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Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion

Chinese Journal of Electronics 2023年第3期22卷 521-524页

作者： WANG Nian WANG Junsheng GE Fang Education Ministry Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improved maximum margin criterion (LE/IMMC) is proposed with applications in gene expression data classification. The LE/IMMC intends to constrain similar data points as close to each other as possible and maximize the margin regions between different pattern classes simultaneously. The proposed LE/IMMC by introducing IMMC into the cost function of LE retains the characteristic of local neighborhood relationship of LE. Meanwhile, it emphasizes the discriminative information by incorporating IMMC, which can maximize the between-class scatter and minimize the within-class scatter. Gene expression data classification experiments on four public datasets demonstrate our method is effective for feature extraction,

关键词： Laplace equations Education Medical services Feature extraction Cost function Manifold learning Gene expression

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Generalized Self-Adaption Network for Domain Adaptation 2

Generalized Self-Adaption Network for Domain Adaptation

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2nd International Conference on Artificial Intelligence and intelligent Information processing, AIIIP 2023

作者： Bian, Yongheng Ke, Xiao College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

ISBN: (纸本)9798350371451

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods. © 2023 IEEE.

关键词： Computer vision

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A Self-adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks 7th

A Self-adaptive Two-Stage Local Expansion Algorithm for Com...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Shan, Hui Li, Bin Yang, Haipeng Zhang, Lei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811989902

Community detection is of great importance to find hidden information in complex networks. For this problem, local expansion algorithms are becoming popular due to the low time complexity. However, most of them depend heavily on seed selection or require setting some thresholds in advance, leading to inaccurate partition. To this end, this paper proposes a self-adaptive two-stage local expansion algorithm (SALEA) for community detection. Specifically, we propose a self-adaptive strategy that can be used in SALEA for finding communities spontaneously. In the first stage, we apply the self-adaptive strategy for nodes to conduct local expansion and get coarse community structures. In the second stage, we apply the self-adaptive strategy for weak communities obtained in the first stage to refine the coarse community structures and get more accurate partitions. Finally, the experimental results on real and synthetic networks demonstrate that SALEA is superior over several state-of-the-arts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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Computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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A Novel Frequency Reconfigurable Mode Reconfigurable Ultra-Wideband Antenna

A Novel Frequency Reconfigurable Mode Reconfigurable Ultra-W...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Guo, Chen. Wu, Xianliang. Huang, Zhixiang. Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

Aiming at the inability to reconstruct the frequency and the pattern of the traditional monopole antenna, an ultra-wideband monopole antenna with frequency reconstruction and pattern reconstruction is proposed. The antenna consists of a monopole antenna and a metasurface with a distance H1 between them. Using HFSS simulation software to simulate, the size of the monopole antenna is 30mm×30mm×1.6mm, and the center frequency is 4.3 GHz. The simulation results show that the impedance bandwidth of the antenna when S11 is less than -10 dB is 3.21-13.64 GHz. In this design, the antenna widens the bandwidth while realizing the frequency reconstruction and pattern reconstruction, and basically does not affect other performances. The simulation results are basically consistent with the measured results, indicating that the antenna has good gain and radiation characteristics in the working frequency band, and can be widely used in ultra-wideband communication systems. © 2022 IEEE.

关键词： Monopole antennas

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A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1048-1063页

作者： Ye Tian Yuandong Feng Xingyi Zhang Changyin Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China School of Automation Southeast UniversityNanjing 210096China

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

关键词： Evolutionary computation fast clustering sparse multi-objective optimization super-large-scale optimization

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Design of Broadband Asymmetric Multisection Directional Coupler

Design of Broadband Asymmetric Multisection Directional Coup...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Geng, Xiaolei Wu, Xianliang Huang, Zhixiang Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

The multi-section coupled transmission line is equivalent to the cascade of multi-section ladder impedance filters, which provides a basis for the research of broadband directional couplers and meets the needs of microwave systems. Using this method, a multi-section -15 dB stripline directional coupler was designed in the application frequency range of 0.7-3.7 GHz, and the directional coupler was improved by loading the branch compensation capacitor. And through the electromagnetic simulation software HFSS for simulation optimization. The simulation results show that the coupling degree of the coupler is about -15 dB in 0.7-3.7 GHz, the return loss is greater than 20 dB and the isolation is greater than 30 dB, the insertion loss is less than 0.33 dB, the in-band fluctuation is less than 1 dB, and the physical size is 38mm × 6 mm, which meets the design requirements of the -15 dB coupler. © 2022 IEEE.

关键词： Electromagnetic simulation

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Spring pair method of finding saddle points using the minimum energy path as a compass

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Physical Review E 2024年第6期110卷 064123-064123页

作者： Gang Cui Kai Jiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan 411105 China

Finding index-1 saddle points is crucial for understanding phase transitions. In this work, we propose a simple yet efficient approach, the spring pair method (SPM), to accurately locate saddle points. Without requiring the Hessian information, the SPM evolves a single pair of spring-coupled particles on an energy surface. By designing complementary drifting and climbing dynamics based on gradient decomposition, the spring pair converges onto the minimum energy path (MEP) and spontaneously aligns its orientation with the MEP tangent, providing a reliable ascent direction for efficient convergence to saddle points. The SPM fundamentally differs from traditional surface walking methods that rely on the eigenvectors of the Hessian, which may deviate from the MEP tangent and potentially lead to convergence failure or undesired saddle points. The efficiency of the SPM for finding saddle points is verified by ample examples, including Lennard-Jones clusters, Morse clusters, water clusters, and the Landau energy functional involving quasicrystal phase transitions.

关键词： Lennard-Jones potential

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EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation 30

EmoTrans: Emotional Transition-based Model for Emotion Recog...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Jian, Zhongquan Wang, Ante Su, Jinsong Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9782493814104

In an emotional conversation, emotions are causally transmitted among communication participants, constituting a fundamental conversational feature that can facilitate the comprehension of intricate changes in emotional states during the conversation and contribute to neutralizing emotional semantic bias in utterance caused by the absence of modality information. Therefore, emotional transition (ET) plays a crucial role in the task of Emotion Recognition in Conversation (ERC) that has not received sufficient attention in current research. In light of this, an Emotional Transition-based Emotion Recognizer (EmoTrans) is proposed in this paper. Specifically, we concatenate the most recent utterances with their corresponding speakers to construct the model input, known as samples, each with several placeholders to implicitly express the emotions of contextual utterances. Based on these placeholders, two components are developed to make the model sensitive to emotions and effectively capture the ET features in the sample. Furthermore, an ET-based Contrastive Learning (ETCL) is developed to compact the representation space, making the model achieve more robust sample representations. We conducted exhaustive experiments on four widely used datasets and obtained competitive experimental results, especially, new state-of-the-art results obtained on MELD and IEMOCAP, demonstrating the superiority of EmoTrans. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Emotion Recognition

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