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Yun Yang

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24 papers

2 author rows

EAAI Journal 2026 Journal Article

A self-adaptive transformer-enhanced physics-informed neural network for railway dynamics system

Chengjia Han
Shuai Qu
Yun Yang
Maggie Y. Gao
Liwei Dong
Fan Yang
Tao Ma
Yaowen Yang

Swiftly solving complex railway dynamics systems holds paramount importance in accurately determining the vibration characteristics of key components, enabling early fault detection and timely warnings. In light of emerging digital twins for railway systems, achieving real-time or near-real-time computational efficiency under limited sensor inputs is increasingly vital. Despite being a promising technology for real-time dynamics response solving, artificial intelligence (AI) suffers from limitations in generalization capability and computational reliability, reducing its practical engineering utility. This study introduces an innovative AI-based solution framework for railway dynamics predicaments, centered around physics-informed neural networks (PINN). It outlines techniques for allocating network loss weights when encountering single or multiple physical constraint losses, thereby enhancing training efficiency post-incorporation of these constraints. Moreover, the study designs a new Self-Adaptive Transformer-Enhanced (SAT) structure and establishes the SATPINN model, a multibody dynamics solution based on the proposed PINN paradigm. The effectiveness of the proposed PINN paradigm is demonstrated through validation in two rail transit cases, showcasing SATPINN's superior performance over baseline models with the lowest mean absolute error and mean absolute percentage error in both cases.

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EAAI Journal 2026 Journal Article

AGRENET: Attributed graph rotation embedding network for clustering

Ying Xie
Junnan Shen
Zhiqiang Xu
Jixiang Wang
Lijie Wen
Rongbin Xu
Yun Yang

Recent studies leverage graph neural networks (GNNs) to learn node embeddings, subsequently employing conventional clustering techniques to identify clusters. However, these methods face several challenges. Firstly, the use of the original graph structure is not ideal for clustering as it is often plagued by noise and sparsity issues. Additionally, non-clustering driven losses are typically used, which might fail to represent the overall cluster structure. Consequently, the generated embeddings are often insufficient for the subsequent clustering task. To tackle these challenges, we introduce an innovative framework, the attributed graph rotation embedding network for clustering (AGRENET), which leverages full graph information based on the proposed dynamic global attention to improve graph structure. The proposed framework learns node embeddings through a spectral rotation embedding loss that integrates both feature and structure information into a kernel order learning via a higher-order graph convolution. Combining graph structure improvement, embedding network construction, and kernel order learning, AGRENET allows the improved graph structure to encode third-order proximities, thus reducing noise and sparsity issues. Experiments on four real-world and two synthetic attributed graph datasets demonstrate that AGRENET consistently outperforms state-of-the-art methods. Specifically, it achieves an average improvement of 11. 5% in Accuracy, 17. 3% in Normalized Mutual Information, 17. 3% in Adjusted Rand Index, and 13. 8% in F1-score over the best baseline. The results of experiments conducted on six benchmark graphs show that AGRENET surpasses cutting-edge methods with respect to clustering performance. The code is available at: https: //github. com/YingXie/AGRENET.

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AAAI Conference 2026 Conference Paper

EnViT: Enhancing the Performance of Early-Exit Vision Transformers via Exit-Aware Structured Dropout-Enabled Self-Distillation

Yonghao Dong
Qiang He
Penghong Rui
Zhenzhe Zheng
Zhao Li
Feifei Chen
Hai Jin
Yun Yang

Vision Transformers (ViTs) have gained significant attention and widespread adoption due to their impressive performance in various computer vision tasks. However, in practice, their substantial computational overhead often leads to high inference latency and increased overheads when deployed on resource-constrained edge devices like smartphones, autonomous vehicles, and robots. To address these challenges, Early Exit (EE) has emerged as a promising approach for lightweight inference on edge devices. It accelerates inference and reduces computational overhead by adaptively producing predictions through early exits based on sample complexity. Existing EE methods typically suffer from substantial accuracy decreases in late exits while providing only marginal accuracy improvements to early exits. This paper presents EnViT, an exit-aware structured dropout-enabled self-distillation approach that enhances the performance of early exits without compromising late exits. EnViT leverages structured dropout to enable self-distillation, where the full model serves as the teacher and its own virtual sub-models generated by structured dropout as students. This mechanism effectively distills knowledge from the full model to early exits and avoids performance degradation in late exits by mitigating parameter conflicts across exits during training. Evaluation on five datasets shows that our EnViT achieves accuracy improvements ranging from 0.36% to 7.92% while maintaining competitive speed-up ratios of 1.72x to 2.23x.

