检索结果-内蒙古大学图书馆

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

来源：评论

学校读者我要写书评

暂无评论

Wasserstein distance regularized graph neural networks

引用

INFORMATION SCIENCES 2024年 670卷

作者： Shi, Yong Zheng, Lei Quan, Pei Niu, Lingfeng Univ Chinese Acad Sci Sch Math Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Distribution shift widely exists in graph representation learning and often reduces model performance. This work investigates how to improve the performance of a graph neural network (GNN) in a single graph by controlling distribution shift between embedding spaces. Specifically, we provide an upper error -bound estimation, which quantitatively analyzes how distribution shift affects GNNs' performance in a single graph. Considering that there is no natural domain division in a single graph, we propose PW-GNN to simultaneously learn discriminative embedding and reduce distribution shift. PW-GNN measures distribution discrepancy using the distance between test embeddings and prototypes, and transfers minimizing distribution shift to minimizing the power of Wasserstein distance, which is introduced into GNNs as a regularizer. A series of theoretical analyses are carried out to demonstrate the effectiveness of PW-GNN. Besides, a lowcomplexity training algorithm is designed by exploring entropy -regularized strategy and block coordinate descent method. Extensive numerical experiments are conducted on different datasets with both biased and unbiased splits. We empirically test our model equipped with four backbone models. Results show that PW-GNN outperforms state-of-the-art baselines and mitigates up to 8% of negative effects off distribution shift on backbones.

关键词： Wasserstein distance Graph neural networks Distribution shift

来源：评论

学校读者我要写书评

暂无评论

Integrated GCN-LSTM stock prices movement prediction based on knowledge-incorporated graphs construction

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第1期15卷 161-176页

作者： Shi, Yong Wang, Yunong Qu, Yi Chen, Zhensong Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

Stock prices movement prediction has been a longstanding research topic. Many studies have introduced several kinds of external information like relations of stocks, combined with internal information of trading characteristics to promote forecasting. Different from previous cases, this article proposes a reasonable assumption that major fluctuations of stock prices are mainly triggered by high-volume transactions which usually occur on a group of stocks that share some common features (e.g., stocks in the same industry, region, concept or yield similar volatility), and further develops an integrated GCN-LSTM method to achieve more precise predictions from the perspective of modelling capital flows. First, we construct four kinds of graphs incorporating various relational knowledge (edge) and utilize graph convolutional network (GCN) to extract stock (node) embeddings in multiple time-periods. Then, the obtained temporal sequences of stock embeddings are put into long short-term memory recurrent neural network (LSTM) to discriminate the moving direction of prices. Extensive experiments on major Chinese stock indexes have demonstrated the effectiveness of our model with best accuracy of 57.81% acquired, which is much better than baselines. Moreover, experimental results of GCN-LSTM under different graphs and various node embedding dimensions have been compared and analyzed, indicating the selection of key parameters to achieve optimal performances. Our research findings provide an improved model to forecast stock prices movement directions with a reliable theoretical interpretation, and in depth exhibit insights for further applications of graph neural networks and graph data in business analytics, quantitative finance, and risk management decision-makings.

关键词： Stock prices movement prediction knowledge-incorporated graphs Graph construction Graph convolutional network (GCN) Long short-term memory recurrent neural network (LSTM)

来源：评论

学校读者我要写书评

暂无评论

Metabolomics Combined with Network Pharmacology Uncovers Effective Targets of Tao-Hong-Si-Wu Decoction for Its Protection from Sepsis-Associated Acute Lung Injury

引用

Journal of Analysis and Testing 2023年第2期7卷 172-186页

作者： Jinzhi He Longfei Wang Yang Wang Zeng Li Feihu Chen Zhicheng Liu Anhui Provincial Laboratory of Infl ammatory and Immunity Disease Anhui Institute of Innovative Drugs School of PharmacyAnhui Medical UniversityHefei 230032China Shanxi Key Laboratory of Big Data for Clinical Decision Research School of ManagementShanxi Medical UniversityTaiyuan 030000China

Sepsis-induced acute lung injury(ALI)is a leading cause of death among septic ***-Hong-Si-Wu decoction(TSD),a classical recipe from traditional Chinese medicine used for treating ischemic stroke,has been recently reported to alleviate inflammation and inflammation-stimulated injuries related to the pathology of ***,we first observed the therapeutic effect of TSD on sepsis-induced *** on integrated metabolomics and network pharmacology analysis(NPA)techniques,we aim to understand the mechanism of TSD alleviating ***’s effects were observed in rats modeled by cecal ligation and puncture(CLP)and rat macrophages stimulated by lipopolysaccharide(LPS).Metabolomics analyses were applied to determine the ingredients in the medicine and key metabolites correlated to the NPA for the prediction of TSD *** and protein expressions of the key predicted targets were evaluated in the lung tissue and macrophages of septic model rat by quantitative polymerase chain reaction(PCR)and enzyme-linked immunosorbent assays,*** improved survival rate and protected against lung injury in CLP *** endogenous metabolites were related to TSD’s *** significantly suppressed IL-6 and TNF-αsecretions and their gene expressions both in the lung tissue of the model rats and in LPS-stimulated *** also restored decreased lung protein expression of VEGFA in septic model *** proteins and their affecting metabolites were finally validated in an external test set of *** study shows that metabolomics coupled with NPA is a promising approach to explore potential targets of medicine with complex compositions.

