Jun Guo

JBHI Journal 2026 Journal Article

ECG–MTDA: A Framework for Morphology–Temporal Decoupling and LLM–Guided Text Alignment

• Huimin Zheng
• Jun Guo
• Xin Zhang
• Xiaofen Xing
• Xiangmin Xu

Effective automated electrocardiogram (ECG) interpretation hinges on disentangling waveform morphology from rhythm dynamics, a challenge for existing multimodal models that often conflate these heterogeneous attributes and introduce semantic ambiguity. We introduce ECG-MTDA, a framework that explicitly decouples these components. It learns morphology-oriented representations via a PQRST-guided masked autoencoder, while separately modeling temporal dynamics using continuous wavelet transform. Crucially, we align the learned morphology with concise, label-conditioned textual descriptions generated by a large language model (LLM) using a contrastive objective, creating a semantically grounded embedding space. ECG-MTDA demonstrates superior performance on the PTB-XL and CPSC 2018 benchmarks (e. g. , AUC 93. 16 on PTB-XL Superclass), with statistically significant gains over a strong multimodal baseline. Furthermore, on a challenging in-house cohort (n=620) for short-term paroxysmal atrial fibrillation (pAF) progression prediction, the model achieves an AUC of 0. 97 $\pm$ 0. 02 with high sensitivity (0. 80 $\pm$ 0. 04) and specificity (0. 98 $\pm$ 0. 01). Ablation studies and qualitative analyses confirm the benefits of our decoupled design and morphology-text alignment. Our results demonstrate that this clinically-inspired decoupling strategy yields more precise and robust multimodal representations for complex ECG analysis, enhancing both diagnostic classification and near-term risk stratification.

EAAI Journal 2025 Journal Article

A prior segmentation knowledge enhanced deep learning system for the classification of tumors in ultrasound image

• Tao Jiang
• Jun Guo
• Wenyu Xing
• Ming Yu
• Yifang Li
• Bo Zhang
• Yi Dong
• Dean Ta

Breast and thyroid cancers are prevalent among women worldwide. Ultrasound (US) examination is widely used for the early detection of breast and thyroid cancers. However, due to the blurred tumor boundaries and irregular shapes, the computer-aided diagnosis (CAD) of tumors based on US is challenging. Numerous studies have introduced deep learning-based multi-task learning approaches to address this issue, but these methods may result in feature redundancy and misinformation. Tumor segmentation is a prerequisite step for US CAD, and a higher Dice coefficient is associated with more accurate classification outcomes. Therefore, this paper introduces a novel deep-learning system that fully utilizes segmentation knowledge to boost classification performance. The system starts with a hybrid convolutional neural network (CNN)-Transformer for tumor localization and coarse segmentation, then uses a lightweight CNN-based U-Net to refine segmentation results. Subsequently, segmentation knowledge is harnessed to augment the network input and enhance multimodal feature extraction, resulting in improved classification performance. Our proposed method yielded a Dice coefficient of 83. 62% and 77. 20% for breast and thyroid tumor segmentation and area under curve (AUC) values of 0. 9536 and 0. 9475 for their respective classifications. Compared to non-segmentation knowledge-based classification models, our method obtained an increase in AUC of 0. 1054 and 0. 0566 on the breast and thyroid datasets, respectively. It outperformed the performance of the State-Of-The-Art (SOTA) methods across various datasets. In summary, our proposed system shows promise for application in US tumor analysis and holds potential to be extended to additional diseases and modalities.

EAAI Journal 2025 Journal Article

An integrated exergy efficiency and machine learning method for optimizing organic solid waste gasification process

• Wenni Chen
• Xianan Xiang
• Sha Liu
• Jun Guo
• Tao Li
• Xuehua Zhou
• Deyong Peng
• Zhiya Deng