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ICML Conference 2025 Conference Paper

A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models

Shivam Kumar
Yun Yang
Lizhen Lin

In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. Our rates depend solely on the intrinsic dimension and smoothness of the true conditional distribution. These findings provide an explanation of why conditional deep generative models can circumvent the curse of dimensionality from the perspective of statistical foundations and demonstrate that they can learn a broader class of nearly singular conditional distributions. Our analysis also emphasizes the importance of introducing a small noise perturbation to the data when they are supported sufficiently close to a manifold. Finally, in our numerical studies, we demonstrate the effective implementation of the proposed approach using both synthetic and real-world datasets, which also provide complementary validation to our theoretical findings.

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ICLR Conference 2025 Conference Paper

Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation

Rong Tang
Lizhen Lin
Yun Yang

We consider a class of conditional forward-backward diffusion models for conditional generative modeling, that is, generating new data given a covariate (or control variable). To formally study the theoretical properties of these conditional generative models, we adopt a statistical framework of distribution regression to characterize the large sample properties of the conditional distribution estimators induced by these conditional forward-backward diffusion models. Here, the conditional distribution of data is assumed to smoothly change over the covariate. In particular, our derived convergence rate is minimax-optimal under the total variation metric within the regimes covered by the existing literature. Additionally, we extend our theory by allowing both the data and the covariate variable to potentially admit a low-dimensional manifold structure. In this scenario, we demonstrate that the conditional forward-backward diffusion model can adapt to both manifold structures, meaning that the derived estimation error bound (under the Wasserstein metric) depends only on the intrinsic dimensionalities of the data and the covariate.

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EAAI Journal 2025 Journal Article

Confidence-aware iterative training for cross-lingual entity alignment

Fan Ye
Yu Zeng
Zhangling Duan
Zhaolong Ling
Yun Yang

Entity alignment (EA) aims to identify equivalent entities across knowledge graphs, serving as a critical step in integrating multi-source knowledge graphs. In recent years, EA methods relying on alignment seeds have achieved impressive performance. However, the high cost of manually labeled alignment seeds has posed a significant limitation to their practical applications in the real world. Most methods adopt iterative strategies to generate pseudo-alignment seeds automatically. However, unreliable iterative strategies introduce a large number of noisy pseudo-alignment seeds, leading to low-quality entity embeddings. In this paper, we propose a Confidence-Aware Iterative Training (CAIT) framework for unsupervised EA tasks. The framework initially extracts semantic and structural features of entities from knowledge graphs, then generates pseudo-alignment seeds by the confidence-aware iterative strategy, which limits the quantity of noisy pseudo-alignment seeds and progressively enhances the quality of entity embeddings. Extensive experiments conducted on widely used benchmark datasets demonstrate that CAIT outperforms existing state-of-the-art methods in cross-lingual EA tasks, both with and without prior alignment seeds.

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EAAI Journal 2025 Journal Article

Joint image synthesis and fusion with converted features for Alzheimer’s disease diagnosis

Zhaodong Chen
Mingxia Wang
Fengtao Nan
Yun Yang
Shunbao Li
Menghui Zhou
Jun Qi
Hanwen Wang

The effectiveness of complete multi-modal neuroimaging data in the diagnosis of Alzheimer’s disease has been extensively demonstrated and applied. Dealing with incomplete modalities poses a common challenge in multi-modal neuroimaging diagnosis. The mainstream approaches aim to synthesize missing neuroimaging data in order to make full use of all available samples. However, these methods treat image synthesis and disease diagnosis as two independent tasks, overlooking the potential feature of cross-modality image synthesis for downstream tasks. To this end, we propose the Joint Image Synthesis and Classification Learning method to jointly optimize image synthesis and disease diagnosis using incomplete neuroimaging modalities. Our approach comprises a submodule for synthesizing missing neuroimaging data and a decision fusion submodule that integrates features from different modalities and the high-level/converted features generated during synthesis. Experimental results demonstrate that our joint optimization approach outperforms conventional two-stage methods. Our method is capable of handling arbitrary neuroimaging modality missing scenarios and achieves state-of-the-art performance in both Alzheimer’s Disease identification and mild cognitive impairment conversion classification tasks. Finally, we further explored the importance of different converted features. This highlights the effectiveness of our approach in addressing the challenges of Alzheimer’s Disease diagnosis and provides insights for future research in multi-modal medical image analysis.

