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MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network

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arXiv 2024年

作者： Xu, Yinlong Liu, Xiaoqiang Kong, Zitai Wu, Yixuan Wang, Yue Lu, Yingzhou Gao, Honghao Wu, Jian Xu, Hongxia College of Computer Science & Technology Zhejiang University Hangzhou China Department of Gastroenterology First Hospital of Quanzhou Affiliated to Fujian Medical University Quanzhou China School of Public Health Zhejiang University Hangzhou China State Key Laboratory of Transvascular Implantation Devices The Second Affiliated Hospital Liangzhu Laboratory Zhejiang University School of Medicine Hangzhou China Innovation Institute for Artificial Intelligence in Medicine Zhejiang University Hangzhou China School of Medicine Stanford University United States School of Computer Engineering and Science Shanghai University Shanghai China College of Future Industry Gachon University Korea Republic of

Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the advent of deep learning, numerous innovative models have been introduced for diagnosing arrhythmias using Electrocardiogram (ECG) signals. However, recent studies solely focus on the performance of models, neglecting the interpretation of their results. This leads to a considerable lack of transparency, posing a significant risk in the actual diagnostic process. To solve this problem, this paper introduces MambaCapsule, a deep neural networks for ECG arrhythmias classification, which increases the explainability of the model while enhancing the *** model utilizes Mamba for feature extraction and Capsule networks for prediction, providing not only a confidence score but also signal features. Akin to the processing mechanism of human brain, the model learns signal features and their relationship between them by reconstructing ECG signals in the predicted selection. The model evaluation was conducted on MIT-BIH and PTB dataset, following the AAMI standard. MambaCapsule has achieved a total accuracy of 99.54% and 99.59% on the test sets respectively. These results demonstrate the promising performance under the standard test protocol. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Expanding semantic knowledge for zero-shot graph embedding

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arXiv 2021年

作者： Wang, Zheng Shao, Ruihang Wang, Changping Hu, Changjun Wang, Chaokun Gong, Zhiguo State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Computer Science and Technology University of Science and Technology Beijing Beijing China Kwai Inc. China School of Software Tsinghua University Beijing China

Zero-shot graph embedding is a major challenge for supervised graph learning. Although a recent method RECT has shown promising performance, its working mechanisms are not clear and still needs lots of training data. In this paper, we give deep insights into RECT, and address its fundamental limits. We show that its core part is a GNN prototypical model in which a class prototype is described by its mean feature vector. As such, RECT maps nodes from the raw-input feature space into an intermediate-level semantic space that connects the raw-input features to both seen and unseen classes. This mechanism makes RECT work well on both seen and unseen classes, which however also reduces the discrimination. To realize its full potentials, we propose two label expansion strategies. Specifically, besides expanding the labeled node set of seen classes, we can also expand that of unseen classes. Experiments on real-world datasets validate the superiority of our methods. © 2021, CC BY.

关键词： Semantics

Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network

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arXiv 2022年

作者： Wang, Chao Liu, Lei Jiang, Chunxiao Wang, Shangguang Zhang, Peiying Shen, Shigen Qingdao266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Xidian Guangzhou Institute of Technology Guangzhou510555 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Tsinghua Space Center Tsinghua University Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China

Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15% and 8.35% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

Cross-View Panorama Image Synthesis

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arXiv 2022年

作者： Wu, Songsong Tang, Hao Jing, Xiao-Yuan Zhao, Haifeng Qian, Jianjun Sebe, Nicu Yan, Yan The School of Computer Science Guangdong University of Petrochemical Technology Maoming525000 China The Department of Information Technology and Electrical Engineering ETH Zurich Zurich8092 Switzerland The School of Computer Science Wuhan University Wuhan430072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The School of Software Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Hoperun Software Co. Ltd. Nanjing210012 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Trento Trento38123 Italy The Department of Computer Science Illinois Institute of Technology Chicago60616 United States

In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a topview aerial image, which is a challenging problem due to the large gap between the two image domains with different viewpoints. Instead of learning cross-view mapping in a feedforward pass, we propose a novel adversarial feedback GAN framework named PanoGAN with two key components: an adversarial feedback module and a dual branch discrimination strategy. First, the aerial image is fed into the generator to produce a target panorama image and its associated segmentation map in favor of model training with layout semantics. Second, the feature responses of the discriminator encoded by our adversarial feedback module are fed back to the generator to refine the intermediate representations, so that the generation performance is continually improved through an iterative generation process. Third, to pursue high-fidelity and semantic consistency of the generated panorama image, we propose a pixel-segmentation alignment mechanism under the dual branch discrimiantion strategy to facilitate cooperation between the generator and the discriminator. Extensive experimental results on two challenging cross-view image datasets show that PanoGAN enables highquality panorama image generation with more convincing details than state-of-the-art approaches. The source code and trained models are available at https://***/sswuai/PanoGAN. Copyright © 2022, The Authors. All rights reserved.

