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Transferable Belief Model on Quantum Circuits

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arXiv 2024年

作者： Zhou, Qianli Luo, Hao Pan, Lipeng Deng, Yong Bossé, Éloi The Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu610054 China The College of Information Engineering Northwest A&F University Yangling712100 China The School of Medicine Vanderbilt University Nashville37240 United States The Department of Image and Information Processing IMT-Atlantique Brest29238 France The Expertises Parafuse Quebec CityG1W 4N1 Canada

The transferable belief model, as a semantic interpretation of Dempster-Shafer theory, enables agents to perform reasoning and decision making in imprecise and incomplete environments. The model offers distinct semantics for handling unreliable testimonies, allowing for a more reasonable and general process of belief transfer compared to the Bayesian approach. However, because both the belief masses and the structure of focal sets must be considered when updating belief functions—leading to extra computational complexity during reasoning—the transferable belief model has gradually lost favor among researchers in recent developments. In this paper, we implement the transferable belief model on quantum circuits and demonstrate that belief functions offer a more concise and effective alternative to Bayesian approaches within the quantum computing framework. Furthermore, leveraging the unique characteristics of quantum computing, we propose several novel belief transfer approaches. More broadly, this paper introduces a new perspective on basic information representation for quantum AI models, suggesting that belief functions are more suitable than Bayesian approach for handling uncertainty on quantum circuits. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

Management of Scientific Agricultural Digital Ecosystems Based on Ecosystem Classification

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Management of Scientific Agricultural Digital Ecosystems Bas...

International Conference on Management of Large-Scale System Development (MLSD)

作者： Vladimir Kulba Viktor Medennikov Laboratory of Modular Data Processing and Control Systems V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Moscow Russia Information and Computing Systems Department Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case; the model enables assessing the impact science exerts on the regions’ socioeconomic position in order to build an adequate response to the advanced economies’ regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country’s digitalization.

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The Main Threat to computer Network Security in Smart Cities

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The Main Threat to Computer Network Security in Smart Cities

Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman Om Vikash Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT. Near East University Near East Boulevard Mersin 10 Turkiye Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Mersin10 Turkey Department of Chemical Engineering Waterloo University ON Canada Department Chemical and Biological Engineerin Clean Energy processing Friedrich- Alexander University Erlangen Germany Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India Department of Computer Science and Engineering) Graphic Era Hill University Dehradun India

As the concept of smart cities continues to gain momentum, the integration of various technologies has become essential for creating sustainable and efficient urban environments. However, with the proliferation of interconnected devices and systems, the risk of network security threats has increased significantly. This research article aims to identify and analyze the main threats to network security in smart cities. By exploring the potential vulnerabilities and challenges posed by the interconnected infrastructure, this study provides insights into the key areas that require attention to ensure the resilience and protection of smart cities. The findings of this research emphasize the need for robust security measures and proactive strategies to mitigate the identified threats.

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

Joint Task Scheduling and Container image Caching in Edge Computing

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis 39

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Promptable Representation Distribution Learning and Data Aug...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Kunming Jiang, Zhiguo Shi, Jun Wang, Wei Wu, Haibo Zheng, Yushan Beijing Advanced Innovation Center on Biomedical Engineering School of Engineering Medicine Beihang University China Image Processing Center School of Astronautics Beihang University China Tianmushan Laboratory China School of Software Hefei University of Technology China Department of Pathology The First Affiliated Hospital of USTC China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China China

ISBN: (纸本)157735897X

Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Labeled data

A coupled-mechanisms modelling framework for neurodegeneration

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arXiv 2023年

作者： He, Tiantian Thompson, Elinor Schroder, Anna Oxtoby, Neil P. Abdulaal, Ahmed Barkhof, Frederik Alexander, Daniel C. UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Radiology & Nuclear Medicine Amsterdam University Medical Center Vrije Universiteit Amsterdam Netherlands

Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer’s disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. This provides a combination of mechanisms that best explains the observations for each individual from the ADNI dataset. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

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SSRN

SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

A multi-stage semi-supervised learning for ankle fracture classification on CT images

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arXiv 2024年

作者： Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang The School of Computer Science and Engineering Southeast University Nanjing210096 China The Peking University People's Hospital Beijing100000 China The Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou510515 China The Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications Southeast University Ministry of Education China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China

Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes. Copyright © 2024, The Authors. All rights reserved.

关键词： image classification