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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mojisola Grace Asogbon Yaping Huai Oluwarotimi Williams Samuel Zhengxiang Jing Yixin Ma Jingyu Liu Yibo Jiang Yunfa Fu Guanglin Li Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke *** Relevance: This study indicated that optimal s

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Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network

arXiv

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arXiv 2024年

作者： Pan, Gang Wang, Chen Sui, Zhijie Guo, Shuai Lv, Yaozhi Li, Honglie Sun, Di Xia, Zixia College of Intelligence and Computing Tianjin University Tianjin China Department of Municipal Engineering Hefei University of Technology Anhui China Key laboratory of Infrastructure Durability Tianjin Municipal Engineering Design and Research Institute Tianjin China West-East Gas Pipeline Company PipeChina Shanghai China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China Donald Bren School of Information and Computer Sciences University of California IrvineCA United States

The Quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information based on the correlation. By combining these modules, the integration of depth priors significantly enhances both visual quality and performance benchmarks. Besides, in pursuit of computational efficiency and compactness, a super-resolution knowledge distillation model based on an attention mechanism is introduced. This mechanism facilitates the acquisition of feature similarity between a more complex teacher model and a streamlined student model, with the latter being a lightweight version of DSRNet. Experimental results demonstrate that DSRNet significantly improves PSNR and SSIM compared with other methods. This study also conducts experiments on sewer defect semantic segmentation, object detection, and classification on the Pipe dataset and Sewer-ML dataset. Experiments show that the method can improve the performance of low-resolution sewer images in these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Decoherence on staggered quantum walks

引用

Physical Review A 2022年第3期105卷 032452-032452页

作者： Raqueline A. M. Santos Franklin de L. Marquezino Center for Quantum Computer Science Faculty of Computing University of Latvia Raina bulvaris 19 Riga LV-1586 Latvia Systems Engineering and Computer Science Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering Federal University of Rio de Janeiro Avenida Horacio Macedo 2030 H319 Rio de Janeiro 21941-972 Brazil Multidisciplinary Laboratory for Research in Computer Science Federal University of Rio de Janeiro Rodovia Washington Luiz 19593 Duque de Caxias 25240-005 Brazil

The decoherence phenomenon has been widely studied in different types of quantum walks. In this work we show how to model decoherence inspired by percolation on staggered quantum walks. Two models of unitary noise are described: breaking polygons and breaking vertices. The evolution operators subject to these noises are obtained, and the equivalence to the coined quantum walk model is presented. Further, we numerically analyze the effect of these decoherence models on the two-dimensional grid of 4-cliques. We examine how these perturbations affect the quantum walk based search algorithm in this graph and how expanding the tessellation intersection can make it more robust against decoherence.

关键词： Network searches Quantum algorithms Quantum computation Quantum information processing Quantum walks

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Federated Learning for Medical Image Classification: Advances, Challenges and Opportunities

SSRN

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SSRN 2023年

作者： Zhang, Xiaobo Zhao, Xiaole Wu, Yunyang Zheng, Hailong Li, Yang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Artificial Intelligence Research Institute Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Medical images are private integrations comprising private patient information owned by various hospitals and relevant research institutes, and the generated image data can be utilized to infer physical health conditions. In recent years, more and more AI researchers have used medical images as datasets for their analysis purposes, which will cause relevant personnel to worry about their privacy. As a distributed machine learning framework, federated learning can effectively solve the problem of data privacy. It participates in and completes model training without sharing personal data, with strong universality and good results. In this work, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Wilhelm Conrad Röntgen (X-Ray) as well as other typical medical imaging modes are used as research objects to summarize, analyze and look forward to the previous work related to federal learning in the field of medical image analysis. On the one hand, this survey classifies and compares the existing federal learning framework and algorithm for different medical image datasets;on the other hand, it discusses the relevant federal studies that are widely used in medical images, analyses the problems that need to be solved in the field of medical images, and looks forward to future studies. © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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International Conference on Data engineering

作者： Lihui Lin Yanghui Rao Haoran Xie Raymond Y. K. Lau Jian Yin Fu Lee Wang Qing Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Lingnan University New Territories Hong Kong SAR Department of Information Systems City University of Hong Kong Kowloon Hong Kong SAR Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou China School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

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Short-Term and Mid-Term Population Projections Especially of Germany via WASD Neural Network: Theoretical Parts

Short-Term and Mid-Term Population Projections Especially of...

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第40届中国控制会议

作者： Zhiwen Yuan Siyuan Feng Chengxu Ye Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Computer Science Qinghai Normal University

Population has a direct impact on the demands of consumption, resource, and employment. Population factors, such as size and growth rate, are crucial in making decisions and plans of trade, environment protection, resource allocation and human resource development. Thus, population projection is an essential part of strategy development for companies as well as governments, and draws the attention of many researchers. Some projection methods have been developed, but most of the popular ones require a vast knowledge in demography and only can be operated by skilled professionals. In this paper, as an improvement, we theoretically develop a regression model and a subtrend model of WASD（weights and structure determination）neural network in a simultaneous manner to make population projections especially of Germany in the mid term and near ***, we aim at utilizing the learning capabilities of the WASD neural-network models with single input and multiple inputs, having been very popular recently, to propose these easy-used and high accuracy models for verifications and projections via later computer experiments.