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NeurIPS Conference 2025 Conference Paper

Sim-LLM: Optimizing LLM Inference at the Edge through Inter-Task KV Reuse

Ruikun Luo
Changwei Gu
Qiang He
Feifei Chen
Song Wu
Hai Jin
Yun Yang

KV cache technology, by storing key-value pairs, helps reduce the computational overhead incurred by large language models (LLMs). It facilitates their deployment on resource-constrained edge computing nodes like edge servers. However, as the complexity and size of tasks increase, KV cache usage leads to substantial GPU memory consumption. Existing research has focused on mitigating KV cache memory usage through sequence length reduction, task-specific compression, and dynamic eviction policies. However, these methods are computationally expensive for resource-constrained edge computing nodes. To tackle this challenge, this paper presents Sim-LLM, a novel inference optimization mechanism that leverages task similarity to reduce KV cache memory consumption for LLMs. By caching KVs from processed tasks and reusing them for subsequent similar tasks during inference, Sim-LLM significantly reduces memory consumption while boosting system throughput and increasing maximum batch size, all with minimal accuracy degradation. Evaluated on both A40 and A100 GPUs, Sim-LLM achieves a system throughput improvement of up to 39. 40\% and a memory reduction of up to 34. 65%, compared to state-of-the-art approaches. Our source code is available at https: //github. com/CGCL-codes/SimLLM.

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ICML Conference 2025 Conference Paper

Testing Conditional Mean Independence Using Generative Neural Networks

Yi Zhang
Linjun Huang
Yun Yang
Xiaofeng Shao

Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel population CMI measure and a bootstrap-based testing procedure that utilizes deep generative neural networks to estimate the conditional mean functions involved in the population measure. The test statistic is thoughtfully constructed to ensure that even slowly decaying nonparametric estimation errors do not affect the asymptotic accuracy of the test. Our approach demonstrates strong empirical performance in scenarios with high-dimensional covariates and response variable, can handle multivariate responses, and maintains nontrivial power against local alternatives outside an $n^{-1/2}$ neighborhood of the null hypothesis. We also use numerical simulations and real-world imaging data applications to highlight the efficacy and versatility of our testing procedure.

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JMLR Journal 2024 Journal Article

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling

Rong Tang
Yun Yang

In this paper, we examine the computational complexity of sampling from a Bayesian posterior (or pseudo-posterior) using the Metropolis-adjusted Langevin algorithm (MALA). MALA first employs a discrete-time Langevin SDE to propose a new state, and then adjusts the proposed state using Metropolis-Hastings rejection. Most existing theoretical analyses of MALA rely on the smoothness and strong log-concavity properties of the target distribution, which are often lacking in practical Bayesian problems. Our analysis hinges on statistical large sample theory, which constrains the deviation of the Bayesian posterior from being smooth and log-concave in a very specific way. In particular, we introduce a new technique for bounding the mixing time of a Markov chain with a continuous state space via the $s$-conductance profile, offering improvements over existing techniques in several aspects. By employing this new technique, we establish the optimal parameter dimension dependence of $d^{1/3}$ and condition number dependence of $\kappa$ in the non-asymptotic mixing time upper bound for MALA after the burn-in period, under a standard Bayesian setting where the target posterior distribution is close to a $d$-dimensional Gaussian distribution with a covariance matrix having a condition number $\kappa$. We also prove a matching mixing time lower bound for sampling from a multivariate Gaussian via MALA to complement the upper bound. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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ICLR Conference 2024 Conference Paper

