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Variational Bayesian Tensor Quantile Regression

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Acta Mathematica Sinica,English Series 2024年

作者： Yunzhi JIN Yanqing ZHANG Yunnan Key Laboratory of Statistical Modeling and data analysis Southwest United Graduate School Yunnan University Yunnan Key Laboratory of Statistical Modeling and data analysis Yunnan University

Quantile regression is widely used in variable relationship research for statistical *** quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods. However, many modern applications involve tensor data with the intrinsic tensor structure. Traditional quantile regression can deal with tensor regression issues well. To this end, we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients. To incorporate the sparsity of tensor coefficients, we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients. The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation. The coordinate ascent algorithm is employed to optimize variational lower bound. Simulation studies and a real example show the numerical performances of the proposed method.

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Disease Burden and Costs Associated with Multiple Sclerosis in China:A Cross-sectional analysis of Nationwide Survey data

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Neuroscience Bulletin 2024年第4期40卷 533-538页

作者： Yusheng Jia Xuanqi Qiao Jin Zhao Hainan Li Shanlian Hu Min Hu Department of health Economics School of Public HealthFudan UniversityShanghai200032China National health Commission Key Laboratory of health Technology Assessment Fudan UniversityShanghai200032China

DearEditors,Multiple sclerosis(MS)is a chronic,autoimmune neurological disorder characterized by inflammatory demyelination of the central nervous system;it is associated with reduced mobility,cognitive impairment,and numerous comorbidities[1].Global MS incidence(2.1 per 100,000 person-years)and prevalence(30.1 per 100,000)have increased in recent years,and its prevalence is notably higher in Western countries,ranging from~30 to 200 cases per 100,000[2].In China,recent epidemiological research has shown that the crude prevalence was 2.44 per 100,000 in 2016[3],and the age-and sex-adjusted incidence was 0.235 per 100,000 person-years[4].Despite the relatively low prevalence compared with the global average,China has extensively addressed the well-being of over 30,000 MS patients,enrolling MS in China's First National List of Rare Diseases in 2018.

关键词： prevalence impairment autoimmune

Visible-frequency nonvolatile reconfigurable Janus metasurfaces for dual-wavelength-switched and spin-asymmetric holograms

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Photonics Research 2024年第2期12卷 356-368页

作者： HUAN YUAN ZHEQIANG ZHONG BIN ZHANG College of Electronics and Information Engineering Sichuan UniversityChengdu 610064China Big data analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu 610065China

Janus metasurface holography with asymmetric transmission characteristics provides new degrees of freedom for multiplexing ***,earlier metasurfaces with asymmetrical transmission faced limitations in terms of tunability and *** this study,we propose a metasurface color holographic encryption scheme with dynamic switching and asymmetric transmission at visible frequencies using a low-loss nonvolatile optical phase-change material,Sb_(2)S_(3).Using a modified holographic optimization strategy,we achieved highfidelity asymmetric holographic imaging of a nanostructured *** controlling the incident direction and wavelength of visible light,as well as the level of crystallization of Sb_(2)S_(3),this reconfigurable metasurface enables the precise manipulation of tunable color holographic image *** particular,in the semi-crystalline state of Sb_(2)S_(3),the encoded information can be securely encrypted using a two-channel color-holographic image,whereas only a preset camouflaged image is displayed in the crystalline or amorphous state of Sb_(2)S_(3).The proposed multiencrypted Janus metasurface provides a potential approach for dynamic holographic displays with ultrahigh capacity,holographic encryption,and information storage.

关键词： holographic asymmetric visible

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Learning-based counterfactual explanations for recommendation

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Science China(Information Sciences) 2024年第8期67卷 115-129页

作者： Jingxuan WEN Huafeng LIU Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic data analysis and Mining Beijing Jiaotong University

Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause. They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism. Among several counterfactual recommendation methods, item-based counterfactual explanation methods have attracted considerable attention because of their flexibility. The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items(i.e., short length) such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions(i.e., good quality). Usually, explanations are generated by ranking the precomputed importance of items, which fails to characterize the true importance of interacted items due to separation from the explanation generation. Additionally, the final explanations are generated according to a certain search strategy given the precomputed importance. This indicates that the quality and length of counterfactual explanations are deterministic;therefore, they cannot be balanced once the search strategy is fixed. To overcome these obstacles, this study proposes learning-based counterfactual explanations for recommendation(LCER) to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference. To achieve consistency between the computation of importance and generation of counterfactual explanations, the proposed LCER endows an optimizable importance for each interacted item, which is supervised by the goal of counterfactual explanations to guarantee its credibility. Because of the model's flexibility, the trade-off between quality and length can be customized by setting different proportions. The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several

关键词： counterfactual explanation counterfactual inference explainable recommendation item-based explanation recommender system

IoTDQ: An Industrial IoT data analysis Library for Apache IoTDB

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Big data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of Software Tsinghua UniversityBeijing 100084China National Engineering Research Center for Big data Software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry *** loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O *** User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB *** satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache *** library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,*** enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability *** provides a framework for users to examine loT time series with data quality *** show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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Computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big data Network Security Detection and analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： data enhancement LSTM deep residual network VAE

