Engineering State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University International Research Center for Advanced Photonics Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Chinese PLA General Hospital The First Medical Center Department of Laser Medicine Chinese PLA General Hospital Hainan HospitalLaser Medicine Center Zhejiang Laboratory Research Center for Intelligent Manufacturing Computing University of Cambridge Department of Chemical Engineering and Biotechnology

In vivo microscopic imaging inside a biological lumen such as the gastrointestinal tract, respiratory airways, or within blood vessels has faced significant technological challenges for decades. A promising candidate technology is the multimode fiber(MMF) endoscope, which enables minimally invasive diagnostics at a resolution reaching the cellular level. However, for in vivo imaging applications deep inside a biological lumen, sample-induced aberrations and the dynamic dispersion in the MMF make the MMF endoscope a chaotic system with many unknowns, where multiple minor fluctuations can couple and compound into intractable problems. We introduce a dynamically encoding, cascaded, optical, and ultrathin polychromatic light-field endoscopy(DECOUPLE) to tackle this challenge. DECOUPLE includes an adaptive aberration correction that can accurately track and control MMF behavior in the spatial-frequency domain to compensate for chaos introduced during complex dynamic imaging processes. We demonstrate the flexibility and practicality of DECOUPLE for noninvasive volumetric imaging in two colors for light passing through various highly aberrating samples including 120-μm-thick onion epidermal slices and 80-μm-thick layers of fat emulsions. To summarize, we represent a significant step toward practical in vivo imaging deep within biological tissue.

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Applications of nanogenerators for biomedical engineering and healthcare systems

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InfoMat 2022年第2期4卷 53-109页

作者： Wanli Wang Jinbo Pang Jie Su Fujiang Li Qiang Li Xiaoxiong Wang Jingang Wang Bergoi Ibarlucea Xiaoyan Liu Yufen Li Weijia Zhou Kai Wang Qingfang Han Lei Liu Ruohan Zang Mark HRümmeli Yang Li Hong Liu Han Hu Gianaurelio Cuniberti College of Electrical Engineering Qingdao UniversityQingdaoChina Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinanChina College of Electronic Information Qingdao UniversityQingdaoChina Department of Pediatric Surgery The Affiliated Hospital of Qingdao UniversityQingdaoChina College of Physics Qingdao UniversityQingdaoChina Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresdenGermany Center for Advancing Electronics Dresden Technische Universität DresdenDresdenGermany College of Biological Science and Technology University of JinanJinanChina School of Chemistry and Chemical Engineering University of JinanShandongJinanChina College of Energy Soochow Institute for Energy and Materials InnovationsSoochow UniversitySuzhouChina Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province Soochow UniversitySuzhouChina Centre of Polymer and Carbon Materials Polish Academy of SciencesZabrzePoland Institute for Complex Materials IFW DresdenDresdenGermany Institute of Environmental Technology VŠB-Technical University of OstravaOstravaCzech Republic School of Information Science and Engineering University of JinanJinanChina Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinanChina State Key Laboratory of Crystal Materials Center of Bio&Micro/Nano Functional MaterialsShandong UniversityJinanChina College of Chemical Engineering China University of Petroleum(East China)QingdaoChina Dresden Center for Computational Materials Science Technische Universität DresdenDresdenGermany The Dresden Center for Intelligent Materials(DCIM) Technische Universität DresdenDresdenGermany

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare ***,conventional biomedical and healthcare devices have shortcomings such as short service life,large equipment size,and high potential safety ***,the power supply for conventional implantable device remains predominantly *** emerging nanogenerators,which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy,provide an ideal solution for self-powering of biomedical *** combination of nanogenerators and biomedicine has been accelerating the development of self-powered biomedical *** article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications,including power supply,smart sensing,and effective ***,the microbial disinfection and biodegradation performances of nanogenerators have been ***,the protection devices have been discussed such as face mask with air filtering function together with real-time monitoring of human health from the respiration and heat ***,the nanogenerator devices have been categorized by the types of mechanical energy from human beings,such as the body movement,tissue and organ activities,energy from chemical reactions,and gravitational potential ***,the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks.