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming

Yubo Zhuang
Xiaohui Chen
Yun Yang
Richard Y. Zhang 0001

$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Recently, semidefinite programming (SDP) relaxations have been proposed for solving the $K$-means optimization problem, which enjoy strong statistical optimality guarantees. However, the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. In contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm widely used by machine learning practitioners, but it lacks a solid statistical underpinning and theoretical guarantees. In this paper, we consider an NMF-like algorithm that solves a nonnegative low-rank restriction of the SDP-relaxed $K$-means formulation using a nonconvex Burer--Monteiro factorization approach. The resulting algorithm is as simple and scalable as state-of-the-art NMF algorithms while also enjoying the same strong statistical optimality guarantees as the SDP. In our experiments, we observe that our algorithm achieves significantly smaller mis-clustering errors compared to the existing state-of-the-art while maintaining scalability.

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JMLR Journal 2024 Journal Article

Wasserstein Proximal Coordinate Gradient Algorithms

Rentian Yao
Xiaohui Chen
Yun Yang

Motivated by approximation Bayesian computation using mean-field variational approximation and the computation of equilibrium in multi-species systems with cross-interaction, this paper investigates the composite geodesically convex optimization problem over multiple distributions. The objective functional under consideration is composed of a convex potential energy on a product of Wasserstein spaces and a sum of convex self-interaction and internal energies associated with each distribution. To efficiently solve this problem, we introduce the Wasserstein Proximal Coordinate Gradient (WPCG) algorithms with parallel, sequential, and random update schemes. Under a quadratic growth (QG) condition that is weaker than the usual strong convexity requirement on the objective functional, we show that WPCG converges exponentially fast to the unique global optimum. In the absence of the QG condition, WPCG is still demonstrated to converge to the global optimal solution, albeit at a slower polynomial rate. Numerical results for both motivating examples are consistent with our theoretical findings. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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ICML Conference 2023 Conference Paper

A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening

Yifan Chen 0004
Rentian Yao
Yun Yang
Jie Chen

Graph coarsening is a technique for solving large-scale graph problems by working on a smaller version of the original graph, and possibly interpolating the results back to the original graph. It has a long history in scientific computing and has recently gained popularity in machine learning, particularly in methods that preserve the graph spectrum. This work studies graph coarsening from a different perspective, developing a theory for preserving graph distances and proposing a method to achieve this. The geometric approach is useful when working with a collection of graphs, such as in graph classification and regression. In this study, we consider a graph as an element on a metric space equipped with the Gromov–Wasserstein (GW) distance, and bound the difference between the distance of two graphs and their coarsened versions. Minimizing this difference can be done using the popular weighted kernel $K$-means method, which improves existing spectrum-preserving methods with the proper choice of the kernel. The study includes a set of experiments to support the theory and method, including approximating the GW distance, preserving the graph spectrum, classifying graphs using spectral information, and performing regression using graph convolutional networks. Code is available at https: //github. com/ychen-stat-ml/GW-Graph-Coarsening.

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TMLR Journal 2023 Journal Article

Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks

Yifan Chen
Tianning Xu
Dilek Hakkani-Tur
Di Jin
Yun Yang
Ruoqing Zhu

Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer. This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement. To address these issues, we accordingly propose two remedies: a new principle for constructing sampling probabilities and an efficient debiasing algorithm. The improvements are demonstrated by extensive analyses of estimation variance and experiments on common benchmarks. Code and algorithm implementations are publicly available at \url{https://github.com/ychen-stat-ml/GCN-layer-wise-sampling}.

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JMLR Journal 2023 Journal Article

Gaussian Processes with Errors in Variables: Theory and Computation

Shuang Zhou
Debdeep Pati
Tianying Wang
Yun Yang
Raymond J. Carroll

Covariate measurement error in nonparametric regression is a common problem in nutritional epidemiology and geostatistics, and other fields. Over the last two decades, this problem has received substantial attention in the frequentist literature. Bayesian approaches for handling measurement error have only been explored recently and are surprisingly successful, although there still is a lack of a proper theoretical justification regarding the asymptotic performance of the estimators. By specifying a Gaussian process prior on the regression function and a Dirichlet process Gaussian mixture prior on the unknown distribution of the unobserved covariates, we show that the posterior distribution of the regression function and the unknown covariate density attain optimal rates of contraction adaptively over a range of Holder classes, up to logarithmic terms. We also develop a novel surrogate prior for approximating the Gaussian process prior that leads to efficient computation and preserves the covariance structure, thereby facilitating easy prior elicitation. We demonstrate the empirical performance of our approach and compare it with competitors in a wide range of simulation experiments and a real data example. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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ICML Conference 2023 Conference Paper

Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data

Yubo Zhuang
Xiaohui Chen
Yun Yang

Clustering is a widely deployed unsupervised learning tool. Model-based clustering is a flexible framework to tackle data heterogeneity when the clusters have different shapes. Likelihood-based inference for mixture distributions often involves non-convex and high-dimensional objective functions, imposing difficult computational and statistical challenges. The classic expectation-maximization (EM) algorithm is a computationally thrifty iterative method that maximizes a surrogate function minorizing the log-likelihood of observed data in each iteration, which however suffers from bad local maxima even in the special case of the standard Gaussian mixture model with common isotropic covariance matrices. On the other hand, recent studies reveal that the unique global solution of a semidefinite programming (SDP) relaxed $K$-means achieves the information-theoretically sharp threshold for perfectly recovering the cluster labels under the standard Gaussian mixture model. In this paper, we extend the SDP approach to a general setting by integrating cluster labels as model parameters and propose an iterative likelihood adjusted SDP (iLA-SDP) method that directly maximizes the exact observed likelihood in the presence of data heterogeneity. By lifting the cluster assignment to group-specific membership matrices, iLA-SDP avoids centroids estimation – a key feature that allows exact recovery under well-separateness of centroids without being trapped by their adversarial configurations. Thus iLA-SDP is less sensitive than EM to initialization and more stable on high-dimensional data. Our numeric experiments demonstrate that iLA-SDP can achieve lower mis-clustering errors over several widely used clustering methods including $K$-means, SDP and EM algorithms.

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AAAI Conference 2023 Conference Paper

Robust Temporal Smoothness in Multi-Task Learning

Menghui Zhou
Yu Zhang
Yun Yang
Tong Liu
Po Yang

Multi-task learning models based on temporal smoothness assumption, in which each time point of a sequence of time points concerns a task of prediction, assume the adjacent tasks are similar to each other. However, the effect of outliers is not taken into account. In this paper, we show that even only one outlier task will destroy the performance of the entire model. To solve this problem, we propose two Robust Temporal Smoothness (RoTS) frameworks. Compared with the existing models based on temporal relation, our methods not only chase the temporal smoothness information but identify outlier tasks, however, without increasing the computational complexity. Detailed theoretical analyses are presented to evaluate the performance of our methods. Experimental results on synthetic and real-life datasets demonstrate the effectiveness of our frameworks. We also discuss several potential specific applications and extensions of our RoTS frameworks.

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NeurIPS Conference 2022 Conference Paper

Wasserstein $K$-means for clustering probability distributions

Yubo Zhuang
Xiaohui Chen
Yun Yang

Clustering is an important exploratory data analysis technique to group objects based on their similarity. The widely used $K$-means clustering method relies on some notion of distance to partition data into a fewer number of groups. In the Euclidean space, centroid-based and distance-based formulations of the $K$-means are equivalent. In modern machine learning applications, data often arise as probability distributions and a natural generalization to handle measure-valued data is to use the optimal transport metric. Due to non-negative Alexandrov curvature of the Wasserstein space, barycenters suffer from regularity and non-robustness issues. The peculiar behaviors of Wasserstein barycenters may make the centroid-based formulation fail to represent the within-cluster data points, while the more direct distance-based $K$-means approach and its semidefinite program (SDP) relaxation are capable of recovering the true cluster labels. In the special case of clustering Gaussian distributions, we show that the SDP relaxed Wasserstein $K$-means can achieve exact recovery given the clusters are well-separated under the $2$-Wasserstein metric. Our simulation and real data examples also demonstrate that distance-based $K$-means can achieve better classification performance over the standard centroid-based $K$-means for clustering probability distributions and images.