Text mining and data analysis identify potential drugs and pathways for polycystic ovary syndrome treatment

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生殖与发育医学(英文) 2024年第1期08卷 32-38页

作者： Yuan Xiao-Jing Wang Yan Yang Hai-Yan Zhao Bin Medical Reproductive Center People’s Hospital of Jiuquan City 3. Jiuquan 735000 4. China Department of Gynecological Oncology The Fourth Affiliated Hospital of Guangxi Medical University 7. Liuzhou 545000 8. Xiamen health and Medical Big data Center 9. Xiamen 361001

Objective: Polycystic ovarian syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. This study aimed to use text mining and microarray data analysis to identify drugs that target genes and potential pathways associated with ***: We extracted a common set of genes associated with PCOS using text mining and the microarray dataset GSE48301. Next, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of these genes, as well as protein–protein interaction (PPI) network analysis. Additionally, we used MCODE and cytoHubba to cluster significant common genes in the PPI network and performed gene–drug interaction analyses to identify potential drugs for further investigation. Finally, we annotated pathways associated with the genes ***: Text mining and microarray analysis yielded 696 text mining genes (TMGs) and 2804 differentially expressed genes (DEGs). Among these, a set of 77 genes was found in both TMGs and DEGs. Interestingly, 67 of these genes participated in constructing the PPI network. Seven common hub genes were selected using the MCODE and CytoHubba methods. Finally, five out of seven genes were targeted by 15 existing ***: Four genes (FASLG, IL13, IL17A, andIL2RA), which are mainly related to the cytokine–cytokine receptor interaction pathway, could be prioritized as targets for PCOS.

关键词： Text mining Differentially expressed genes Polycystic ovary syndrome Drug discovery

Metagenomics reveals the microbial community and functional metabolism variation in the partial nitritation-anammox process:From collapse to recovery

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Journal of Environmental Sciences 2024年第1期135卷 210-221页

作者： Hong Wang Hui Gong Xiaohu Dai Min Yang State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China BIOMATH Department of Data Analysis and Mathematical ModellingGhent UniversityGhent 9000Belgium

Mainstream partial nitritation-anammox(PNA)process easily suffers from performance instability and even reactor collapse in ***,it is of great significance to unveil the characteristic of performance recovery,understand the intrinsic mechanism and then propose operational *** this study,we combined long-term reactor operation,batch tests,and metagenomics to reveal the succession of microbial community and functional metabolism variation from system collapse to *** aeration control(0.10-0.25mg O_(2)/L)was critical for performance *** was also found that Candidatus Brocadia became the dominant flora and its abundance increased from 3.5%to 11.0%.Significant enhancements in carbon metabolism and phospholipid biosynthesis were observed during system recovery,and the genes abundance related to signal transduction was dramatically *** up-regulation of sdh and suc genes showed the processes of succinate dehydrogenation and succinyl-CoA synthesis might stimulate the production of amino acids and the synthesis of proteins,thereby possibly improving the activity and abundance of AnAOB,which was conducive to the performance ***,the increase in abundance of hzs and hdh genes suggested the enhancement of the anammox *** in the abundance of key genes involved in nitrogen metabolism indicated that nitrogen removal pathway was more diverse after system *** achievement of performance recovery was driven by anammox,nitrification and denitrification coupled with dissimilatory nitrate reduction to *** results provide deeper insights into the recovery mechanism of PNA system and also provide a potential regulation strategy for the stable operation of the mainstream PNA process.

关键词： Partial nitritation-anammox Metagenomics Microbial community Functional metabolism Recovery

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Compressed data Separation via ℓ_(q)-Split analysis with ℓ_(∞)-Constraint

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Acta Mathematica Sinica,English Series 2024年第7期40卷 1655-1673页

作者： Ming Yang Gu Song Li Jun Hong Lin School of Mathematical Science Zhejiang UniversityHangzhou310027P.R.China Center for data Science Zhejiang UniversityHangzhou310027P.R.China

In this paper,we study compressed data separation(CDS)problem,i.e.,sparse data separation from a few linear random *** propose the nonconvex ℓ_(q)-split analysis with ℓ_(∞)-constraint and 0analysis Dantzig selector(ℓ_(q)-split-analysis DS).We show that the two distinct subcomponents that are approximately sparse in terms of two different dictionaries could be stably approximated via the ℓ_(q)-split-analysis DS,provided that the measurement matrix satisfies either a classical D-RIP(Restricted Isometry Property with respect to Dictionaries and ℓ_(2) norm)or a relatively new(D,q)-RIP(RIP with respect to Dictionaries and ℓ_(q)-quasi norm)condition and the two different dictionaries satisfy a mutual coherence condition between *** the Gaussian random measurements,the measurement number needed for the(D,q)-RIP condition is far less than those needed for the D-RIP condition and the(D,1)-RIP condition when q is small enough.

关键词： data separation ℓ_(q)-split analysis Dantzig selector frames restricted isometry property compressed sensing

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