检索结果-内蒙古大学图书馆

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ma, Yuanling Guan, Jinting Zhejiang University Biosensor National Special Laboratory Key Laboratory of Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Hangzhou310027 China Xiamen University Department of Automation Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China

ISBN: (纸本)9781665468190

Using multi-omics data to achieve the classification of cancer subtypes from the perspective of machine learning can facilitate the understanding of cancer mechanism from the biomolecular aspect, which is of guidance to achieve personalized precision medicine in clinical medicine. To address the problem of cancer subtype classification based on multi-omics data, we propose a framework named MOCSC. Considering the high-dimensional complexity of multi-omics data and the heterogeneity among them, we use the idea of late integration model framework. Specifically, after dividing the training and test sets, for each omics data, the training set is first learned using a stacked sparse denoising autoencoder to extract its feature, and then the extracted feature is supplied to a single-layer neural network to obtain an initial prediction. Then, the initial results of all omics-specific classifiers are integrated and put into a view correlation discovery network for training to obtain the final prediction. Finally, the entire trained model is applied to the test set. The designed model is used to explore the best combination of multi-omics data and then tested on several different datasets. We compare our framework with the existing classification tools, showing its effectiveness in cancer subtype classification problem and also other classification problems based on multi-omics data. © 2022 IEEE.

关键词： data integration

来源：评论

学校读者我要写书评

暂无评论

Robust Visual Odometry Using Semantic Information in Complex Dynamic Scenes 5th

Robust Visual Odometry Using Semantic Information in Complex...

引用

5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hao Wang, Le Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230009 China Department of Computer Science and Technology Hefei University of Technology Hefei Anhui230009 China

ISBN: (纸本)9789811623356

Traditional vision-based simultaneous localization and mapping (SLAM) technology cannot obtain the semantic information of the surrounding environment, which will cause the robot to fail to complete intelligent grasping, human-computer interaction, and other advanced decision tasks. There are many challenges to solve this problem, such as obtaining less semantic information of the environment with low precision and being unable to deal with dynamic objects in a real environment quickly and effectively. In this paper, a robust visual odometry using semantic information in complex dynamic scenes was designed. Specifically, we present a refined instance segmentation method based on the contextual information of the frame to improve the accuracy of segmentation. On this basis, a feature detection and elimination algorithm for dynamic objects based on instance-level semantic information is proposed to improve the localization accuracy of camera pose. We extensively evaluate our system on the public TUM data set and compare it with ORB-SLAM2 and other methods. Experiments show that our methods greatly improve the localization accuracy of the camera pose and the robustness of the system, which verifies that our system is effective in complex dynamic scenes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-ZhongLab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

DBMGNet: A Dual-Branch Mamba-GCN Network for Hyperspectral Image Classification

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Hao Zhuang, Peixian Zhang, Xiaochen Li, Jiangyun University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education the School of Automation and Electrical Engineering Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing China

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) excel at local feature modeling but are limited to Euclidean space. Transformers offer long-range dependency modeling but suffer from high computational complexity. In contrast, graph convolutional networks (GCNs) can process information in non-Euclidean space, compensating for the limitations of CNNs. Meanwhile, the state space model Mamba, thanks to its linear complexity and strong long-range dependency modeling, shows great potential to offer an alternative to Transformers for HSI classification. To address the limitations of CNNs and Transformers while exploiting the potential of Mamba, we propose a dual-branch hybrid architecture named DBMGNet that combines Mamba with GCN for the HSI classification. In the Mamba branch, we design Band Selection Enhanced Bidirectional Mamba (BSEBM), which leverages Mamba’s long-range dependency modeling and sequential modeling capabilities to process spatial-spectral information. In the GCN branch, we apply reparameterized Chebyshev graph convolution to model similarity dependencies in non-Euclidean space, along with designing an adjacency matrix based on the intrinsic characteristics of HSIs. Extensive experiments demonstrate that our DBMGNet achieves the state-of-the-art performance of HSI classification against thirteen mainstream approaches. © 1980-2012 IEEE.