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AIIM Journal 2021 Journal Article

MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction

Jin Li
Tao Liu
Jingru Wang
Qing Li
Chenxi Ning
Yun Yang

Predicting the associations between microRNAs (miRNAs) and diseases is of great significance for identifying miRNAs related to human diseases. Since it is time-consuming and costly to identify the association between miRNA and disease through biological experiments, computational methods are currently used as an effective supplement to identify the potential association between disease and miRNA. This paper presents a Multi-view Kernel Fusion Network (MvKFN) based prediction method (MvKFN-MDA) to address the problem of miRNA-disease associations prediction. A novel multiple kernel fusion framework Multi-view Kernel Fusion Network (MvKFN) is first proposed to effectively fuse different views similarity kernels constructed from different data sources in a highly nonlinear way. Using MvKFNs, both different base similarity kernels for miRNA, such as sequence, functional, semantic, Gaussian profile kernels and different base similarity kernels for diseases, such as semantic, Gaussian profile kernel are nonlinearly fused into two integrated similarity kernels, one for miRNA, another for disease. Then, miRNA and disease feature representations are extracted from the miRNA and disease integrated similarity kernels respectively. These features are then fed into a neural matrix completion framework which finally outputs the association prediction scores. The parameters of MvKFN-MDA are learned based on the known miRNA-disease association matrix in a supervised end-to-end way. We compare the proposed method with other state-of-the-art methods. The AUCs of our proposed method were superior to the existing methods in both 5-FCV and LOOCV on two open experimental datasets. Furthermore, 49, 48, and 47 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, are verified respectively using experimental literature. Finally, 100% accuracy from the top 50 predicted miRNAs is achieved when breast cancer is used as a case study to evaluate the ability of MvKFN-MDA for predicting a new disease without any known related miRNAs.

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NeurIPS Conference 2021 Conference Paper

Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method

Yifan Chen
Qi Zeng
Heng Ji
Yun Yang

Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism. On the other hand, although kernel machines suffer from the same computation bottleneck in pairwise dot products, several approximation schemes have been successfully incorporated to considerably reduce their computational cost without sacrificing too much accuracy. In this work, we leverage the computation methods for kernel machines to alleviate the high computational cost and introduce Skyformer, which replaces the softmax structure with a Gaussian kernel to stabilize the model training and adapts the Nyström method to a non-positive semidefinite matrix to accelerate the computation. We further conduct theoretical analysis by showing that the matrix approximation error of our proposed method is small in the spectral norm. Experiments on Long Range Arena benchmark show that the proposed method is sufficient in getting comparable or even better performance than the full self-attention while requiring fewer computation resources.

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TIST Journal 2019 Journal Article

Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data

Po Yang
Jing Liu
Jun Qi
Yun Yang
Xulong Wang
Zhihan Lv

As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.

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IJCAI Conference 2019 Conference Paper

Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model

Chunyang Ruan
Jiangang Ma
Ye Wang
Yanchun Zhang
Yun Yang

Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.

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JBHI Journal 2018 Journal Article

User Profiling in Elderly Healthcare Services in China: Scalper Detection

Cheng Xie
Hongming Cai
Yun Yang
Lihong Jiang
Po Yang

Driven by the automation technologies and health informatics of Industry 4. 0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.

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TAAS Journal 2007 Journal Article

Adaptive selection of necessary and sufficient checkpoints for dynamic verification of temporal constraints in grid workflow systems

Jinjun Chen
Yun Yang

In grid workflow systems, a checkpoint selection strategy is responsible for selecting checkpoints for conducting temporal verification at the runtime execution stage. Existing representative checkpoint selection strategies often select some unnecessary checkpoints and omit some necessary ones because they cannot adapt to the dynamics and uncertainty of runtime activity completion duration. In this article, based on the dynamics and uncertainty of runtime activity completion duration, we develop a novel checkpoint selection strategy that can adaptively select not only necessary, but also sufficient checkpoints. Specifically, we introduce a new concept of minimum time redundancy as a key reference parameter for checkpoint selection. An important feature of minimum time redundancy is that it can adapt to the dynamics and uncertainty of runtime activity completion duration. We develop a method on how to achieve minimum time redundancy dynamically along grid workflow execution and investigate its relationships with temporal consistency. Based on the method and the relationships, we present our strategy and rigorously prove its necessity and sufficiency. The simulation evaluation further demonstrates experimentally such necessity and sufficiency and its significant improvement on checkpoint selection over other representative strategies.

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