Organic solid waste (OSW) gasification is a critical pathway toward sustainable energy utilization. This study develops an integrated prediction model by combining exergy efficiency-based analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) with machine learning techniques. The model aims to select the optimal gasifier type and operational parameters based on OSW characteristics and processing capacities. Exergy efficiency derived from experimental data is used to construct AHP-FCE scores, which are then predicted using eight machine learning algorithms. Gradient boosting decision tree (GBDT) achieves the best performance. The prediction model is applied to three practical cases. For a project with an annual processing capacity of 2000 tons of refuse-derived fuel (RDF), the model consistently recommends the downdraft fixed-bed gasifier (DBG). In a corn straw gasification project processing 11, 000 tons per year, the bubbling fluidized-bed gasifier (BBG) is identified as the optimal choice. For a bamboo chip gasification project with an annual capacity of 150, 000 tons, the model suggests using the circulating fluidized-bed gasifier (CFBG) for reduction objectives and the dual fluidized-bed gasifier (DFBG) for hydrogen production goals. Additionally, the model shows significant potential. It can also be applied to optimize other complex systems that require balancing multiple influencing factors.

NeurIPS Conference 2025 Conference Paper

Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning

• Simin Li
• Zihao Mao
• Hanxiao Li
• Zonglei Jing
• Zhuohang bian
• Jun Guo
• Li Wang
• Zhuoran Han

In cooperative Multi-Agent Reinforcement Learning (MARL), it is a common practice to tune hyperparameters in ideal simulated environments to maximize cooperative performance. However, policies tuned for cooperation often fail to maintain robustness and resilience under real-world uncertainties. Building trustworthy MARL systems requires a deep understanding of \emph{robustness}, which ensures stability under uncertainties, and \emph{resilience}, the ability to recover from disruptions—a concept extensively studied in control systems but largely overlooked in MARL. In this paper, we present a large-scale empirical study comprising over 82, 620 experiments to evaluate cooperation, robustness, and resilience in MARL across 4 real-world environments, 13 uncertainty types, and 15 hyperparameters. Our key findings are: (1) Under mild uncertainty, optimizing cooperation improves robustness and resilience, but this link weakens as perturbations intensify. Robustness and resilience also varies by algorithm and uncertainty type. (2) Robustness and resilience do not generalize across uncertainty modalities or agent scopes: policies robust to action noise for all agents may fail under observation noise on a single agent. (3) Hyperparameter tuning is critical for trustworthy MARL: surprisingly, standard practices like parameter sharing, GAE, and PopArt can hurt robustness, while early stopping, high critic learning rates, and Leaky ReLU consistently help. By optimizing hyperparameters only, we observe substantial improvement in cooperation, robustness and resilience across all MARL backbones, with the phenomenon also generalizing to robust MARL methods across these backbones.

EAAI Journal 2025 Journal Article

Prediction and evaluation of key parameters in coalbed methane pre-extraction based on transformer and inversion model

• Li Yan
• Hu Wen
• Zhenping Wang
• Yongfei Jin
• Jun Guo
• Yin Liu
• Shixing Fan

Accurate parameter prediction in the coalbed methane (CBM) pre-extraction process is crucial for formulating effective control measures and preventing CBM-related accidents. Traditional prediction methods rely on feature extraction or complex physical model parameter calculations, which require extensive manual intervention and have limited practical applicability. Additionally, simple neural network methods are prone to overfitting and gradient vanishing when handling parameters, and they lack the capability to dynamically monitor gas pressure during extraction, leading to inefficient and blind extraction operations. This study proposes a CBM pre-extraction parameter and completion time prediction method based on the Transformer model. By integrating autoregressive models and wavelet denoising techniques, the approach effectively captures temporal features and long-term dependencies in CBM data. Experimental results demonstrate that the proposed model outperforms traditional methods in short-, medium-, and long-term predictions, with a median R2 value of 0. 99072, and 76% of the training results exceeding 0. 9. Furthermore, a CBM pressure inversion model was developed, combining dimensional analysis and physical similarity principles with the Transformer model, enabling the dynamic detection of high- and low-pressure regions in coal seams. In single borehole compliance time predictions, the median compliance time for the first stage is 4 days, with an average of 49 days and a maximum of 277 days, providing adjustment guidance for boreholes with extended compliance times. The proposed model significantly improves prediction accuracy and stability, offering critical support for developing scientifically sustainable pre-extraction plans and advancing intelligent CBM management.

