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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Yu Lu Rong Mengyao Wang Min Huang Yazhou Zhang Yijie Ding Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Human Resources Office Zhengzhou University of Light Industry Zhengzhou China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The outbreak of COVID-19 pandemic has spread rapidly and severely affected all aspects of human lives. Recent researches has shown artificial intelligence and deep learning based approaches have achieved successful results in detecting diseases. How to accurately and quickly detect COVID-19 has always been the core topic of research. In this paper, we propose a novel approach based on prompt learning for COVID-19 diagnosis. Different from the traditional “pre-training, fine-tuning” paradigm, we propose the prompt-based method that redefine the COVID-19 diagnosis as a masked predict task. Specifically, we adopt an attention mechanism to learn the multi-modal representation of medical image and text, and manually construct a cloze prompt template and a label word set. Selecting the label word corresponding to the maximum probability by pre-training language model. Finally, mapping the prediction results to the disease categories. Experimental results show that our proposed method obtains obvious improvement of 1.2% in terms of Mi-F1 score compared with the state-of-the-art methods.

关键词： COVID-19 Representation learning Learning systems Deep learning Pandemics Feature extraction Task analysis

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Improving Heavy Precipitation Forecast Using Data Assimilation with Ensemble-Based Forecast Sensitivity to Observations Technique: A Case Study

SSRN

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SSRN 2023年

作者： Huang, Lingdong Zhang, Yu Wang, Donghai Zeng, Zhilin School of Atmospheric Sciences Sun Yat-sen University Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Education Zhuhai China Zhuhai China College of Ocean and Meteorology Guangdong Ocean University Zhanjiang China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Macau University of Science and Technology 999078 China Guangdong Meteorological Observatory China Meteorological Administration Tornado Key Laboratory Guangdong Meteorological Service Guangdong China

To evaluate the role of observational data assimilated in model, the ensemble-based forecast sensitivity to observations (EFSO) technique was employed to explore the sensitive observations in numerical experiments on a heavy rainfall event over South China through three strategies of data assimilation (i.e., without data assimilation, CNTL;with all observation assimilation, CNTL_DA;with specific observation assimilation based on EFSO, SEN_DA). The results showed the Infrared Atmospheric Sounder Interferometer exhibiting the greatest impact, followed by Advanced Microwave Sounding Unit A and surface land reports. The top 10% of observation impact (i.e., positive contribution to the rainfall forecast) was found to be located in the upstream ocean of the low-level winds of the heavy rainfall. SEN_DA exhibited the better precipitation in magnitude and location than CNTL and CNTL_DA via assimilating the top 10% of observations over the upstream ocean. The improvement of prediction combined with EFSO technique was preliminarily explored in synoptic perspective. Compared to CNTL and CNTL_DA, SEN_DA exhibited stronger monsoonal southwesterly and horizontal water vapor transport over the South China Sea, the upstream ocean of the heavy rainfall. Although CNTL, CNTL_DA, and SEN_DA had similar magnitudes of convective instability, the SEN_DA had stronger vertical ascent, favoring heavy coastal rainfall through releasing convective instability. Moreover, SEN_DA exhibited a performance of near-surface horizontal mass convergence superposed horizontal mass divergence at mid-to-low levels. Such collectively vertical configuration of horizontal mass divergence potentially promoted the coastal ascent. These results help to optimize the future strategy of observations and data assimilation over South China. © 2023, The Authors. All rights reserved.

关键词： Rain

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Fusion of infrared and visible images based on improved fuzzy rules in multi-scale domain

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Guangxue Jingmi Gongcheng/Optics and Precision Engineering 2015年 23卷 623-630页

作者： Feng, Xin Hu, Kai-Qun Key Laboratory of Manufacturing Equipment Mechanism Design and Control of Chongqing Chongqing International Technology Cooperation Base Equipment System Service Health Care International Joint Research Center Chongqing Technology and Business University Chongqing400067 China

On the basis of shift-invariant Shearlet domain fuzzy processing, an image fusion method with improved fuzzy rules was proposed. First of all, infrared and visible light images were processed by Shift Invariant Shearlet Transformation (SIST) to decomposed into low-pass and high-pass subbands. For low frequency subband coefficients, the rules of local area information entropy was used. For high frequency subband coefficients, the Shearlet transform frame redundancy was considered. To solve the redundant information of the high frequency subbands, the infrared and visible light image membership degree, non-belongingness degree, the hesitation degree of high-frequency sub-band figure and the optimal entropy were calculated. The two coefficient images were decomposed, then the total count of blackness, whiteness of two corresponding blocks are computed. Finally, the block of the blended coefficient image was constructed and the infrared and visible light images were obtained by using the SIST. This method effectively eliminates the Gibbs phenomenon, and offers an edge keeping degree more than 0.85. © 2015, Chinese Academy of Sciences. All right reserved.

关键词： Fuzzy rules

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A 6-DOF Heavy-Load Parallel Manipulator with RFTA and its Application

A 6-DOF Heavy-Load Parallel Manipulator with RFTA and its Ap...

