检索结果-内蒙古大学图书馆

Homophily Induced Contrastive Attributed Graph Clustering

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China South China Technology Commercialization Center Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China

Attributed graph clustering, aiming to discover the underlying graph structure and partition the graph nodes into several disjoint categories, is a basic task in graph data analysis. Although recent efforts over graph contrastive clustering have achieved decent performances, most of them get accustomed to construct the positive neighbor set by the generated pseudo clustering information, directly ignoring the ready-made neighbor nodes and the underlying semantics of edges in a graph. How to well deal with the graph neighbor-specific information to facilitate the performance of graph contrastive clustering is still a challenging problem. Therefore, in this work, we propose a novel Homophily Induced Contrastive Attributed Graph Clustering (HomoCAGC) method, where the power of homophily is exploited in facilitating the performance of contrastive attributed graph clustering, while the pseudo homophily in a graph is also explored and distinguished. Especially, the node feature as reliable information guidance is used to compute the underlying feature-oriented pair-wise node similarity, based on which the positive node pairs in contrastive regularizer are adjusted for better node representation characterization. According to the refined node representations, a triplet self-supervised clustering objective is well-designed to ensure the output embedding is cluster-oriented, and suitable for the downstream clustering task. Extensive experiments on seven benchmarks are conducted to demonstrate the effectiveness of HomoCAGC. © 1991-2012 IEEE.

关键词： Attributed graph clustering contrastive learning graph homophily self-supervised clustering

来源：评论

学校读者我要写书评

暂无评论

Achieving High Open-Circuit Voltage in Efficient Kesterite Solar Cells Via Lanthanide Europium Ion Induced Carrier Lifetime Enhancement

SSRN

引用

SSRN 2024年

作者： Chen, Xingye Zhao, Yunhai Ahmad, Nafees Zhao, Jun Zheng, Zhuanghao Su, Zhenghua Peng, Xiaogang Li, Xuejin Zhang, Xianghua Fan, Ping Liang, Guangxing Chen, Shuo Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China UMR 6226 Université de Rennes Rennes F-35000 France Shenzhen518060 China

Short carrier lifetimes is a key challenge limiting the open-circuit voltage (VOC) and power conversion efficiency (PCE) of kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. In this work, for the first time, lanthanide europium (Eu) ions have been introduced into Ag-incorporated CZTSSe absorber layer, which can modify the selenization reaction pathway during the initial selenization process, leading to high-quality (Ag, Eu)-CZTSSe absorber with large compact crystal grains and benign surface potential fluctuations. This innovative absorber engineering can also optimize defects dynamics with less detrimental defects and defects-assisted non-radiative recombination. Thus, significantly enhanced minority carrier lifetimes from 0.97 to 3.15 ns can be achieved, representing one of the top values among various bulk and/or interface engineering treated CZTSSe. The champion CZTSSe thin-film solar cell exhibits an impressive VOC of 545.6 mV, accompanied with a stimulating increase in PCE from 10.61% to 13.30%. These findings underscore the potential of lanthanide cation doping to drive significant advancements in CZTSSe photovoltaic technology, and therefore promoting its further development and future applications. © 2024, The Authors. All rights reserved.

关键词： Open circuit voltage

来源：评论

学校读者我要写书评

暂无评论

HiGIL: Hierarchical Graph Inference Learning for Fact Checking

HiGIL: Hierarchical Graph Inference Learning for Fact Checki...

引用

IEEE International Conference on data Mining (ICDM)

作者： Qianren Mao Yiming Wang Chenghong Yang Linfeng Du Hao Peng Jia Wu Jianxin Li Zheng Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Computing Macquarie University Sydney Australia The School of Computing University of Leeds Leeds U.K.