EAAI Journal 2025 Journal Article

Probability forecasting for multivariate urban water demand using temporal convolutional network based on quantile regression and Parzen window

• Jun Guo
• Qingya Meng
• Baigang Du
• Hui Sun

Probability prediction can provide more abundant information about uncertainties in future water demand, which is gaining increasing attention in building economical and reliable water resource management plans. However, most existing literature on water demand prediction focus on provide deterministic point prediction results. To overcome this problem, a novel hybrid probability forecasting model based on quantile regression temporal convolutional network and Parzen window is proposed for the probability density forecast of multivariate urban water demand. Firstly, to address the complex coupling relationship between water demand and multiple influencing factors, a random forest-based feature selection method is employed to eliminate the redundant variables. Then, a discrete wavelet transform is deployed to decompose the original series into a variety of characteristic subseries to reduce fluctuations of the original water demand series. Secondly, a quantile regression-based temporal convolutional neural network is employed to obtain the conditional quantiles of future water demand. Moreover, a probability density prediction method based on Parzen window estimation is developed to further obtain the distribution information of prediction uncertainty. Finally, a real-world multivariate dataset from a water plant in Suzhou, China, is used for comparison experiments with state-of-the-art models. The comparison results show that the proposed model has achieved an average improvement of 15. 4 % and 53. 3 % in interval prediction and probability density prediction, respectively. It shows that the proposed model is a reliable prediction model that can assist policymakers to optimize the management of urban water demand.

YNICL Journal 2025 Journal Article

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke

• Hongchuan Zhang
• Jun Guo
• Jingchun Liu
• Caihong Wang
• Hao Ding
• Tong Han
• Jingliang Cheng
• Chunshui Yu

Patients with chronic subcortical stroke undergo regional and network morphometric reorganizations beyond the lesion site, but the interplay between network and regional reorganization remains poorly understood. We aimed to clarify the reorganization patterns of the individualized differential structural covariance networks (IDSCN) in chronic subcortical stroke and investigate their associations with regional gray matter volume (GMV) changes and functional recovery. Structural MRI from four datasets enrolled 112 patients with chronic subcortical stroke (81 male, age: 55.82 ± 7.79) and 122 matched healthy controls (HC) (74 male; age: 55.28 ± 7.54). Network-based statistics were employed to identify aberrant IDSCN, Spearman correlation was conducted to assess the association between IDSCN and regional GMV alterations, and partial correlation was utilized to investigate the association between abnormal IDSCN and functional recovery. We identified 133 connections with balanced increased and decreased IDSCN. Aberrant IDSCN involved more regions than local GMV alterations, local GMV alteration exhibited intricate correlations with IDSCN, which could explain partly IDSCN reorganization (p < 0.05, corrected). Finally, abnormal IDSCN showed a weak association with long-term clinical recovery (p < 0.01). These findings reinforce the theory of adaptive network reorganization post-stroke and suggest that IDSCN may provide further insights into cortical reorganization and functional rehabilitation beyond regional morphometric measures.

EAAI Journal 2024 Journal Article

A hybrid estimation of distribution algorithm for solving assembly flexible job shop scheduling in a distributed environment

• Baigang Du
• Shuai Han
• Jun Guo
• Yibing Li

This paper proposes a novel distributed assembly flexible job shop scheduling problem (DAFJSP), which involves three stages: production stage, assembly stage, and delivery stage. The production stage is accomplished in a few flexible job shops, the assembly stage is accomplished in a few single-machine factories, and the delivery stage is to deliver the obtained products to the corresponding customers. To address the problem, a hybrid estimation of distribution algorithm based on differential evolution operator and variable neighborhood search (HEDA-DEV) is proposed with the goal of minimizing the total cost and tardiness. Firstly, a new multidimensional coding method is designed based on the features of the DAFJSP. Secondly, two mutation operators and the similarity coefficient based on the probability matrix are put forward to implement the dynamic mutation. Thirdly, five types of neighborhood structures satisfying cooperative search strategies are employed to adequately improve the local exploitation ability. Finally, the comparison experiment results suggest that the proposed HEDA-DEV has competitive performance compared to the selected efficient algorithms. Moreover, a real case study is used to demonstrate that HEDA-DEV is an effective method for solving DAFJSP.

