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The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

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The European Physical Journal Special Topics 2024年 1-11页

作者： You, Yujie Zhou, Fei Yue, Yimin Qiu, Yue Wang, Xinhang Yu, Yangchao Li, Bing Li, Ran Zhang, Le College of Computer Science Sichuan University Chengdu China Jinan Institute of Quantum Technology Jinan China Jinan Branch Hefei National Laboratory Jinan China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu People’s Republic of China Med-X Center for Informatics Sichuan University Chengdu People’s Republic of China West China Hospital- SenseTime Joint Lab Chengdu People’s Republic of China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

The abdominal aortic aneurysm (AAA) rupture is an extremely dangerous surgical acute abdominal disease. An important preventive method for AAA rupture is to simulate the hemodynamic parameters of AAA for medical evaluation through computational fluid dynamics. To fill the gap between the application of quantum computing and the field of biomedical engineering, we report a classical iterative HHL-based quantum algorithm for AAA hemodynamic simulation, and provide a Hamiltonian simulation approach for the coefficient matrix of linearized one-dimensional hemodynamic equations. Simulation experiments on IBM’s Qiskit (virtual machine) show that our proposed algorithm cannot only significantly reduce quantum resource consumption, but also improve simulation accuracy compared with the HHL algorithm. In addition, we have established a search-based circuit mapping which considers the hardware structure of the ‘Xiaohong’ quantum computer and selects mapping circuits with lower noise to improve the accuracy of measured results.

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A Power Balance Control Strategy of Wireless Sensor Network with Collaborating Heuristic 16th

A Power Balance Control Strategy of Wireless Sensor Network ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Yu, Jie Dao, Thi-Kien Ngo, Truong-Giang Nguyen, Trong-The College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong18000 Viet Nam

ISBN: (纸本)9789813367562

In order to effort a strong enough signal, nodes in wireless sensor networks (WSN) have to increase their transmission power that continues to maintain the transmission power. However, a vicious circle is iterated that causes a decline in the overall network performance, low utility, and network life cycle shorten. This paper presents a solution to the power balance control strategy for WSN with collaborating heuristic. A distance between the weight factors and obtained nodes interference value is used to establish a useful interference model for enhancing the signal-to-interference-noise ratio (SINR). The utility function of the nodes residual energy and transmission rate is modeled by apply heuristic strategy. The optimal transmission power is obtained after several iterations of the heuristic algorithm. Simulation results show that the proposed approach can prolong the network life cycle and achieve higher network utility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Heuristic algorithms

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An identity-based anonymous signcryption for vehicular ad hoc networks 2nd

An identity-based anonymous signcryption for vehicular ad ho...

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2nd International Cognitive Cities Conference, IC3 2019

作者： Lu, Yong Yang, Huijie School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China

ISBN: (纸本)9789811561122

The vehicular ad hoc networks (VANETs) can share information of vehicles’ location, speed and route within a certain range by interconnecting vehicles, roadside units (RSU) and the cloud. As people’s daily traffic tools, vehicles reflect users’ daily routines and other privacy. Therefore, privacy protection is very critical in VANETs. However, in the existing schemes, a user’s temporary identity (TID) is generated by the user himself, which is inconvenient to be managed and can be easily forged. The reputation values of users also need to be considered. In this paper, an identity based signcryption scheme for VANETs is proposed. In the novel scheme, TID is generated by key generation center (KCG) and a personal reputation system is produced. The security analysis of the scheme shows that our scheme is practical. © Springer Nature Singapore Pte Ltd 2020.

关键词： Vehicular ad hoc networks

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Stability and generalization of kernel clustering: from single kernel to multiple kernel 22

Stability and generalization of kernel clustering: from sing...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Weixuan Liang Xinwang Liu Yong Liu Sihang Zhou Jun-Jie Huang Siwei Wang Jiyuan Liu Yi Zhang En Zhu School of Computer National University of Defense Technology (NUDT) Changsha China Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China College of Intelligence Science and Technology NUDT Changsha China

ISBN: (纸本)9781713871088

Multiple kernel clustering (MKC) is an important research topic that has been widely studied for decades. However, current methods still face two problems: inefficient when handling out-of-sample data points and lack of theoretical study of the stability and generalization of clustering. In this paper, we propose a novel method that can efficiently compute the embedding of out-of-sample data with a solid generalization guarantee. Specifically, we approximate the eigen functions of the integral operator associated with the linear combination of base kernel functions to construct low-dimensional embeddings of out-of-sample points for efficient multiple kernel clustering. In addition, we, for the first time, theoretically study the stability of clustering algorithms and prove that the single-view version of the proposed method has uniform stability as O(Kn-3/2) and establish an upper bound of excess risk as Õ(Kn-3/2 + n-1/2), where K is the cluster number and n is the number of samples. We then extend the theoretical results to multiple kernel scenarios and find that the stability of MKC depends on kernel weights. As an example, we apply our method to a novel MKC algorithm termed SimpleMKKM and derive the upper bound of its excess clustering risk, which is tighter than the current results. Extensive experimental results validate the effectiveness and efficiency of the proposed method.

