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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Chao Liu, Baolin Wei, Jianguo College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9780999241165

Using a convolutional neural network to build visual encoding and decoding models of the human brain is a good starting point for the study on relationship between deep learning and human visual cognitive mechanism. However, related studies have not fully considered their differences. In this paper, we assume that only a portion of neural network features is directly related to human brain signals, which we call shared features. In the encoding process, we extract shared features from the lower and higher layers of the neural network, and then build a non-negative sparse map to predict brain activities. In the decoding process, we use back-propagation to reconstruct visual stimuli, and use dictionary learning and a deep image prior to improve the robustness and accuracy of the algorithm. Experiments on a public fMRI dataset confirm the rationality of the encoding models, and comparing with a recently proposed method, our reconstruction results obtain significantly higher accuracy. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Signal encoding

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Research on Multi-dimensional Bilingual Teaching Model of computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

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17th International Conference on computer science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Ultra-Sensitive Optical Fiber Spr Biosensor for Precise Prostate Cancer Diagnosis by Label-Free Detection of Mirna-21

SSRN

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SSRN 2025年

作者： Wang, Jiale Deng, Minghua Niu, Kunpeng Wang, Chuanquan Wang, Ning Li, Jinhua Xia, Binyun Ding, Liyun Yang, Huan Chen, Xiaohan Zhu, Jiayi National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China School of Computer and Information Engineering Hubei Normal University Huangshi China Hubei Optical Fundamental Research Center Wuhan China Collaborative Innovation Center for Advanced Organic Chemical Materials Co-constructed by the Province and Ministry Key Laboratory for the Green Preparation and Application of Functional Materials Ministry of Education Hubei Key Laboratory of Polymer Materials School of Materials Science and Engineering Hubei University Wuhan430062 China School of Physical Science and Technology Lanzhou University Lanzhou730000 China Department of Urology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430030 China

Efficient and rapid identification of miRNA-21 may be conducive to the early diagnosis of prostate cancer. In this work, a label-free and highly sensitive optical fiber Surface Plasmon Resonance (SPR) biosensor was developed for detecting the miRNA-21, which was fabricated simply by coating a layer of Au film and AuNPs onto a multi-mode-single-mode-multi-mode (MSM) optical fiber, and then biofunctionalized with ssDNA enabling the quantification of miRNA-21. The working principle involves initiating the change of the effective refractive index (RI) around the optical fiber's surface, which was caused by nucleic acid hybridization between the ssDNA and the miRNA-21. As a result, the interaction would result in a noticeable shift of the SPR wavelengths in the transmission spectra. In the experiment, a substantial enhancement was exhibited in the RI sensitivity of the optical fiber sensor due to the surface plasmon resonance (SPR) coupling effect of AuNPs and the Au film, measuring approximately 3021.48 nm/RIU. Excitingly, the biosensors, which have a simple composition, were experimentally verified to have an ultra-low limit of detection (LOD) of 10-20 M, significantly lower than current detection methods. And trace miRNA-21 was enabled for detection without RNA amplification. The SPR sensors were also experimentally proved with excellent linearity, specificity and stability. When employed to detect the serum samples, the results indicated that the proposed sensors could brilliantly distinguish clinical patients with and without prostate cancer. Compared to the commonly used PSA quantification in clinical practice, the method developed in this work has higher detection accuracy. The proposed sensing platform processes the merits of simple configuration as well as low cost and may be applied for the precise diagnosis of prostate cancer. © 2025, The Authors. All rights reserved.

关键词： Surface plasmon resonance

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Geographical flows: a fresh perspective on quantifying urban function

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Urban Informatics 2024年第1期3卷 1-9页

作者： Pei, Tao Yan, Xiaorui Shu, Hua Wang, Xi Chen, Xiao Guo, Sihui Jiang, Jingyu Song, Ci Chen, Jie State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China School of Computer Science and Information Engineering Hubei University Wuhan China Beijing Institute of Surveying and Mapping Beijing China Nanjing Institute of Surveying Mapping & Geotechnical Investigation Co. Ltd Nanjing China

Urban function refers to various roles and activities that take place in an urban region or a public facility. Previous research only focused on the types of urban function and its quantitative characterization did not attract enough attention. In this paper, we highlight the issue of quantifying the urban function and divide it into two aspects: one is the intensity of the urban function, which can be seen as the capacity to accommodate people engaging in activities related to the function, and the other is its influence on the surrounding area. Due to the cause-and-effect dynamics between urban functions and geographical flows, which refer to the human or objects moving between locations, geographical flows have been used to estimate the intensity and influence of urban function. Based on the review of recent updates, this paper provides a research framework for utilizing flow data to quantify urban function in terms of intensity and influence. Regarding the intensity, the indices are mainly based on the volume of inflow/outflow and their ratio. Regarding the influence, there are two types of indices for evaluating it: one for planar facilities and the other for linear facilities. To describe the intensity and influence of the urban function simultaneously, the volume along with the length of flows are considered into the indices, which can quantify the importance or irreplaceability of a place or facility. The analysis shows that the geographical flows provide a new perspective for quantifying urban function in terms of intensity and influence.