AIIM Journal 2024 Journal Article

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

• Yue Zhang
• Jiewei Lai
• Chenyu Zhao
• Jinliang Wang
• Yong Yan
• Mingyang Chen
• Lei Ji
• Jun Guo

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4, 913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94. 97% and 94. 27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

NeurIPS Conference 2024 Conference Paper

Animal-Bench: Benchmarking Multimodal Video Models for Animal-centric Video Understanding

• Yinuo Jing
• Ruxu Zhang
• Kongming Liang
• Yongxiang Li
• Zhongjiang He
• Zhanyu Ma
• Jun Guo

With the emergence of large pre-trained multimodal video models, multiple benchmarks have been proposed to evaluate model capabilities. However, most of the benchmarks are human-centric, with evaluation data and tasks centered around human applications. Animals are an integral part of the natural world, and animal-centric video understanding is crucial for animal welfare and conservation efforts. Yet, existing benchmarks overlook evaluations focused on animals, limiting the application of the models. To address this limitation, our work established an animal-centric benchmark, namely Animal-Bench, to allow for a comprehensive evaluation of model capabilities in real-world contexts, overcoming agent-bias in previous benchmarks. Animal-Bench includes 13 tasks encompassing both common tasks shared with humans and special tasks relevant to animal conservation, spanning 7 major animal categories and 819 species, comprising a total of 41, 839 data entries. To generate this benchmark, we defined a task system centered on animals and proposed an automated pipeline for animal-centric data processing. To further validate the robustness of models against real-world challenges, we utilized a video editing approach to simulate realistic scenarios like weather changes and shooting parameters due to animal movements. We evaluated 8 current multimodal video models on our benchmark and found considerable room for improvement. We hope our work provides insights for the community and opens up new avenues for research in multimodal video models. Our data and code will be released at https: //github. com/PRIS-CV/Animal-Bench.

YNICL Journal 2024 Journal Article

Cortical structure reorganization and correlation with attention deficit in subcortical stroke: An underlying pattern analysis

• Jingchun Liu
• Caihong Wang
• Yujie Zhang
• Jun Guo
• Peifang Miao
• Ying Wei

BACKGROUND: Subcortical stroke may significantly alter the cerebral cortical structure and affect attention function, but the details of this process remain unclear. The study aimed to investigate the neural substrates underlying attention impairment in patients with subcortical stroke. MATERIALS AND METHODS: In this prospective observational study, two distinct datasets were acquired to identify imaging biomarkers underlying attention deficit. The first dataset consisted of 86 patients with subcortical stroke, providing a cross-sectional perspective, whereas the second comprised 108 patients with stroke, offering longitudinal insights. All statistical analyses were subjected to false discovery rate correction upon P < 0.05. RESULTS: In the chronic-stage data, the stroke group exhibited significantly poorer attention function compared with that of the control group. The cortical structure analysis showed that patients with stroke exhibited decreased cortical thickness of the precentral gyrus and surface area of the cuneus, along with an increase in various frontal, occipital, and parietal cortices regions. The declined attention function positively correlated with the superior frontal gyrus cortical thickness and supramarginal gyrus surface area. In the longitudinal dataset, patients with stroke showed gradually increasing cortical thickness and surface area within regions of obvious structural reorganization. Furthermore, deficient attention positively correlated with supramarginal gyrus surface area both at the subacute and chronic stages post-stroke. CONCLUSIONS: Subcortical stroke can elicit dynamic reorganization of cortical areas associated with attention impairment. Moreover, the altered surface area of the supramarginal gyrus is a potential neuroimaging biomarker for attention deficits.

