检索结果-内蒙古大学图书馆

Enhancing Classification of Alzheimer’s Disease using Spatial Attention Mechanism

Open Neuroimaging Journal 2024年 17卷

作者： Krishnan, Deepa Bishnoi, Akshat Bansal, Shaivi Ravi, Vinayakumar Ravi, Pradeep Department of Computer Engineering Mukesh Patel School of Technology Management and Engineering SVKM’s Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-University Center for Artificial Intelligence Prince Mohammad Bin Fahd University Computer Science and Engineering GSSS Institute of Engineering and Technology for Women

Aim: This study aims to enhance the precision of Alzheimer's disease (AD) detection by integrating Spatial Attention Mechanism into a Convolutional Neural Network (CNN) architecture. Background: Alzheimer's disease is a progressive neurodegenerative disorder characterized by abnormal protein deposits in the brain, leading to nerve cell loss and posing a significant global health challenge. Early and accurate detection is crucial for disease management and treatment due to the lack of a cure and the disease's severe progression. Objective: The objective of this research is to improve the accuracy of Alzheimer's disease classification using MRI data by implementing a Spatial Attention Mechanism in a CNN architecture. Methods: The study utilized T1-weighted MRI data from the OASIS 1 and OASIS 2 datasets. The key innovation is the Spatial Attention layer incorporated within a CNN model, which computes the average of each channel in the input feature map. This layer guides subsequent layers to focus on critical brain regions, enhancing the model's accuracy in differentiating between Alzheimer's disease stages. Results: The model achieved a validation accuracy of 99.69% with a sensitivity and specificity of 1.0000, demonstrating its reliability in distinguishing between different stages of Alzheimer's disease. The adaptability of the Spatial Attention layer allows the model to assign higher weights to crucial brain regions, improving its discriminative power. Conclusion: The integration of the Spatial Attention Mechanism into the CNN architecture significantly contributes to the early detection of Alzheimer's disease, enabling timely interventions. This innovative approach has the potential to revolutionize Alzheimer's diagnosis by enhancing accuracy and offering a robust solution for classification. © 2024 The Author(s).

关键词： Alzheimer's Attention CNN Deep learning Neurodegenerative disease

来源：评论

学校读者我要写书评

暂无评论

Policy Representation via Diffusion Probability Model for Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： Yang, Long Huang, Zhixiong Lei, Fenghao Zhong, Yucun Yang, Yiming Fang, Cong Wen, Shiting Zhou, Binbin Lin, Zhouchen School of Artificial Intelligence Peking University Beijing China College of Computer Science and Technology Zhejiang University China MOE Frontiers Science Center for Brain and Brain-Machine Integration College of Computer Science Zhejiang University China Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Data Engineering NingboTech University China College of Computer Science and Technology Hangzhou City University China

Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of exploration. The diffusion probability model is powerful to learn complicated multimodal distributions, which has shown promising and potential applications to RL. In this paper, we formally build a theoretical foundation of policy representation via the diffusion probability model and provide practical implementations of diffusion policy for online model-free RL. Concretely, we character diffusion policy as a stochastic process induced by stochastic differential equations, which is a new approach to representing a policy. Then we present a convergence guarantee for diffusion policy, which provides a theory to understand the multimodality of diffusion policy. Furthermore, we propose the DIPO, which implements model-free online RL with DIffusion POlicy. To the best of our knowledge, DIPO is the first algorithm to solve model-free online RL problems with the diffusion model. Finally, extensive empirical results show the effectiveness and superiority of DIPO on the standard continuous control Mujoco benchmark. © 2023, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Website Fingerprinting Attack Based on Parameter Prediction Model for Conceptual Drift

A Website Fingerprinting Attack Based on Parameter Predictio...

引用

Software System and Information Processing (ICSSIP), IEEE International Conference on

作者： Jian Cui Taizhong Xu Li Zhao Shuai Han College of Computer Science and Technology Harbin Engineering University Harbin China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Electrical and Information Engineering Yunnan Minzu University Yunnan Key Laboratory of Unmanned Autonomous System Kunming China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

Website content updates and changes in the network environment can lead to concept drift, significantly impacting the performance of existing website fingerprinting methods. Recent research has focused on transfer learning-based approaches for concept drift adaptation to address this issue with limited samples. These methods involve pre-training a robust feature extractor and fine-tuning the classifier with a few samples. However, the effectiveness of these classifiers is highly dependent on the distributional gap between pre-training and fine-tuning datasets. As the divergence increases, more fine-tuning data is required to maintain classification performance. Consequently, existing methods struggle to achieve effective website identification tasks under severe concept drift with limited tuning data. To overcome these limitations, we propose a model parameter prediction approach, which adapts to new domains from data and model perspectives to enhance website identification performance. The proposed method predicts future model parameters to mitigate the effects of distributional changes, effectively adapting to evolving network environments. Experimental results demonstrate that our approach achieves significant improvements in accuracy, particularly under scenarios with limited tuning data and substantial concept drift.

