检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongzhou Zhou Ziqi Shu Weixing Xie Junfeng Yao Qingqi Hong Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Institute of Artificial Intelligence Xiamen University Xiamen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S 2 CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks; (2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation; (3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S 2 CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon.

关键词： Image segmentation Three-dimensional displays Superresolution Collaboration Organizations Feature extraction Transformers Medical diagnosis Medical diagnostic imaging Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

RESEARCH ON THE REWARDS AND PUNISHMENTS OF URBAN SEWAGE TREATMENT UNDER THE UNCERTAINTY THEORY

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 111-124页

作者： Ruixi, Yuan Lin, Zhang Li, Cun-Lin Masron, Tajul Ariffin Yang, Bao-Jun School of management North Minzu University Yinchuan750021 China Universiti Sains Malaysia Pinang 13500 Malaysia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China School of Management The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Management Universiti Sains Malaysia Pinang 13500 Malaysia School of Business Northinzu University Yinchuan750021 China

This paper fills a gap in urban sewage treatment decision-making by exploring reward and punishment mechanisms within the uncertainty theory framework. Addressing challenges in characterizing sewage treatment capacity due to incomplete information, the study integrates incentive and punishment mechanisms in a decision-making model. Results indicate that government rewards encourage companies to treat more sewage, but a nuanced relationship exists between rewards, treatment capacity, and expected returns. The study emphasizes the need for a balanced approach, suggesting a focus on technology enhancement over sole reliance on rewards. Examining a double-headed sewage market, the research establishes an uncertain Cournot game model, providing insights into sewage treatment dynamics under asymmetric information. In conclusion, the paper contributes to practical reward and punishment strategies in sewage treatment policies, offering theoretical support for effective decision-making in urban sewage management within a concise framework.. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Sewage treatment

来源：评论

学校读者我要写书评

暂无评论

A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

SSRN

引用

SSRN 2022年

作者： Yu, Jiabin Chen, Zhihao Zhao, Zhiyao Yao, Peng Xu, Jiping Artificial Intelligence Beijing Technology and Business University Beijing100048 China China and Beijing Laboratory for Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China College of Engineering Ocean University of China Qingdao266100 China

The multi-USVs cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2022, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

SSRN

引用

SSRN 2023年

The multi-unmanned surface vessels (USVs) cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2023, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Learning Representation for Clustering via Dual Correlation

Learning Representation for Clustering via Dual Correlation

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Tao Zhang Mingming Hu State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Shenyang China Data Management Department Center for Clinical Studies Beijing China

Contrastive learning is a self-supervised learning method used to learn the general features of a dataset without labels by letting the model learn which data points are similar or different. It constructs labeled samples through data augmentation. Therefore, the core and difficulty of self-supervised contrastive learning are to construct high-quality contrastive samples, especially negative sample pairs. In this paper, a cluster-based contrast sample selection method is proposed, which selects the positive contrast samples from the same cluster and the negative contrast samples from different clusters. By minimizing the forward contrast sample loss and maximizing the reverse sample loss, the deep representation clustering result of the image is obtained. The experimental results show the excellence and adaptability of the algorithm.

关键词： Correlation Automation Clustering algorithms Self-supervised learning data models

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization

引用

IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Wang, Zhenzhong Xu, Dejun Jiang, Min Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong P.R. China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Xiamen Fujian China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong P.R. China