AAAI Conference 2024 Conference Paper

Dual-Prior Augmented Decoding Network for Long Tail Distribution in HOI Detection

• Jiayi Gao
• Kongming Liang
• Tao Wei
• Wei Chen
• Zhanyu Ma
• Jun Guo

Human object interaction detection aims at localizing human-object pairs and recognizing their interactions. Trapped by the long-tailed distribution of the data, existing HOI detection methods often have difficulty recognizing the tail categories. Many approaches try to improve the recognition of HOI tasks by utilizing external knowledge (e.g. pre-trained visual-language models). However, these approaches mainly utilize external knowledge at the HOI combination level and achieve limited improvement in the tail categories. In this paper, we propose a dual-prior augmented decoding network by decomposing the HOI task into two sub-tasks: human-object pair detection and interaction recognition. For each subtask, we leverage external knowledge to enhance the model's ability at a finer granularity. Specifically, we acquire the prior candidates from an external classifier and embed them to assist the subsequent decoding process. Thus, the long-tail problem is mitigated from a coarse-to-fine level with the corresponding external knowledge. Our approach outperforms existing state-of-the-art models in various settings and significantly boosts the performance on the tail HOI categories. The source code is available at https://github.com/PRIS-CV/DP-ADN.

EAAI Journal 2024 Journal Article

MHT: A multiscale hourglass-transformer for remaining useful life prediction of aircraft engine

• Jun Guo
• Shicheng Lei
• Baigang Du

Remaining useful life (RUL) prediction of aircraft engines is significant in the health monitoring, operation, and maintenance of aircraft. Capturing more comprehensive device degradation trends at different time scales and extracting long-term dependencies effectively among elements in long time series are two challenges in the field of aircraft engine RUL estimation. To address the aforementioned challenges, this paper proposes a novel multiscale Hourglass-Transformer (MHT) aircraft engine RUL prognostics. Specifically, an hourglass-shaped multiscale feature extractor (HME) is designed based on one-dimensional convolutional neural network, which can scale the time sequence into multi-time scales for feature fusion. Then, a transformer network is employed to further extract features from the fused feature map and output the RUL. To enhance inter-scale data attention, a pyramid self-attention mechanism is employed in both the encoder and decoder. Finally, the superiority and effectiveness of this approach are verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Furthermore, the robustness and generalization capability of this method are further validated on New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset.

AAAI Conference 2023 Conference Paper

Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

• Jijie Wu
• Dongliang Chang
• Aneeshan Sain
• Xiaoxu Li
• Zhanyu Ma
• Jie Cao
• Jun Guo
• Yi-Zhe Song

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used fine-grained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.

AAAI Conference 2020 Conference Paper

Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data

• Jun Guo
• Heng Chang
• Wenwu Zhu

To better pre-process unlabeled data, most existing feature selection methods remove redundant and noisy information by exploring some intrinsic structures embedded in samples. However, these unsupervised studies focus too much on the relations among samples, totally neglecting the feature-level geometric information. This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering. In the feature selection part, we design an ordinal consensus preserving term based on a triplet-induced graph. This term enforces the projection vectors to preserve the relative proximity of original features, which contributes to selecting more relevant features. In the clustering part, Self-Paced Learning (SPL) is introduced to gradually learn from ‘easy’ to ‘complex’ samples. SPL alleviates the dilemma of falling into the bad local minima incurred by noise and outliers. Specifically, we propose a compelling regularizer for SPL to obtain a robust loss. Finally, an alternating minimization algorithm is developed to efficiently optimize the proposed model. Extensive experiments on different benchmark datasets consistently demonstrate the superiority of our proposed method.

AIIM Journal 2020 Journal Article

Retraction notice to “Diagnosis Labeling with Disease-Specific Characteristics Mining” [Artif. Intell. Med. 90 (2018) 25–33]

• Jun Guo
• Xuan Yuan
• Xia Zheng
• Xu Pengfei
• Yun Xiao
• Baoying Liu

AAAI Conference 2019 Conference Paper

A Human-Like Semantic Cognition Network for Aspect-Level Sentiment Classification

• Zeyang Lei
• Yujiu Yang
• Min Yang
• Wei Zhao
• Jun Guo
• Yi Liu

In this paper, we propose a novel Human-like Semantic Cognition Network (HSCN) for aspect-level sentiment classification, motivated by the principles of human beings’ reading cognitive process (pre-reading, active reading, post-reading). We first design a word-level interactive perception module to capture the correlation between context words and the given target words, which can be regarded as pre-reading. Second, to mimic the process of active reading, we propose a targetaware semantic distillation module to produce the targetspecific context representation for aspect-level sentiment prediction. Third, we further devise a semantic deviation metric module to measure the semantic deviation between the targetspecific context representation and the given target, which evaluates the degree we understand the target-specific context semantics. The measured semantic deviation is then used to fine-tune the above active reading process in a feedback regulation way. To verify the effectiveness of our approach, we conduct extensive experiments on three widely used datasets. The experiments demonstrate that HSCN achieves impressive results compared to other strong competitors.