关键词： biomedical engineering healthcare implantable devices nanogenerators self-powered devices sensors

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Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

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Prediction of multi-parameters in the inverse heat conduction problems

Prediction of multi-parameters in the inverse heat conductio...

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2020 International Conference on Physics, Mechanics and mathematical Science, PMMS 2020

作者： Abdelhamid, Talaat Chen, Rongliang Alam, Mahbub Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Institute for Turbulence-Noise-Vibration Interaction and Control Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Physics and Mathematical Engineering Department Faculty of Electronic Engineering Menoufiya University Menouf Egypt Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This paper studies the prediction of the spatial-dependent-heat transfer coefficient γ(x) and- heat flux q(x), using the modified conjugate gradient method (MCGM). The mathematical formulation of the problem well defined and the existence of the minimizer is investigated. We establish the sensitivity and adjoint equations for computing the gradient with respect to γ(x) and q(x). The proposed algorithm 4.1 is derived for reconstructing γ(x) and q(x) using the MCGM. The numerical experiments are examined to show the efficiency and accuracy of the proposed method. Finally, some conclusions and remarks are given. © Published under licence by IOP Publishing Ltd.

关键词： Conjugate gradient method

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Upper Security Bounds for Coherent-One-Way Quantum key Distribution

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Physical Review Letters 2020年第26期125卷 260510-260510页

作者： Javier González-Payo Róbert Trényi Weilong Wang Marcos Curty Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan 450001 China

The performance of quantum key distribution (QKD) is severely limited by multiphoton pulses emitted by laser sources due to the photon-number splitting attack. Coherent-one-way (COW) QKD has been introduced as a promising solution to overcome this limitation, and thus extend the achievable distance of practical QKD. Indeed, thanks to its experimental simplicity, the COW protocol is already used in commercial applications. Here, we derive simple upper security bounds on its secret key rate, which demonstrate that it scales at most quadratically with the system’s transmittance, thus solving a long-standing problem. That is, in contrast to what has been claimed, this approach is inappropriate for long-distance QKD transmission. Remarkably, our findings imply that all implementations of the COW protocol performed so far are insecure.

关键词： Quantum communication Quantum cryptography

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Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Li, Qian Guo, Shu Chen, Yingjia Ji, Cheng Sheng, Jiawei Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The State Key Laboratory of Software Development Environment Beihang University Beijing100083 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100083 China School of Mathematics Edinburgh University Edinburgh United Kingdom Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China

Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better understanding of the limited data by learning representations under Gaussian distribution. Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution. Further, to better integrate the neighbors with uncertainty characteristics for entity features, we design an uncertainty-aware relational graph neural network (UR-GNN) to conduct convolution operations between the Gaussian distributions. Then, multiple random samplings are conducted for reference triples within the Gaussian distribution to generate smooth reference representations during the optimization. The final completion score for each query instance is measured by the designed uncertainty optimization to make our approach more robust to the noises in few-shot scenarios. Experimental results show that our approach achieves excellent performance on two benchmark datasets compared to its competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A biologically plausible audio-visual integration model for continual learning

arXiv

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arXiv 2020年

作者： Chen, Wenjie Du, Fengtong Wang, Ye Cao, Lihong State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

The problem of catastrophic forgetting has a history of more than 30 years and has not been completely solved yet. Since the human brain has natural ability to perform continual lifelong learning, learning from the brain may provide solutions to this problem. In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption that the integration of audio and visual perceptual information in the medial temporal lobe during learning is crucial to form concepts and make continual learning possible. Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based synaptic tagging and capture as the model’s learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain. We then test our model under this new paradigm. Our experimental results show that the proposed AVIM can achieve state-of-the-art continual learning performance compared with other advanced methods such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning. These results support our assumption that concept formation is essential for continuous lifelong learning and suggest the proposed AVIM is a possible concept formation mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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