Dynamic Constrained Multiobjective Optimization Problems (DCMOPs) are characterized by multiple conflicting optimization objectives and constraints that vary over time. The presence of both dynamism and constraints underscores the importance of preserving population diversity. This diversity is essential not only to escape local optima following environmental changes but also to climb infeasible barriers to approach feasible regions. However, existing constraint-handling techniques for enhancing solution feasibility could steer infeasible solutions toward partially feasible regions, potentially resulting in the loss of diversity. To maintain both diversity and feasibility, this work establishes two synergistic tasks: one task concentrates on exploring the unconstrained search space to preserve diversity, while the other delves into searching the constrained search space to prioritize feasibility. Particularly, in light of evolutionary transfer optimization, two knowledge transfer modules, i.e., the spatial knowledge transfer module and temporal knowledge transfer module are designed. The spatial knowledge transfer module facilitates knowledge transfer between the constrained and unconstrained search spaces to accelerate the exploration of both spaces. On the other hand, the temporal transfer module leverages historical knowledge to enhance search efficiency within the new environment. To advance the test suite toward real-world cases, we designed fourteen test problems with various properties. Experiments conducted on the proposed test problems and a real-world problem have demonstrated the efficacy of our proposed algorithm. IEEE

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection

arXiv

引用

arXiv 2023年

作者： He, Junwei Xu, Qianqian Jiang, Yangbangyan Wang, Zitai Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering CAS 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

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the Anomaly Overfitting and Homophily Trap problems caused by the abnormal patterns in the graph, breaking the assumption that normal nodes are often better reconstructed than abnormal ones. Our observations indicate that models trained on graphs with fewer anomalies exhibit higher detection performance. Based on this insight, we introduce a novel two-stage framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns. In the next stage, the decoders are retrained for detection on the original graph, benefiting from the multi-level representations learned in the previous stage. Meanwhile, we propose the node anomaly distribution regularization to further alleviate Anomaly Overfitting. We validate the effectiveness of our approach through extensive experiments on both synthetic and real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Media Attention and ESG Greenwashing: Evidence from Chinese Listed Companies

SSRN

引用

SSRN 2023年

作者： Jiang, Cuixia Li, Xiuxiu Xu, Qifa Liu, Junhang School of Management Hefei University of Technology Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China

We investigate the relationship between media attention and environmental, social, and governance (ESG) greenwashing using the data of Chinese A-share listed companies from 2011 to 2020. Our results show that media attention can inhibit corporate ESG greenwashing behavior. Moreover, this inhibiting effect is mainly caused by paper media and becomes more prominent in non-state-owned enterprises (non-SOEs). The mediation analysis proves that media attention plays an important role in improving investor attention, thereby inhibiting corporate ESG greenwashing. The moderation analysis indicates that the effect of media attention on inhibiting ESG greenwashing is weakened in companies with more analyst following and higher quality of internal control. Our study provides a new view on the governance of ESG greenwashing in China. © 2023, The Authors. All rights reserved.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Electricity Price Forecasting and Uncertainty Estimation Using Deep Bayesian Model with Multi-Distribution Fusion

SSRN

引用

SSRN 2024年

作者： Shao, Zhen Han, Yating Fu, Chao Zha, Jianrui Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Hefei University of Technology Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Probabilistic electricity price forecasting (EPF) is paramount for stakeholder scheduling and trading in deregulated energy markets. However, during the process of establishing a probability prediction model, the consequential aleatoric and epistemic uncertainties are indistinguishable. Moreover, identifying epistemic uncertainty rather than aleatoric uncertainty is significant for conducting credible probabilistic EPF in cases with expected and unexpected fluctuations. This article proposes a hybrid approach under a Bayesian probability framework for performing reliable and accurate probabilistic forecasting. Specifically, it applies Bayesian deep learning model to convey the latent probability distribution of electricity prices, which represents the desirable epistemic uncertainty and inevitable aleatoric uncertainty. The embedded architecture employs a multi-head attention mechanism (MHAM) to allocate previous hidden states of long short-term memory (LSTM) cells, detecting both the local and global price patterns simultaneously. Considering the forecasting bias arising from aleatoric uncertainty in the process of establishing Bayesian networks, a new Wasserstein distance-based multi-distribution fusion mechanism driven by evidence theory is proposed and employed to identify and mitigate the aleatoric uncertainty of credible probabilistic EPF problems. The effectiveness of the proposed approach is evaluated on two real-world electricity price datasets and compared with twelve state-of-the-art models. © 2024, The Authors. All rights reserved.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

arXiv

引用

arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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：