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Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

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4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Multi-Sequence MRI Thoracic Spine Compression Fracture Classification Based on Local-Global Feature Selection and Cross-Attention Fusion

Multi-Sequence MRI Thoracic Spine Compression Fracture Class...

引用

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Shuhua Jin Jinjin Hai Shijie Wei Jian Chen Kai Qiao Weicong Zhang Hai Lv Bin Yan Henan Key Laboratory of Imaging and Intelligent Processing School of Cyber Science and Engineering Zhengzhou University Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou University Zhengzhou Henan China Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application Jiangmen Guangdong China Department of Spinal Surgery The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai Guangdong China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Thoracic vertebral compression fracture (VCF) is a common fracture type in clinical practice, which seriously affects the quality of life of patients. The benign and malignant classifications of VCF are of significant diagnostic importance for the selection of clinical treatment options. Magnetic resonance imaging (MRI), as the main diagnostic tool for VCF, has the characteristics of multi-sequence imaging, and the structural and tissue information provided by different sequences complement each other. This paper proposes a multi-sequence MRI feature fusion network CAMSF based on vision transformer (ViT), which solves the problems of limited local feature extraction of ViT and insufficient interaction between multi-sequence image features. This method combines a multi-head self-attention mechanism and a deep convolution module to effectively capture global and local features, and uses a graph pooling-based feature selection method to optimize redundant features. In addition, a fusion method based on the cross-attention mechanism is used to deeply interact with different MRI sequence features to obtain complementary information across sequence images. Experimental results on the thoracic VCF dataset show that the classification accuracy of CAMSF reaches 84.88% and the AUC reaches 83.39%, which is significantly better than the existing ViT-based multimodal image feature fusion method. The ablation experiment further verifies the effectiveness of each module, indicating that CAMSF can effectively fuse multi-sequence MRI features and provide an effective solution for the accurate classification of VCF.

关键词： Computer vision Accuracy Image coding Control engineering Convolution Magnetic resonance imaging Sensitivity and specificity Feature extraction Transformers Cancer

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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An English Pronunciation Quality Evaluation Model Based on Multi-dimensional Features 2

An English Pronunciation Quality Evaluation Model Based on M...

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2021 2nd International Symposium on Automation, information and Computing, ISAIC 2021

作者： Li, Yiqun Huang, Guimin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners' English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746. © Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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Adaptive Environment Geographic Optimal Transport Based on Remote Sensing Feature Analysis

Adaptive Environment Geographic Optimal Transport Based on R...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qiwang Yuan Zhenjie Liu Jun Li Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The fusion of remote sensing data and social media data effectively enhances the spatiotemporal resolution of available datasets, providing assistance in addressing specific issues. For the integration of heterogeneous data, domain adaptation and Geographic Optimal Transport (GOT) methods have offered significant support. However, within the process of data fusion, the climatic environment of the data collection areas is often less considered, resulting in the omission of certain climate characteristics under remote sensing imagery, which typically significantly impact algorithm precision. In this paper, building upon the GOT, we further explore the impact of variations in different remote sensing parameters on data fusion accuracy under specific climatic conditions, leading to modifications in the GOT formula. In experiments conducted for flood scenarios, we have enhanced the accuracy of heterogeneous data fusion in this context by emphasizing the MNDWI term and removing the NDVI term from the GOT equation.

关键词： Accuracy Social networking (online) Disasters Data integration Data collection Spatiotemporal phenomena Sensors

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Focal plane imaging system based on CMOS terahertz detector

Focal plane imaging system based on CMOS terahertz detector

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2024 International Conference on intelligent Manufacturing and Mechatronics, IMM 2024

作者： Lin, Yue Zhang, Hui Qi, Feng Liu, Zhaoyang Wang, Guanwen Wang, Ningbo School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China The Key Laboratory of OptoElectronic Information Processing Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China

Terahertz technology has shown remarkable potential for application in the field of imaging since it offers several particular advantages such as great penetration capabilities, non-ionization and high instantaneous bandwidth. This paper presents a focal plane imging system based on CMOS terathertz detector for forward-looking detection. The focal plane imaging system has several advantages, including fast scanning, simple composition, low cost, and easy integration of a CMOS detector. It is composed of a terahertz radiation source, a Polytetrafluoroethylene (PTFE) lens and a broadband CMOS detector. The detector was fabricated in a 65 nm CMOS process. The operating frequency range of the detector covers 75 GHz to 1000 GHz. Measurement results have shown that the focal plane imaging system is capable of stably imaging at frequencies of 100 GHz and 220 GHz. To address the issue of missing information in measurement images, the morphological closing algorithm is employed to process the original image, successfully filling in the missing information and improving image quality. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Focusing

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Age estimation of Caenorhabditis elegans with label distribution learning

Age estimation of Caenorhabditis elegans with label distribu...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zi-Kang Liu, Jun Wang, Jun-Ji Chen, Gong Li, Chen-Qian Zhao, Zihao Jiang, Min Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Caenorhabditis elegans (C. elegans) is widely used in life research as a model organism. Accurate measurement of its age has practical implications for follow-up research. However, due to the similar characteristics of nematodes in different age groups, the general method is difficult to estimate accurately. Aiming at this problem, this paper proposes a method of label distribution learning, and uses fluorescence microscopy images to estimate the age of C. elegans. First, the logical age label are mapped to the label distribution. Secondly, the distribution learning loss function is used to make the neural network learn the relationship between the label distribution and corresponding image. Finally, the distribution learning loss function and the classification loss function are combined to strengthen the learning of the neural network. Experiments show that the accuracy and mean absolute error (MAE) of the method in this paper are improved compared with the baseline. In the experiment of estimating the age on different parts' data set, it was also concluded that the fluorescent image of the torso of C. elegans can better reflect the age characteristics than the head and tail. © 2022 IEEE.

关键词： Deep learning

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 11

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Sun, Pingping Qiang, Baohua Liu, Zhiguang Yang, Xianyi Xi, Guangyong Liu, Weigang Chen, Ruidong Zhang, Shihao Guilin University of Electronic Technology China The 54th Research Institute of Cetc Hebei Key Laboratory of Intelligent Information Perception and Processing China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods. © 2022 ACM.

关键词： Alignment

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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