AAAI Conference 2019 Conference Paper

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

• Jun Guo
• Jiahui Ye

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

YNICL Journal 2019 Journal Article

Differential involvement of rubral branches in chronic capsular and pontine stroke

• Jun Guo
• Jingchun Liu
• Caihong Wang
• Chen Cao
• Lejun Fu
• Tong Han
• Jingliang Cheng
• Chunshui Yu

Background and Purpose Early studies have indicated that the cortico-rubro-spinal tracts play important roles in motor dysfunction after stroke. However, the differential involvement of the rubral branches in capsular and pontine stroke, and their associations with the motor impairment are still unknown. Methods The present study recruited 144 chronic stroke patients and 91 normal controls (NC) from three hospitals, including 102 cases with capsular stroke (CS) and 42 cases with pontine stroke (PS). The rubral branches, including bilateral corticorubral tracts (CRT), dentatorubral tracts (DRT), and rubrospinal tracts (RST), and the cortico-spinal tract (CST) were reconstructed based on the dataset of the Human Connectome Project. Group differences in diffusion scalars of each rubral branch were compared, and the associations between the diffusion measures of rubral branches and the Fugl-Meyer assessment (FMA) scores were tested. Results The bilateral CRT of the CS cases showed significantly lower factional anisotropy (FA) than in the NC. The bilateral DRT of the PS cases had lower FA than in the NC. Both CS and PS cases had significantly lower FA of the bilateral RST than the NC. Besides, the stroke patients demonstrated significantly lower FA in bilateral CSTs than the NC. Partial correlation analysis identified significantly positive correlations between the FA of the ipsilesional and CRT and the FMA scores in the CS group, and significantly positive correlations between the FA of the RST bilaterally and the FMA scores in the CS and PS groups. Furthermore, the association between RST integrity and FMA scores still survived after controlling for the effect of the CST. Finally, multiple regression modelling found that rubral tract FA explained 39. 2% of the variance in FMA scores for CS patients, and 48. 8% of the variance in FMA scores for PS patients. Conclusions The bilateral rubral branches were differentially involved in the chronic capsular and pontine stroke, and the impairment severity of each rubral branch was dependent on lesion locations. The integrity of the rubral branches is related to motor impairment in both the chronic capsular and pontine stroke.

AAAI Conference 2018 Conference Paper

Dependence Guided Unsupervised Feature Selection

• Jun Guo
• Wenwu Zhu

In the past decade, various sparse learning based unsupervised feature selection methods have been developed. However, most existing studies adopt a two-step strategy. i. e. , selecting the top-m features according to a calculated descending order and then performing K-means clustering, resulting in a group of sub-optimal features. To address this problem, we propose a Dependence Guided Unsupervised Feature Selection (DGUFS) method to select features and partition data in a joint manner. Our proposed method enhances the interdependence among original data, cluster labels, and selected features. In particular, a projection-free feature selection model is proposed based on l2, 0-norm equality constraints. We utilize the learned cluster labels to fill in the information gap between original data and selected features. Two dependence guided terms are consequently proposed for our model. More specifically, one term increases the dependence of desired cluster labels on original data, while the other term maximizes the dependence of selected features on cluster labels to guide the process of feature selection. Last but not least, an iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the constrained minimization problem efficiently. Extensive experiments on different datasets consistently demonstrate that our proposed method significantly outperforms state-of-the-art baselines.

