检索结果-内蒙古大学图书馆

Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

来源：评论

学校读者我要写书评

暂无评论

Ontology Revision based on Pre-trained Language Models

arXiv

引用

arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts

arXiv

引用

arXiv 2024年

作者： Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Bernard and Irene Schwartz Center for Biomedical Imaging Dept. of Radiology NYU School of Medicine NY United States Dept. of Radiology NYU School of Medicine NY United States CUBRIC School of Engineering Cardiff University Cardiff United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Computation Information and Technology Technical University of Munich Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Neurobiology Research Unit Copenhagen University Hospital Copenhagen Denmark

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics’ robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. © 2024, CC BY.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Correction to: Deep mantle melting, global water circulation and its implications for the stability of the ocean mass

引用

Progress in Earth and Planetary science 2021年第1期8卷 1-3页

作者： Karato, Shun-ichiro Karki, Bijaya Park, Jeffrey Department of Earth and Planetary Sciences Yale University New Haven USA School of Electrical Engineering and Computer Science Department of Geology and Geophysics Center for Computation and Technology Louisiana State University Baton Rouge USA

An amendment to this paper has been published and can be accessed via the original article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Topological Edge States at Singular Points in Non-Hermitian Plasmonic Waveguide-Cavity Systems

Topological Edge States at Singular Points in Non-Hermitian ...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Yin Huang Yuecheng Shen Georgios Veronis Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Changsha China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Center for Computation and Technology Louisiana State University Baton Rouge LA USA

We show that non-Hermitian plasmonic waveguide-cavity systems can simultaneously exhibit both topological edge states and singular points. We also show that the sensitivity of the reflected light intensity is greatly ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Sensitivity Waveguide lasers Lasers and electrooptics Plasmons Electro-optical waveguides

来源：评论

学校读者我要写书评

暂无评论

Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

引用

Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

来源：评论

学校读者我要写书评

暂无评论

A novel control scheme for automatic voltage regulator using novel modified artificial rabbits optimizer

引用

e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 6卷

作者： Izci, Davut Rizk-Allah, Rizk M. Snášel, Václav Ekinci, Serdar Hashim, Fatma A. Abualigah, Laith Department of Computer Engineering Batman University Batman 72100 Turkey MEU Research Unit Middle East University Amman Jordan Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom 32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Faculty of Engineering Helwan University Egypt Prince Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD²) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD² controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD² controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness. © 2023 The Author(s)

关键词： Adaptive local search Artificial rabbits optimization algorithm Automatic voltage regulator Experience-based perturbed learning Optimization

来源：评论

学校读者我要写书评

暂无评论

Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Qingquan Liu, Jialin Zhang, Zeqi Wen, Junyi Mao, Bifei Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Trustworthiness Theory Research Center Huawei Technologies Co. Ltd. Shenzhen518129 China The School of Computer Science University of Birmingham United Kingdom

In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the training of fair models. It has been theoretically and empirically shown that there exist conflicts and inconsistencies among accuracy and multiple fairness measures. Optimising one or several fairness measures may sacrifice or deteriorate other measures. Two key questions should be considered, how to simultaneously optimise accuracy and multiple fairness measures, and how to optimise all the considered fairness measures more effectively. In this paper, we view the mitigating unfairness problem as a multi-objective learning problem considering the conflicts among fairness measures. A multi-objective evolutionary learning framework is used to simultaneously optimise several metrics (including accuracy and multiple fairness measures) of machine learning models. Then, ensembles are constructed based on the learning models in order to automatically balance different metrics. Empirical results on eight well-known datasets demonstrate that compared with the state-of-the-art approaches for mitigating unfairness, our proposed algorithm can provide decision-makers with better tradeoffs among accuracy and multiple fairness metrics. Furthermore, the high-quality models generated by the framework can be used to construct an ensemble to automatically achieve a better tradeoff among all the considered fairness metrics than other ensemble methods. Our code is publicly available at https://***/qingquan63/FairEMOL. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

arXiv

引用

arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

来源：评论

学校读者我要写书评

暂无评论

Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

Elucidating Spatial Complex Structures from Mass Spectrometr...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：