关键词： NP-hard

来源：评论

学校读者我要写书评

暂无评论

Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

引用

Computer engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

来源：评论

学校读者我要写书评

暂无评论

Variable projection algorithms with sparse constraint for separable nonlinear models

引用

Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

来源：评论

学校读者我要写书评

暂无评论

Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing

SSRN

引用

SSRN 2024年

作者： Li, Qi Guo, Dan Qian, Wei Zhou, Yuanen Zhao, Haifeng Sun, Xiao Wang, Meng School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

The remote photoplethysmography (rPPG) technique enables the estimation of vital signs such as heart rate by analyzing pulse-induced subtle skin color variation from facial videos. Robustly deriving cardiac pulse information with head motion (\eg, rotation and swings) interference, remains a critical challenge, as it inevitably introduces complex noise disturbances such as facial geometric deformation and head motions. Most existing approaches primarily focus on how to extract the weak pulse signals, overlooking the detrimental effects induced by such noises. In response to this, we propose a Temporal Gated Face Alignment Network (TGFAN) to adaptively counteract motion noise in long-term video sequences. As key modules in TGFAN, the bidirectional temporal face alignment block first captures inter-frame motion discrepancies and achieves temporal alignment, and then extracts motion-robust pulse features by spatiotemporal convolution. Furthermore, we also propose a temporal gating mechanism, which can dynamically guide motion-disturbed segments for face alignment to alleviate local temporal interference. Experimental evaluations on four public datasets demonstrate the superior performance of our proposed model under both intra-dataset and cross-dataset tests. Our codes will be available at \url{https://***/Eadith/TGFAN}. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Demand Response Scheme: Achieving Sustainability in Distributed data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

引用

9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

来源：评论

学校读者我要写书评

暂无评论

A Novel Privacy-Enhancing Framework for Low-Dose CT Denoising

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Yang, Ziyuan Huangfu, Huijie Ran, Maosong Wang, Zhiwen Yu, Hui Sun, Mengyu Zhang, Yi Sichuan University College of Computer Science Chengdu610065 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China

Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with enormous self-collected data and providing application programming interfaces (APIs) for users, such as Chat-GPT. To avoid model leakage, users are required to upload their data to the server. This approach is particularly advantageous for devices with limited computational capabilities, as it offloads computation to the server, easing the workload on the devices themselves. However, this way raises public concerns about the privacy disclosure risk. Hence, to alleviate related concerns, we propose to directly denoise LDCT in the encrypted domain to achieve privacy-preserving cloud services without exposing private data to the server. Concretely, we employ homomorphic encryption to encrypt private LDCT, which is then transferred to the server model trained with plaintext LDCT for further denoising. Since fundamental DL operations, such as convolution and linear transformation, cannot be directly used in the encrypted domain, we transform the fundamental mathematic operations in the plaintext domain into the operations in the encrypted domain. Moreover, we present two interactive frameworks for linear and nonlinear models, both of which can achieve lossless operating. In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage. Moreover, we provide theoretical proof to validate the lossless property of our framework. Finally, experiments were conducted to demonstrate that the transferred contents are well protected and cannot be reconstructed.1 © 2020 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Accelerated Design for Distributed Asynchronous Optimization

Accelerated Design for Distributed Asynchronous Optimization

引用

第43届中国控制会议

作者： Zhoubin Zhang Xin Yu Yinghui Wang Songsong Cheng Yuan Fan School of Electrical Engineering and Automation Anhui University Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper proposes an accelerated algorithm for addressing distributed optimization problems in directed *** develop an accelerated asynchronous distributed Nesterov gradient descent algorithm for directed networks called ASYADND. For the considered problems, unpredictable, time-varying, and bounded. Under strong convexity and smoothness conditions, we analyze the proposed algorithm by transforming the asynchronous scheme into a synchronous counterpart. We adopt an acceleration mechanism and establish its linear convergence with the aid of the small gain theorem.

关键词： Distributed optimization asynchrony directed graph linear convergence Nesterov

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：