AAAI Conference 2018 Conference Paper

Partial Multi-View Outlier Detection Based on Collective Learning

• Jun Guo
• Wenwu Zhu

In the past decade, various multi-view outlier detection methods have been designed to detect horizontal outliers that exhibit inconsistent across-view characteristics. The existing works assume that all objects are present in all views. However, in real-world applications, it is often the incomplete case that every view may suffer from some missing samples, resulting in partial objects difficult to detect outliers from. To address this problem, we propose a novel Collective Learning (CL) based framework to detect outliers from partial multi-view data in a self-guided way. More specifically, by well exploiting the inter-dependence among different views, we develop an algorithm to reconstruct missing samples based on learning. Furthermore, we propose similarity-based outlier detection to break through the dilemma that the number of clusters is unknown priori. Then, the calculated outlier scores act as the confidence levels in CL and in turn guide the reconstruction of missing data. Learning-based missing sample recovery and similarity-based outlier detection are iteratively performed in a self-guided manner. Experimental results on benchmark datasets show that our proposed approach consistently and significantly outperforms state-of-the-art baselines.

AIIM Journal 2018 Journal Article

RETRACTED: Diagnosis labeling with disease-specific characteristics mining

• Jun Guo
• Xuan Yuan
• Xia Zheng
• Pengfei Xu
• Yun Xiao
• Baoying Liu

IJCAI Conference 2018 Conference Paper

Robust Auto-Weighted Multi-View Clustering

• Pengzhen Ren
• Yun Xiao
• Pengfei Xu
• Jun Guo
• Xiaojiang Chen
• Xin Wang
• Dingyi Fang

Multi-view clustering has played a vital role in real-world applications. It aims to cluster the data points into different groups by exploring complementary information of multi-view. A major challenge of this problem is how to learn the explicit cluster structure with multiple views when there is considerable noise. To solve this challenging problem, we propose a novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters. ℓ1-norm is employed for robustness of the proposed algorithm. We have validated this in the later experiment. The new graph learned by the proposed model approximates the original graphs of each individual view but maintains an explicit cluster structure. With this optimal graph, we can immediately achieve the clustering results without any further post-processing. We conduct extensive experiments to confirm the superiority and robustness of the proposed algorithm.

AAAI Conference 2017 Conference Paper

Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution

• Jun Guo
• Hongyang Chao

We propose an end-to-end deep network for video superresolution. Our network is composed of a spatial component that encodes intra-frame visual patterns, a temporal component that discovers inter-frame relations, and a reconstruction component that aggregates information to predict details. We make the spatial component deep, so that it can better leverage spatial redundancies for rebuilding high-frequency structures. We organize the temporal component in a bidirectional and multi-scale fashion, to better capture how frames change across time. The effectiveness of the proposed approach is highlighted on two datasets, where we observe substantial improvements relative to the state of the arts.

YNICL Journal 2017 Journal Article

Gray matter volume changes in chronic subcortical stroke: A cross-sectional study

• Qingqing Diao
• Jingchun Liu
• Caihong Wang
• Chen Cao
• Jun Guo
• Tong Han
• Jingliang Cheng
• Xuejun Zhang

This study aimed to investigate the effects of lesion side and degree of motor recovery on gray matter volume (GMV) difference relative to healthy controls in right-handed subcortical stroke. Structural MRI data were collected in 97 patients with chronic subcortical ischemic stroke and 79 healthy controls. Voxel-wise GMV analysis was used to investigate the effects of lesion side and degree of motor recovery on GMV difference in right-handed chronic subcortical stroke patients. Compared with healthy controls, right-lesion patients demonstrated GMV increase (P <0. 05, voxel-wise false discovery rate correction) in the bilateral paracentral lobule (PCL) and supplementary motor area (SMA) and the right middle occipital gyrus (MOG); while left-lesion patients did not exhibit GMV difference under the same threshold. Patients with complete and partial motor recovery showed similar degree of GMV increase in right-lesion patients. However, the motor recovery was correlated with the GMV increase in the bilateral SMA in right-lesion patients. These findings suggest that there exists a lesion-side effect on GMV difference relative to healthy controls in right-handed patients with chronic subcortical stroke. The GMV increase in the SMA may facilitate motor recovery in subcortical stroke patients.

JBHI Journal 2017 Journal Article

HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information

• Xianbiao Qi
• Guoying Zhao
• Chun-Guang Li
• Jun Guo
• Matti Pietikainen

Indirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features.