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Jianzheng Zhang Feng Gao Cunxiang Ma Xianchao Zhao Hongnian Yu Dan Zhang State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 20024 Faculty of Computing Engineering and Technology Staffordshire UniversityStaffordST18 0DGUK Faculty of Engineering and Applied Science UOIT2000 Simcoe Street North OshawaON L1H 7K4Canada

This paper proposes a 6-DOF (Degree of Freedom) heavy-load parallel manipulator with a redundant actuation and fault-tolerant actuator (RFTA). The novel RFTA model of the proposed manipulator is developed and its working principle is described. In order to achieve the given motion, the mathematic models of the proposed manipulator with the RFTA are derived. As a prototype of an earthquake simulator, two experiments are performed. The experimental results demonstrate that the RFTA is able to supply the required double driving force and appropriate used as an actuator of a low frequency earthquake simulator. The results of the fault-tolerant experiment show the earthquake simulator with the RFTA is capable of tolerating some local faults. The proposed parallel manipulator can also be applied under other heavy-load environments.

关键词： Force Fasteners Kinematics Mathematical model Equations Manipulator dynamics

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Spike Sorting Based on Window-Gradient Feature

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Journal of Physics: Conference Series 2019年第2期1176卷

作者： Min Dai Feng Jia Wen Yang Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology 300384 Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

Spike sorting is difficult when there is high waveforms similarity between different spike or when there is a large number of superimposed spikes in the sample. A new sample optimization method is proposed in the paper, called window-gradient feature. Every spike waveform is segmented into successive fragments in terms of the width σ, and each segment is called a window. Then calculate the gradient change of each window and use the ratio as the new alternative feature of the window. Finally, all window-gradient features of the spike waveforms are used to replace the original waveform features for spike sporting. The method is verified on the simulation data at different SNR. The experimental results show that when using SVM for spike sorting, the optimization effect of the proposed method is better than PCA, especially for data sets data sets with high noise or large sampling waveform similarity.

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Trackerless Freehand Ultrasound with Sequence Modelling and Auxiliary Transformation Over Past and Future Frames

Trackerless Freehand Ultrasound with Sequence Modelling and ...

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IEEE International Symposium on Biomedical Imaging

作者： Qi Li Ziyi Shen Qian Li Dean C Barratt Thomas Dowrick Matthew J Clarkson Tom Vercauteren Yipeng Hu Department of Medical Physics and Biomedical Engineering Centre for Medical Image Computing Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London U.K. State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Biomedical Engineering & Imaging Sciences King’s College London London U.K.

Three-dimensional (3D) freehand ultrasound (US) reconstruction without a tracker can be advantageous over its two-dimensional or tracked counterparts in many clinical applications. In this paper, we propose to estimate 3D spatial transformation between US frames from both past and future 2D images, using feed-forward and recurrent neural networks (RNNs). With the temporally available frames, a further multi-task learning algorithm is proposed to utilise a large number of auxiliary transformation-predicting tasks between them. Using more than 40,000 US frames acquired from 228 scans on 38 forearms of 19 volunteers in a volunteer study, the hold-out test performance is quantified by frame prediction accuracy, volume reconstruction overlap, accumulated tracking error and final drift, based on ground-truth from an optical tracker. The results show the importance of modelling the temporal-spatially correlated input frames as well as output transformations, with further improvement owing to additional past and/or future frames. The best performing model was associated with predicting transformation between moderately-spaced frames, with an interval of less than ten frames at 20 frames per second (fps). Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs. Interestingly, with the proposed approach, explicit within-sequence loss that encourages consistency in composing transformations or minimises accumulated error may no longer be required. The implementation code and volunteer data 1 will be made publicly available ensuring reproducibility and further research.

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AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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Full-scale field-free spin-orbit switching of the CoPt layer grown on vicinal substrates

arXiv

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arXiv 2023年

作者： Yongming, Luo Mengfan, Liang Zhongshu, Feng Haoran, Chen Nan, Jiang Jianhui, Chen Mingyue, Yuan Jingcang, Zhang Yifeng, Cheng Lu, Sun Ru, Bai Xiaohe, Miao Ningning, Wang Yizheng, Wu Renchao, Che Center for Integrated Spintronic Devices Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Research Center for Intelligent Computing Platforms Zhejiang laboratory Hangzhou311121 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Instrumentation and Service Center for Physical Sciences Westlake University Hangzhou310024 China

A simple, reliable and field-free spin orbit torque (SOT)-induced magnetization switching is a key ingredient for the development of the electrical controllable spintronic devices. Recently, the SOT induced deterministic switching of the CoPt single layer has attracts a lot of interests, as it could simplifies the structure and add new flexibility in the design of SOT devices, compared with the Ferromagnet/Heavy metal bilayer counterparts. Unfortunately, under the field-free switching strategies used nowadays, the switching of the CoPt layer is often partial, which sets a major obstacle for the practical applications. In this study, by growing the CoPt on vicinal substrates, we could achieve the full-scale (100% switching ratio) field-free switching of the CoPt layer. We demonstrate that when grown on vicinal substrates, the magnetic easy axis of the CoPt could be tilted from the normal direction of the film plane;the strength of Dzyaloshinskii–Moriya interaction (DMI) would be also be tuned as well. Micromagnetic simulation further reveal that the field-free switching stems from tilted magnetic anisotropy induced by the vicinal substrate, while the enhancement of DMI help reducing the critical switching current. In addition, we also found that the vicinal substrates could also enhance the SOT efficiency. With such simple structure, full-scale switching, tunable DMI and SOT efficiency, our results provide a new knob for the design SOT-MRAM and future spintronic devices. © 2023, CC BY.

关键词： Binary alloys

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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