关键词： Adaptation models Accuracy Concept drift Predictive models Fingerprint recognition Feature extraction Data models Robustness Long short term memory Tuning

来源：评论

学校读者我要写书评

暂无评论

Unlocking the Boundaries of Thought: A Reasoning Granularity Framework to Quantify and Optimize Chain-of-Thought

arXiv

引用

arXiv 2024年

作者： Chen, Qiguang Qin, Libo Wang, Jiaqi Zhou, Jinxuan Che, Wanxiang Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The Chinese University of Hong Kong Hong Kong

Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) on complex reasoning tasks. Recently, a series of studies attempt to explain the mechanisms underlying CoT, aiming to deepen the understanding of its efficacy. Nevertheless, the existing research faces two major challenges: (1) a lack of quantitative metrics to assess CoT capabilities and (2) a dearth of guidance on optimizing CoT performance. Motivated by this, in this work, we introduce a novel reasoning granularity framework (RGF) to address these challenges. To solve the lack of quantification, we first define a reasoning granularity (RG) to quantify the upper bound of CoT and establish a combination law for RG, enabling a practical quantitative approach applicable to various real-world CoT tasks. To address the lack of optimization, we propose three categories of RGs. We further optimize these categories with combination laws focused on RG promotion and reasoning path optimization for CoT improvement. Through extensive experiments on 25 models and 4 tasks, the study validates the existence and rationality of the proposed framework. Furthermore, it explains the effectiveness of 10 CoT strategies and guides optimization from two perspectives. We hope this work can provide a comprehensive understanding of the boundaries and optimization strategies for reasoning in LLMs. Our code and data are available at https://***/LightChen233/reasoning-granularity. © 2024, CC BY-SA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

引用

Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

来源：评论

学校读者我要写书评

暂无评论

Virtual Target Based Multi-agent Surrounding Approach

Virtual Target Based Multi-agent Surrounding Approach

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Weiping Zhu Haoyang Li Ning Ding Yukang Chen Yufei Sun Chao Ma Wei Li School of Computer Science Wuhan University P. R. China School of Cyber Science and Engineering Wuhan University P. R. China School of Artificial Intelligence and Computer Science Jiangsu Key Laboratory of Media Design and Software Technology Science Center for Future Foods Jiangnan University P. R. China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Multi-agent surrounding is a collaborative task that uses multiple agents to surround a stationary or moving target. Multi-agent surrounding has a wide range of applications, such as area monitoring of unmanned ships, environmental monitoring, and exploration of unknown environments. Existing work pay attention to the case of one-to-one surrounding of targets by agents, but there is a lack of consideration for the case where agents are not one-to-one with the target. In this paper, we propose the concept of virtual target, which is used as a mediator to realize the generic multi-agent surrounding a target. The main idea is to surround the actual target with the virtual target, while the agents surround the virtual target, where the generation of the virtual target is based on any given surrounding graph and random sampling, and the performance of the surrounding is ensured by the virtual target control algorithm and the agent controlling algorithm. The Lyapunov stability analysis and simulation results show that the proposed approach can make the virtual target fit the actual target effectively, and the agents can surround the actual target with the shape of the virtual target effectively.

关键词： Shape Simulation Fitting Stability analysis Sensors Reliability Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition 4th

Mirror Contrastive Loss Based Sliding Window Transformer for...

引用

4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819640003

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Post-Design Modifications for Impedance Matching of UHF RFID Tag Antenna

Post-Design Modifications for Impedance Matching of UHF RFID...

引用

2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Ali, Jawad Kaemarungsi, Kamol Chalermwisutkul, Suramate The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's U. of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

This paper proposes post-design modifications of a standard dipole antenna to match the footprint of different RFID chips while maintaining the impedance matching. A feeding gap of 4 mm is reduced to 0.6 mm to match the RFID chip's footprint. To address the analogy, two different methods are discussed including the placement of conducting pins over the dipole arms and addition of L-stubs to ensure the antenna's matching to the chip impedance with the reduced feeding gap. These modifications provide a simple and viable solution to the problem related to RFID chip footprint. The design is simulated using CST-MWS for NXP UCODE G2iL RFID chip with the impedance of 23-j224 Ω at 915 MHz. The results show a good agreement between the original design and proposed modifications with the bandwidth from 893.7 MHz to 936 MHz and the gain of around 2 dBi for all cases. The antenna is also studied for different chip impedance, where a coupled feeding dipole for HIGGS 3 RFID chip with the impedance of II-j162 Ω is used. The design is initially developed to match at 915 MHz and the gain of 0.158 dBi is obtained. The proposed post-design modifications can provide a universal UHF RFID antenna for any RFID chip footprint and impedance. © 2022 IEEE.

关键词： Dipole antennas

来源：评论

学校读者我要写书评

暂无评论

Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment

arXiv

引用

arXiv 2024年

作者： Kuang, Jidong Wang, Hongsong Han, Chaolei Gui, Jie The School of Cyber Science and Engineering Southeast University Nanjing210000 China Department of Computer Science and Engineering Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing210000 China The Engineering Research Center of Blockchain Application Supervision and Management Ministry of Education Southeast University Nanjing210000 China

Zero-shot action recognition, which addresses the issue of scalability and generalization in action recognition and allows the models to adapt to new and unseen actions dynamically, is an important research topic in computer vision communities. The key to zero-shot action recognition lies in aligning visual features with semantic vectors representing action categories. Most existing methods either directly project visual features onto the semantic space of text category or learn a shared embedding space between the two modalities. However, a direct projection cannot accurately align the two modalities, and learning robust and discriminative embedding space between visual and text representations is often difficult. To address these issues, we introduce Dual Visual-Text Alignment (DVTA) for skeleton-based zero-shot action recognition. The DVTA consists of two alignment modules-Direct Alignment (DA) and Augmented Alignment (AA)-along with a designed Semantic Description Enhancement (SDE). The DA module maps the skeleton features to the semantic space through a specially designed visual projector, followed by the SDE, which is based on cross-attention to enhance the connection between skeleton and text, thereby reducing the gap between modalities. The AA module further strengthens the learning of the embedding space by utilizing deep metric learning to learn the similarity between skeleton and text. Our approach achieves state-of-the-art performances on several popular zero-shot skeleton-based action recognition benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

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

ISBN: (纸本)9798350358780

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. © 2023 IEEE.

关键词： Data integration

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：