关键词： Feedback

Engineered Probiotics Enable Targeted Gut Delivery of Dual Gasotransmitters for Inflammatory Bowel Disease Therapy

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Angewandte Chemie 2025年

作者： Tengfei Ma Guihai Gan Jian Cheng Zhiqiang Shen Guoying Zhang Shiyong Liu Jinming Hu Department of Pharmacy The First Affiliated Hospital of University of Science and Technology of China (USTC) Division of Life Sciences and Medicine and State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei Anhui Province 230026 China

Inflammatory bowel disease (IBD) remains an incurable condition, often accompanied by high rates of anxiety and depression, further diminishing the quality of life of patients. Endogenous gasotransmitters, such as carbon monoxide (CO) and hydrogen sulfide (H₂S), exhibit potent anti-inflammatory and immunomodulatory effects. However, their therapeutic application is limited by challenges in targeted delivery to affected tissues. Here, we propose a novel strategy for targeted gut delivery of CO/H 2 S through engineering Escherichia coli Nissle 1917 (EcN) with CO/H 2 S-releasing copolymer (POSR) loading. This engineered probiotic (POSR@EcN) enhances EcN colonization in the intestine and enables controlled, localized release of CO/H 2 S at inflamed sites. The release of CO/H 2 S modulates inflammation, restores intestinal barrier integrity, and reshapes gut microbiota by promoting beneficial bacteria and increasing short-chain fatty acids production, effectively alleviating IBD symptoms. Notably, targeted CO/H 2 S delivery also elevates neuroprotective metabolites like indoleacetic acid and γ-aminobutyric acid, reducing neuroinflammation via the gut-brain axis and mitigating anxiety- and depression-like behaviors in IBD mice. This approach highlights the potential of EcN as a probiotic carrier for the targeted delivery of gasotransmitters, offering a promising strategy for IBD treatment.

关键词： Carbon monoxide Gut–brain axis Hydrogen sulfide Inflammatory bowel disease Probiotics

A Multi-Level Thresholding Image Segmentation Based on an Improved Artificial Bee Colony Algorithm 2nd

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A Multi-Level Thresholding Image Segmentation Based on an Im...

2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Image segmentation

2-inch, 2, 000-ppi silicon nitride mask for patterning ultra-high-resolution OLED displays 57th

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2-inch, 2, 000-ppi silicon nitride mask for patterning ultra...

57th SID International Symposium, Seminar and Exhibition, Display Week, 2020

作者： Jiang, Yibin Tam, Bryan Siu Ting Dong, Shou-Cheng Tang, Ching W. Institute for Advanced Study Hong Kong University of Science and Technology Kowloon Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

We developed a 2-inch, 2000 ppi shadow mask based on micron-thin, free-standing silicon nitride (SiNx) membrane for patterning OLED displays. Its layer composition and fabrication process were optimized to yield an ultra-flat, self-tensioned silicon nitride mask (SNM) that can achieve a submicron shadow distance limited only by the thickness of the membrane in line-of-sight vacuum depositions. © 2020 SID.

关键词： Silicon nitride

Multi-Scale Terahertz Image Reconstruction ⋆,⋆⋆

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SSRN

SSRN 2024年

作者： Su, Zhipeng Zhang, Yixiong Zhou, Jianyang Shi, Jianghong Qi, Feng Zhang, Xiao-Ping Ji, Chunlin School of Informatics Xiamen University National Demonstrative Software School Xiamen University China School of Electronic Science and Engineering National Model Microelectronics College Xiamen University China Shenyang Institute of Automation Chinese Academy of Sciences China Department of Electrical Computer & Biomedical Engineering Finance Department The Ted Rogers School of Management Toronto Metropolitan University Canada Kuang-Chi Institute of Advanced Technology State Key Laboratory of Meta-RF Electromagnetic Modulation Technology Shen Zhen China

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function