AAAI Conference 2016 Conference Paper

Discriminative Analysis Dictionary Learning

• Jun Guo
• Yanqing Guo
• Xiangwei Kong
• Man Zhang
• Ran He

Dictionary learning (DL) has been successfully applied to various pattern classification tasks in recent years. However, analysis dictionary learning (ADL), as a major branch of DL, has not yet been fully exploited in classification due to its poor discriminability. This paper presents a novel DL method, namely Discriminative Analysis Dictionary Learning (DADL), to improve the classification performance of ADL. First, a code consistent term is integrated into the basic analysis model to improve discriminability. Second, a tripletconstraint-based local topology preserving loss function is introduced to capture the discriminative geometrical structures embedded in data. Third, correntropy induced metric is employed as a robust measure to better control outliers for classification. Then, half-quadratic minimization and alternate search strategy are used to speed up the optimization process so that there exist closed-form solutions in each alternating minimization stage. Experiments on several commonly used databases show that our proposed method not only significantly improves the discriminative ability of ADL, but also outperforms state-of-the-art synthesis DL methods.

AAAI Conference 2015 Conference Paper

Building Effective Representations for Sketch Recognition

• Jun Guo
• Changhu Wang
• Hongyang Chao

As the popularity of touch-screen devices, understanding a user’s hand-drawn sketch has become an increasingly important research topic in artificial intelligence and computer vision. However, different from natural images, the hand-drawn sketches are often highly abstract, with sparse visual information and large intraclass variance, making the problem more challenging. In this work, we study how to build effective representations for sketch recognition. First, to capture saliency patterns of different scales and spatial arrangements, a Gabor-based low-level representation is proposed. Then, based on this representation, to discovery more complex patterns in a sketch, a Hybrid Multilayer Sparse Coding (HMSC) model is proposed to learn midlevel representations. An improved dictionary learning algorithm is also leveraged in HMSC to reduce overfitting to common but trivial patterns. Extensive experiments show that the proposed representations are highly discriminative and lead to large improvements over the state of the arts.

AAAI Conference 2015 Conference Paper

Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model

• Siting Ren
• Sheng Gao
• Jianxin Liao
• Jun Guo

AAAI Conference 2015 Conference Paper

VecLP: A Realtime Video Recommendation System for Live TV Programs

• Sheng Gao
• Dai Zhang
• Honggang Zhang
• Chao Huang
• Yongsheng Zhang
• Jianxin Liao
• Jun Guo

We propose VecLP, a novel Internet Video recommendation system working for Live TV Programs in this paper. Given little information on the live TV programs, our proposed VecLP system can effectively collect necessary information on both the programs and the subscribers as well as a large volume of related online videos, and then recommend the relevant Internet videos to the subscribers. For that, the key frames are firstly detected from the live TV programs, and then visual and textual features are extracted from these frames to enhance the understanding of the TV broadcasts. Furthermore, by utilizing the subscribers’ profiles and their social relationships, a user preference model is constructed, which greatly improves the diversity of the recommendations in our system. The subscriber’s browsing history is also recorded and used to make a further personalized recommendation. This work also illustrates how our proposed VecLP system makes it happen. Finally, we dispose some sort of new recommendation strategies in use at the system to meet special needs from diverse live TV programs and throw light upon how to fuse these strategies.

AAAI Conference 2013 Conference Paper

A Maximum K-Min Approach for Classification

• Mingzhi Dong
• Liang Yin
• Weihong Deng
• Li Shang
• Jun Guo
• Honggang Zhang

In this paper, a general Maximum K-Min approach for classification is proposed. With the physical meaning of optimizing the classification confidence of the K worst instances, Maximum K-Min Gain/Minimum K- Max Loss (MKM) criterion is introduced. To make the original optimization problem with combinational number of constraints computationally tractable, the optimization techniques are adopted and a general compact representation lemma for MKM Criterion is summarized. Based on the lemma, a Nonlinear Maximum K- Min (NMKM) classifier and a Semi-supervised Maximum K-Min (SMKM) classifier are presented for traditional classification task and semi-supervised classi- fication task respectively. Based on the experiment results of publicly available datasets, our Maximum K- Min methods have achieved competitive performance when comparing against Hinge Loss classifiers.