Fact-checking is vital for countering fake news. This process requires verifying the truthfulness of a claim by reasoning about multiple pieces of evidence. The current dominant approach depends upon capturing the claim-evidence relations from a claim-evidence interaction graph. Existing solutions utilize phrase-level semantics on a single-granularity but ignore other hierarchical features, such as fact- and sentence-level textual semantics and their logical topology. Since the hierarchical features often provide hints to infer collaborative high-order clues that can be essential for fact-checking, they should not be overlooked. This paper proposes a better method to model the claim-evidence graph in a multi-granularity manner. Doing so allows one to exploit more textual semantics and logical topology between a claim and its evidence. To achieve the target, we first employ a graph inference learning framework to infer graph nodes on different granular semantic units within their hierarchical topology. Then, an inference learning procedure is designed to optimize the global textual similarity and local topological reachability from the claim-evidence graph. We evaluate our approach by applying it to fact-checking on an open dataset, and experimental results show that our technique outperforms existing graph-based techniques by a large margin.

关键词： Semantics Collaboration Information retrieval Cognition Topology data mining Usability

来源：评论

学校读者我要写书评

暂无评论

Feature Selection Based on Consistent Granulation

SSRN

引用

SSRN 2024年

作者： Yang, Tian Shen, Shuo Deng, Jinsheng Liang, Jie Qian, Yuhua Dai, Jianhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Institute of Big Data Science and Industry Shanxi university Shanxi Taiyuan030006 China

Classifying large-scale data is a challenging task in machine learning. Feature selection and feature construction can improve the classification performances of classifiers. However, existing feature selections struggle to handle large-scale data. Granulation is the key process for generating accurate and efficient models. In this paper, we propose a novel granulation approach based on rough sets, consistent granulation, and a novel feature construction based on consistent granulation to improve the computation speed, scale, and classification performance of feature selection. As a result, the time and space complexities are reduced to a linear level in terms of sample scales. Moreover, consistent granulation can precisely represent the topological structure of the data and reduce the parameter space from [0, 1] to a finite set lowering the difficulty of optimal parameter searching. Experiments show that the proposed algorithm, FCG, outperforms four classical feature selection algorithms in handling datasets with millions of samples or tens of thousands of dimensions. Additionally, on the first thirteen datasets, FCG reduces the average runtime by 1145.68 times (NARNeMF), 883.11 times (HANDI), 1333.10 times (GBNRS), and 3.73 times (RMDPS), while increasing the average classification accuracy by 2.73% (FARNeMF), 2.30% (HANDI), 4.61% (GBNRS) and 14.33% (RMDPS) compared to these methods. © 2024, The Authors. All rights reserved.

关键词： Granulation

来源：评论

学校读者我要写书评

暂无评论

StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_data$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

引用

Chinese Control Conference (CCC)

作者： Weidong Yan Zhaoliang Xu Yang Li The Department of Automation Science and Electrical Engineering Beihang University Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph-Augmented Contrastive Clustering for Time Series

SSRN

引用

SSRN 2023年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives, but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (Tsgcc) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that Tsgcc outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2023, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition

A Multi-Head Convolution Network with Attention Consistency ...

引用

Chinese Control Conference (CCC)

作者： Wenkang Liu Mingyi Sun Yang Li the Department of Automation Science and Electrical Engineering Beihang University Beijing China the Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

引用

Materials Genome Engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

来源：评论

学校读者我要写书评

暂无评论

Fast Robot Hierarchical Exploration Based on Deep Reinforcement Learning

Fast Robot Hierarchical Exploration Based on Deep Reinforcem...

引用

International Wireless Communications and Mobile computing Conference, IWCMC

作者： Shun Zuo Jianwei Niu Lu Ren Zhenchao Ouyang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing China Beihang Hangzhou Innovation Institute Yuhang Beihang University Beijing China

This paper investigates the use of reinforcement learning for autonomous exploration in an unknown environment. Autonomous exploration is crucial in many situations, such as urban search, security inspection, environmental mapping, etc. Traditional approaches focused on frontiers are unlikely to span a variety of enormously complex scenarios. Convergence is a little more difficult for learning-based approaches, which can adapt to many different environments. Consequently, a hierarchical exploration framework is built using frontier information. We propose a reinforcement learning-based local decision exploration model that uses deep neural networks to learn the optimal strategy from the environment. To prevent falling into local optimization, we also suggest a global rescue module to assist the robot in returning to the proper exploration track. Compared with other hierarchical methods, the framework is more effective and resilient in many contexts, greatly decreasing the total completion time and path length.

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：