关键词： Germany population Projections Neural network WASD（weights and structure determination） Multiple inputs

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Design and Implementation of Big Data Management Platform for Android Applications 20

Design and Implementation of Big Data Management Platform fo...

引用

3rd International Conference on Big Data Technologies, ICBDT 2020

作者： Han, Bing Chen, Zhenxiang Liu, Cong Shang, Mingyue School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9781450387859

In recent years, the number of Android malicious applications has grown rapidly. In the field of network security, the detection of Android malicious applications has been a hot spot. Traditional Android malicious application detection has two methods: Dynamic detection and static detection. With the development of network technology, network traffic has increased dramatically, analysis and researchers pay attention to malware detection based on network traffic. As the number of applications increases, application data management becomes particularly important. This paper proposes a method of the collection, store, analysis and visualization of Android applications. This platform provides a simple way to access data, which has broad application prospects. © 2020 ACM.

关键词： Information management

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Landau–Zener–Stückelberg Interference in Nonlinear Regime

引用

Chinese Physics Letters 2019年第12期36卷 36-39页

作者： Tong Wu Yuxuan Zhou Yuan Xu Song Liu Jian Li Department of Physics Harbin Institute of TechnologyHarbin 150001 Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055 Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055 Center for Quantum Computing Peng Cheng LaboratoryShenzhen 518055

Landau-Zener-Stückelberg(LZS)interference has drawn renewed attention to quantum information processing research because it is not only an effective tool for characterizing two-level quantum systems but also a powerful approach to manipulate quantum *** quantum circuits,due to their versatile tunability and degrees of control,are ideal platforms for studying LZS interference *** use a superconducting Xmon qubit to study LZS interference by parametrically modulating the qubit transition frequency *** dc flux biasing of the qubit slightly far away from the optimal flux point,the qubit excited state population shows an interference pattern that is very similar to the standard LZS interference in linear regime,except that all bands shift towards lower frequencies when increasing the rf modulation *** dc flux biasing close to the optimal flux point,the negative sidebands and the positive sidebands behave differently,resulting in an asymmetric interference *** experimental results are also in good agreement with our analytical and numerical simulations.

关键词： bands quantum Landau

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An Unbiased Quantum Random Number Generator Based on Boson Sampling

arXiv

引用

arXiv 2022年

作者： Shi, Jinjing Zhao, Tongge Wang, Yizhi Yu, Chunlin Lu, Yuhu Shi, Ronghua Zhang, Shichao Wu, Junjie School of Computer Science and Engineering Central South University Changsha410083 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China China Greatwall Quantum Laboratory China Greatwall Technology Group CO. LTD. Changsha410073 China

It has been proven that Boson sampling is a much promising model of optical quantum computation, which has been applied to designing quantum computer successfully, such as "Jiuzhang". However, the meaningful randomness of Boson sampling results, whose correctness and significance were proved from a specific quantum mechanical distribution, has not been utilized or exploited. In this research, Boson sampling is applied to design a novel Quantum Random Number Generator (QRNG) by fully exploiting the randomness of Boson sampling results, and its prototype system is constructed with the programmable silicon photonic processor, which can generate uniform and unbiased random sequences and overcome the shortcomings of the existing discrete QRNGs such as source-related, high demand for the photon number resolution capability of the detector and slow self-detection generator speed. Boson sampling is implemented as a random entropy source, and random bit strings with satisfactory randomness and uniformity can be obtained after post-processing the sampling results. It is the first approach for applying the randomness of Boson sampling results to develop a practical prototype system for actual tasks, and the experiment results demonstrate the designed Boson sampling-based QRNG prototype system pass 15 tests of the NIST SP 800-22 statistical test component, which prove that Boson sampling has great potential for practical applications with desirable performance besides quantum advantage. Copyright © 2022, The Authors. All rights reserved.

关键词： Sampling

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Deep Attention Model with Multiple Features for Rumor Identification 3rd

Deep Attention Model with Multiple Features for Rumor Identi...

引用

3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Wang, Lina Wang, Wenqi Chen, Tong Ke, Jianpeng Tang, Benxiao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9789811597381

With the rapidly development of social networks and advances in natural language processing (NLP) techniques, rumors are extremely common and pose potential threats to community. In recent years, massive efforts are working on detecting rumors by using various techniques like simply investigating the content of texts, exploring the abnormality of propagation. However, these techniques are not ready to fully tackling this emerging threats due to the dynamic variations of rumors in a period of time. In this paper, we observed that the user feedback provides a clean signal for determining the trend of rumors, thus we combine the text content and the improved representation of network topology to characterize the dynamic features of rumors in a period of time. In detection, we employ a deep attention model with proposed features for spotting the minor differences between legitimate news and rumors. Experimental results show that our approach give an accuracy more than 94.7% in detecting rumors and outperforms previous approaches. Our studies also give a new insight that user interactions could be working as an important asset in rumor identification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network topology

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