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False Negative Sample Detection for Graph Contrastive Learning

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Tsinghua science and Technology 2024年第2期29卷 529-542页

作者： Binbin Zhang Li Wang College of data science Taiyuan University of TechnologyJinzhong 030600China

Recently,self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning,which aims to learn discriminative features for each node without label information. The key to graph contrastive learning is data augmentation. The anchor node regards its augmented samples as positive samples,and the rest of the samples are regarded as negative samples,some of which may be positive samples. We call these mislabeled samples as “false negative” samples,which will seriously affect the final learning effect. Since such semantically similar samples are ubiquitous in the graph,the problem of false negative samples is very significant. To address this issue,the paper proposes a novel model,False negative sample Detection for Graph Contrastive Learning (FD4GCL),which uses attribute and structure-aware to detect false negative samples. Experimental results on seven datasets show that FD4GCL outperforms the state-of-the-art baselines and even exceeds several supervised methods.

关键词： graph representation learning contrastive learning false negative sample detection

Cohomology groups of a new class of Kadison-Singer algebras

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science China Mathematics 2024年第3期67卷 593-606页

作者： Guangyu An Xing Cheng Jun Sheng School of Mathematics and data science Shaanxi University of Science and TechnologyXi’an 710021China

Let N be a maximal discrete nest on an infinite-dimensional separable Hilbert space H,ξ=∑^(∞)_(n=1)en/2n be a separating vector for the commutant N',E_(ξ)be the projection from H onto the subspace[Cξ]spanned by the vectorξ,and Q be the projection from K=H⊕H⊕H onto the closed subspace{(η,η,η)^(T):η∈H}.Suppose that L is the projection lattice generated by the projections(E_(ξ) 0 0 0 0 0 0 0 0),{(E 0 0 0 0 0 0 0 0):E∈N},(I 0 0 0 I 0 0 0 0) and *** show that L is a Kadison-Singer lattice with the trivial ***,we prove that every n-th bounded cohomology group H~n(AlgL,B(K))with coefficients in B(K)is trivial for n≥1.

关键词： Kadison-Singer algebra Kadison-Singer lattice derivation cohomology group

THE EXTREMES OF DEPENDENT CHI-PROCESSES ATTRACTED BY THE BROWN-RESNICK PROCESS

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Acta Mathematica Scientia 2024年第2期44卷 686-701页

作者：孙俊杰谭中权 College of data science Jiaxing UniversityJiaxing314001China

Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized poi... 详细信息

关键词： chi-processes Brown-Resnick process pointwise maxima functional limit theorem

Covariate-Assisted Matrix Completion with Multiple Structural Breaks

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Journal of Systems science & Complexity 2024年第2期37卷 692-728页

作者： MENG Jing FENG Long ZOU Changliang WANG Zhaojun School of Statistics and data science KLMDASRLEBPSand LPMCNankai UniversityTianjin 300071China

In matrix completion,additional covariates often provide valuable information for completing the unobserved entries of a high-dimensional low-rank matrix *** this paper,the authors consider the matrix recovery problem when there are multiple structural breaks in the coefficient matrix β under the column-space-decomposition model A=Xβ+B.A cumulative sum(CUSUM)statistic is constructed based on the penalized estimation of β.Then the CUSUM is incorporated into the Wild Binary Segmentation(WBS)algorithm to consistently estimate the location of ***,a nearly-optimal recovery of A is *** findings are further corroborated via numerical experiments and a real-data application.

关键词： Additional covariates matrix completion multiple structural breaks wild Binary Segmentation

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Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1757-1776页

作者： Minfeng Qi Ziyuan Wang Qing-Long Han Jun Zhang Shiping Chen Yang Xiang School of science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn3122VictoriaAustralia Faculty of data science City University of MacaoMacaoChina CSIRO data61 SydneyNew South WalesAustralia

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

关键词： Blockchain internet of healthcare things(IoHT) privacy-preserving techniques(PPTs).

Physical information-enhanced graph neural network for predicting phase separation

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Chinese Physics B 2024年第7期33卷 278-283页

作者：张亚强王煦文王雅楠郑文 Institute of Public-Safety and Big data College of Computer Science and Technology(College of Data Science)Taiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Centre for Intelligent data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China Innovation Academy for Microsatellites of Chinese Academy of sciences Shanghai 200050China

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词： graph neural network phase separation machine learning dissipative particle dynamics

Computing Persistent Homology by Spanning Trees and Critical Simplices

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Research 2024年第2期2023卷 519-527页

作者： Dinghua Shi Zhifeng Chen Chuang Ma Guanrong Chen Department of Mathematics Cllege of ScienceShanghai UniversityShanghaiChina School of Big data Fuzhou University of International Studies and TradeFuzhouChina Department of data science and Big data Technology School of InternetAnhui UniversityHefeiChina Department of Electrical Engineering City University of Hong KongHong KongChina

Topological data analysis can extract effective information from higher-dimensional *** mathematical basis is persistent *** persistent homology can calculate topological features at different spatiotemporal scales of the dataset,that is,establishing the integrated taxonomic relation among points,lines,and ***,the simplicial network composed of all-order simplices in a simplicial complex is *** the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values,a method based on the concept of critical simplices can be developed by searching all-order spanning *** this new method,not only the Morse function values with the theoretical minimum number of critical simplices can be obtained,but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated ***,this method is used to analyze some examples and compared with other methods,showing its effectiveness and feasibility.

关键词： Morse establishing topological

Asymptotic Inference in the Random Coefficient Autoregressive Model with Time-functional Variance Noises

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 320-346页

作者： En-wen ZHU Zi-wei DENG Han-jun ZHANG Jun CAO Xiao-hui LIU Department of Mathematics and Statistics Changsha University of Science and TechnologyChangsha 410114China School of Mathematics and Computational science Xiangtan UniversityXiangtan 411105China School of Statistics and data science and Key Laboratory of Data Science in Finance and EconomicsJiangxi University of Finance and EconomicsNanchang 330013China

This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV *** first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant *** semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.

关键词： random coefficient autoregressive model time-functional variance conditional least squares semiparametric least squares

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4703-4724页

作者： Peng Xiao Qi Zhong Jingxue Chen Dongyuan Wu Zhen Qin Erqiang Zhou Network and data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of data science City University of MacaoMacao999078China

In the intelligent medical diagnosis area,Artificial Intelligence(AI)’s trustworthiness,reliability,and interpretability are critical,especially in cancer *** neural networks,while excellent at processing natural images,often lack interpretability and adaptability when processing high-resolution digital pathological *** limitation is particularly evident in pathological diagnosis,which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the ***,the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and *** this paper,we introduce an innovative Multi-Scale Multi-Branch Feature Encoder(MSBE)and present the design of the CrossLinkNet *** MSBE enhances the network’s capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and *** CrossLinkNet Framework,serving as a versatile image segmentation network architecture,employs cross-layer encoder-decoder connections for multi-level feature fusion,thereby enhancing feature integration and segmentation *** quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet,equipped with the MSBE encoder,not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature ***,CrossLinkNet emphasizes the interpretability of the AI model,a crucial aspect for medical professionals,providing an in-depth understanding of the model’s decisions and thereby enhancing trust and reliability in AI-assisted diagnostics.

关键词： Explainable AI security trustworthy CrossLinkNet whole slide images