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Visual Anomaly Detection for Images: A Systematic Survey

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Procedia computer science 2022年 199卷 471-478页

作者： Jie Yang Ruijie Xu Zhiquan Qi Yong Shi School of Computer and Technology University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem and brought a great variety of novel methods. In this paper, we provide a comprehensive survey of the classical and deep learning-based approaches for visual anomaly detection in the literature. We group the relevant approaches in view of their underlying principles and discuss their assumptions, advantages, and disadvantages carefully. We aim to help the researchers to understand the common principles of visual anomaly detection approaches and identify promising research directions in this field.

关键词： Visual anomaly visual anomaly detection deep learning

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10 GHz Robust polarization modulation towards high-speed satellite-based quantum communication

arXiv

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arXiv 2024年

作者： Wang, Zexu Xu, Huaxing Li, Ju Huang, Jinquan Han, Hui Wang, Changlei Zhang, Ping Yin, Feifei Xu, Kun Liu, Bo Dai, Yitang State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information Technology Beijing100041 China CETC Academy of Electronics and Information Technology Group Co. Ltd. Beijing100041 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangdong Shenzhen518107 China College of Computer National University of Defense Technology Changsha4100730 China

In practical satellite-based quantum key distribution (QKD) systems, the preparation and transmission of polarization-encoding photons suffer from complex environmental effects and high channel-loss. Consequently, the hinge to enhancing the secure key rate (SKR) lies in achieving robust, low-error and high-speed polarization modulation. Although the schemes that realize self-compensation exhibit remarkable robustness. Their modulation speed is constrained to approximately 2 GHz to avoid the interaction between the electrical signal and the reverse optical pulses. Here we utilize the non-reciprocity of the lithium niobate modulators and eliminate the modulation on the reverse optical pulses. As this characteristic is widely available in the radio-frequency band, the modulation speed is no longer limited by the self-compensating optics and can be further increased. The measured average intrinsic QBER of the different polarization states at 10 GHz system repetition frequency is as low as 0.53% over 10 min without any compensation. And the experiment simulation shows that the proposed scheme extends the transmission distance to more than 350 km. Our work can be be efficient performed to the high-speed and high-loss satellite-based quantum communication scenario. © 2024, CC BY.

关键词： Quantum communication

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Joint Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis

arXiv

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arXiv 2023年

作者： Shao, Qian Dai, Ye Ying, Haochao Xu, Kan Wang, Jinhong Chi, Wei Wu, Jian College of Computer Science & Technology Zhejiang University Hangzhou China State Key Laboratory of Ophthalmology Sun Yat-sen University China Second Affiliated Hospital School of Medicine School of Public Health Zhejiang University Hangzhou China Institute of Wenzhou Zhejiang University Wenzhou China

Uveitis demands the precise diagnosis of anterior chamber inflammation (ACI) for optimal treatment. Current diagnostic methods only rely on single-modal data, which leads to poor performance. This may be attributed to the current lack of publicly available multi-modal datasets and the lack of a fusion method for the task of diagnosis for ACI. Specifically, existing fusion methods focus on empowering implicit interactions (i.e., self-attention and its variants) but neglect explicit cross-modal interactions, especially clinical knowledge and imaging properties, which are crucial to the diagnosis task. To this end, we propose a joint Explicit and implicit Cross-Modal Interaction Network (EiCI-Net), which uses anterior segment optical coherence tomography (AS-OCT) images, slit-lamp images, and clinical indicators (tabular data) as input. In detail, we develop CNN-Based Encoders and a Tabular Processing Module (TPM) to extract feature representations of different modalities. Next, we devise an Explicit Cross-Modal Interaction Module (ECIM) to generate attention maps as a kind of explicit clinical knowledge based on the tabular feature maps, then integrate them into the slit-lamp feature maps, allowing the model to focus on more effective informativeness of the slit-lamp images. After that, the Implicit Cross-Modal Interaction Module (ICIM), a transformer-based network, implicitly enhances modality interactions. Finally, we construct a considerable real-world multimodal dataset, which will be made available publicly, and conduct extensive experiments to evaluate the superior performance of EiCI-Net compared with other methods. In addition, the experimental results demonstrate the effectiveness of explicit cross-modal interaction, which has always been ignored before. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical coherence tomography

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Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

arXiv

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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

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Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization

Adaptive Latent Factor Analysis via Generalized Momentum-Inc...

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IEEE International Conference on Networking, Sensing and Control

作者： Jiufang Chen Ye Yuan School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9781665472449

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications.

关键词： Adaptation models Analytical models Tensors Computational modeling Predictive models Prediction algorithms Time complexity

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An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Image segmentation

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