关键词： Metabolomics Drug eff ect analysis Sepsis Traditional Chinese medicine Cytokines Bioanalysis

来源：评论

学校读者我要写书评

暂无评论

Fintech, technological innovation and regional economic growth: Theoretical modeling and empirical evidence

引用

CHINA ECONOMIC REVIEW 2025年 91卷

作者： Huang, Xirong Dong, Jichang Li, Xiuting Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Hefei Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This study aims to investigate the role of fintech in stimulating innovation and promoting regional economic growth. A Multi-Sector Endogenous Growth Model (MEGM) is constructed by incorporating fintech while internalizing technological progress and human capital accumulation within the framework of creative destruction. Based on this model, we theoretically analyze how fintech influences innovation and regional economic growth, followed by empirical testing using panel data from 246 prefecture-level cities in China during the period from 2012 to 2020. Furthermore, we contextualize these findings within China's fintech development landscape, trends in technological innovation, and patterns of regional economic growth. The results demonstrate that fintech exerts a significant incentive effect on regional innovation and effectively promotes economic growth. Heterogeneity analysis based on cities' endowments reveals that the impact of fintech is more pronounced in financially developed cities, those with high digital endowments, as well as in first- and second-tier cities. The analyses presented herein elucidate how fintech facilitates technology-driven regional economic growth in China, providing valuable insights for fostering innovation-driven development through fintech.

关键词： Fintech Technological innovation Economic growth Endogenous growth model Panel data analysis

来源：评论

学校读者我要写书评

暂无评论

Joint user profiling with hierarchical attention networks

引用

Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

引用

Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

来源：评论

学校读者我要写书评

暂无评论

The cluster D-trace loss for differential network analysis

引用

JOURNAL OF APPLIED STATISTICS 2024年第10期51卷 1843-1860页

作者： Yan, Han Lu, Shuhan Zhang, Sanguo Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Calif Los Angeles Dept Math Los Angeles CA USA

A growing literature suggests that gene expression can be greatly altered in disease conditions, and identifying those changes will improve the understanding of complex diseases such as cancers or diabetes. A prevailing direction in the analysis of gene expression studies the changes in gene pathways which include sets of related genes. Therefore, introducing structured exploration to differential analysis of gene expression networks may lead to meaningful discoveries. The topic of this paper is differential network analysis, which focuses on capturing the differences between two or more precision matrices. We discuss the connection between the thresholding method and the D-trace loss method on differential network analysis in the case that the precision matrices share the common connected components. Based on this connection, we further propose the cluster D-trace loss method which directly estimates the differential network and achieves model selection consistency. Simulation studies demonstrate its improved performance and computational efficiency. Finally, the usefulness of our proposed estimator is demonstrated by a real-data analysis on non-small cell lung cancer.

关键词： Gaussian graphical models high-dimensional setting precision matrices hierarchical clustering model selection consistency

来源：评论

学校读者我要写书评

暂无评论

Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

来源：评论

学校读者我要写书评

暂无评论

Divide and conquer: A granular concept-cognitive computing system for dynamic classification decision making

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期308卷 255-273页

作者： Mi, Yunlong Wang, Zongrun Liu, Hui Qu, Yi Yu, Gaofeng Shi, Yong Cent South Univ Sch Business Changsha 410083 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Sanming Univ Sch Management & Econ Sanming 365004 Fujian Peoples R China

Dynamic classification decision making is a crucial issue in management decision making and data min-ing, which is widely applied in different areas such as human-machine collaborative decision making, network intrusion detection, and traffic data stream mining. However, the existing strategies of static classification decision making are always unable to achieve desired outcomes in ill-structured domains, as the standard machine learning approaches mainly focus on static learning, which is not suitable to mine evolving dynamic data to support decision making. In addition, the main factors regarding incor-rect classification predictions are also important for knowledge management and decision making, which is often ignored in many standard learning systems. Therefore, inspired by the idea of divide and con-quer, we in this article propose a novel dynamic concept learning framework, namely granular concept -cognitive computing system (gC3S), for dynamic classification decision making by transforming instances into concepts. More specifically, to better characterize the process of dynamic classification decision mak-ing, we give the objective function of gC3S via mathematical programming theory. For management deci-sion making, gC3S emphasizes tracing the corresponding approximate concepts via the incorrect classifi-cation predictions. Finally, we also apply gC3S to traffic data stream mining, and the experimental results on the different real-world situations further demonstrate that our approach is very effective for dynamic classification decision making.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Decision support systems Dynamic classification decision making Dynamic learning Granular computing Concept -cognitive computing

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：