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Transcriptome Analysis of Female and Male Henosepilachna vigintioctopunctata(Coleoptera:Coccinellidae)Identifies Sex-related Genes

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Horticultural Plant Journal 2021年第2期7卷 142-148页

作者： Wei Guo Zhuoqi Liu Jiaqi Yu Jing Lü Xiaoguo Jiao Lan Kong Chunxiao Yang Huipeng Pan Key Laboratory of Bio-Pesticide Innovation and Application of Guangdong Province Engineering Technology Research Center of Agricultural Pest Biocontrol of Guangdong ProvinceSouth China Agricultural UniversityGuangzhou 510642China State Key Laboratory of Biocatalysis and Enzyme Engineering Center for Behavioral Ecology&EvolutionSchool of Life SciencesHubei UniversityWuhan 430062China Department of Computer Science Eastern Kentucky UniversityRichmond KY 40475USA State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources South China Agricultural UniversityGuangzhou 510642China

Henosepilachna vigintioctopunctata is an emerging horticultural pest of Solanaceae plants in ***,we employed transcriptome sequencing of three female and three male *** libraries to identify sex-related *** recorded 281599294 bp of clean reads,and de novo assembly generated 85206 unigenes,with N_(50) of 1311 bp and average length of 763 *** found 5002 genes highly expressed in males and 2179 genes highly expressed in *** profiles of six unigenes specific to females,six unigenes specific to males,and ten unigenes common to both sexes were verified using semi-quantitive RT-PCR;results showed expression levels of these 22 unigenes were relatively consistent with the transcriptome *** all unigenes as references,we identified 6657 putative simple sequence *** transcriptome sequences and gene expression profiles of female and male *** help explore genetic mechanisms of sexual dimorphism.

关键词： Henosepilachna vigintioctopunctata sex RNA-seq transcriptome annotation differential expression

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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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

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Performance Comparison of Various Photovoltaic Module Arrangements based on Game Puzzle Theory under PSCs: Experimental Validation

Performance Comparison of Various Photovoltaic Module Arrang...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Rupendra Kumar Pachauri Pankaj Kumar Gupta Manish Sharma Ankur Kumar Gupta Ahmad Faiz Minai Shashikant EEED School of Engineering University of Petroleum and Energy Studies Dehradun India Department of Information and Technology Greater Noida Institute of Technology Greater Noida India School of Computer Science & Application IIMT University Meerut India Department of Research and Development IIMT University Meerut India Electrical Engineering Department Integral University Lucknow India

In this paper, series-parallel (SP), total-cross-tied (TCT) and game puzzle dependent photovoltaic (PV) array configurations show the comprehensive results during partial shadowing conditions (PSCs). The productivity indices are investigated with respect to power and voltage at global maximum power point (GMPP), fill factor (FF), power enhancement (PE), etc. In this paper, a MATLAB/Simulink performs to develop PV system configurations and carries the extensive investigation under the shadowing conditions. Experimental study proves the obtained the simulation results and found that the Calcu-Do-Ku game puzzle based configuration has higher results in terms of GMPP as 49.29W and 60.14W compared to the conventional PV array configurations under considered both shadowing cases. The experimental study proves the results obtained through the MATLAB/results during the similar shading circumstances.

关键词： Productivity Renewable energy sources Fill factor (solar cell) Simulation Games Voltage Matlab

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An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN

An Improved Network Segmentation Performance in Lesion Segme...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： T. Thivya C. Kalaivanan N. Juliet Vengadapathiraj M G. Mary Valantina V. Mohanavel Department of Information Technology Velammal Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Master of Computer Application S.A Engineering College Chennai Department of Electronics and Communication Engineering Rajalakshmi Instittue of Technology Chennai Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Mechanical Engineering Chandigarh University Mohali Punjab India

ISBN: (纸本)9781665482721

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis.

关键词： Representation learning Deep learning Visualization Costs Medical services Feature extraction Lesions

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Learning from ambiguous labels for lung nodule malignancy prediction

arXiv

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arXiv 2021年

作者： Liao, Zehui Xie, Yutong Hu, Shishuai Xia, Yong Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China

Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come from the ambiguous labels provided by annotators, since deep learning models have in some cases been found to reproduce or amplify human biases. In this paper, we propose a multi-view 'divide-and-rule' (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction on chest CT scans. According to the consistency and reliability of their annotations, we divide nodules into three sets: A consistent and reliable set (CR-Set), an inconsistent set (IC-Set), and a low reliable set (LR-Set). The nodule in IC-Set is annotated by multiple radiologists inconsistently, and the nodule in LR-Set is annotated by only one radiologist. Although ambiguous, inconsistent labels tell which label(s) is consistently excluded by all annotators, and the unreliable labels of a cohort of nodules are largely correct from the statistical point of view. Hence, both IC-Set and LR-Set can be used to facilitate the training of MV-DAR. Our MV-DAR contains three DAR submodels to characterize a lung nodule from three orthographic views. Each DAR consists of a prediction network (Prd-Net), a counterfactual network (CFNet), and a low reliable network (LR-Net), learning on CRSet, IC-Set, and LR-Set, respectively. The image representation ability learned by CF-Net and LR-Net is then transferred to Prd-Net by negative-attention module (NA-Module) and consistent-attention module (CA-Module), aiming to boost the prediction ability of Prd-Net. The MV-DAR model has been evaluated on the LIDC-IDRI dataset and LUNGx dataset. Our results indicate not only the effectiveness of the proposed MV-DAR model in learning from ambiguous labels but also its superiority over present noisy labellearning models in lung nodule malignancy prediction. © 2021, CC BY-NC-ND.

关键词： computerized tomography

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