Bin Shen

YNIMG Journal 2026 Journal Article

Alteration in the dynamics of ipsilateral cerebellar fluctuations and its association with balance performance in recreational athletes with chronic ankle instability: A cross-sectional study of resting-state fMRI

• Songlin Xiao
• Chuyi Zhang
• Bin Shen
• Zhen Xu
• Junhong Zhou
• Weijie Fu

OBJECTIVES: Chronic ankle instability (CAI) is not only associated with those peripheral neuromuscular impairments but also with the functional changes in the supraspinal regions. Nevertheless, the characteristics of the cortical elements in CAI remain poorly understood. This study aimed to examine the dynamics of resting-state BOLD and ankle-related functional performance in recreational athletes with CAI, as well as explore the associations between neural fluctuations and ankle functional performance. METHODS: This cross-sectional design study recruited 82 participants, comprising 41 active recreational athletes with CAI (CAI group) and 41 active recreational athletes without CAI (Control group). Data on joint position sense, one-leg standing balance, and resting-state fMRI were collected from both groups. A two-sample t-test was used to determine the difference in amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) between the two groups. Linear regression analysis evaluated the associations between functional performance and dynamics of resting-state BOLD in the two groups. RESULTS: Compared with control group, athletes with CAI had lower ALFF values in the bilateral supplementary motor area and reduced ReHo values in the right precentral gyrus and postcentral gyrus, while higher ALFF and ReHo values in the right cerebellum. Moreover, athletes with CAI had lower fALFF values in the left superior frontal gyrus and the right superior frontal gyrus than controls. The sway velocities of center of pressure in the one-leg standing with eyes closed condition were negatively associated with ALFF and ReHo values in the right cerebellum cluster. CONCLUSIONS: Athletes with severely right-sided CAI had different neural fluctuations compared with controls. Elevated ALFF and ReHo values in the right cerebellum cluster were associated with balance control, suggesting that high ipsilateral cerebellar activity and homogeneity may compensate for balance control in athletes with CAI.

EAAI Journal 2023 Journal Article

Interpretable knowledge-guided framework for modeling minimum miscible pressure of CO2-oil system in CO2-EOR projects

• Bin Shen
• Shenglai Yang
• Xinyuan Gao
• Shuai Li
• Kun Yang
• Jiangtao Hu
• Hao Chen

Carbon dioxide enhanced oil recovery (CO2-EOR) is a promising application for carbon capture, utilization and storage (CCUS). Accurate modeling of CO2-oil minimum miscible pressure (MMP) is crucial for CO2-EOR projects. In this study, a knowledge-guided framework for an extreme gradient boosting machine (XGBoost) and interpretable tabular learning architecture (TabNet), called KXGB and KTabNet, respectively, are developed to model the MMP. The proposed models are strengthened using a large MMP database of 421 samples collected from literature. Domain knowledge is integrated into intelligent models to prevent data-driven models from producing predictions that violated the domain knowledge. The Shapley Additive Explanations (SHAP) method is used to explain the proposed model to ensure the credibility of petroleum engineers. To further verify the model’s effectiveness, the same experimental strategy is employed to compare the proposed models with existing machine-learning (ML) methods. The results show that KXGB is the most recommended superior solution for modeling MMP owing to its outstanding performance and simplicity of optimization. The correlation coefficient, root mean square error, and mean absolute error are 0. 9833, 0. 7637 and 0. 55, respectively. However, KTabNet has great potential. Although its accuracy is slightly lower than that of the former, its strong representation ability and decision transparency may be favorable for future research. This study also demonstrate that the proposed framework conforms to certain theoretical rules and has a reasonable domain of applicability. To the best of our knowledge, this is the first study on integration of domain knowledge into MMP modeling methods. The experience and insights obtained from this study can guide CO2-EOR projects and other tabular data modeling in the oil and gas industry.

YNIMG Journal 2021 Journal Article

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit

• Moss Y Zhao
• Audrey P Fan
• David Yen-Ting Chen
• Magdalena J. Sokolska
• Jia Guo
• Yosuke Ishii
• David D Shin
• Mohammad Mehdi Khalighi

O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42±19% and 40±18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15±10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

IJCAI Conference 2017 Conference Paper

Instance-Level Label Propagation with Multi-Instance Learning

• Qifan Wang
• Gal Chechik
• Chen Sun
• Bin Shen

Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, example-level similarity is sometimes badly defined. For instance, two images may contain two different objects, but have similar overall appearance due to large similar background. In this case, computing similarities based on whole-image would fail propagating information to the right labels. This paper proposes a novel Instance-Level Label Propagation (ILLP) approach that integrates label propagation with multi-instance learning. Each example is treated as containing multiple instances, as in the case of an image consisting of multiple regions. We first construct a graph based on instance-level similarity and then simultaneously identify the instances carrying the labels and propagate the labels across instances in the graph. Optimization is based on an iterative Expectation Maximization (EM) algorithm. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach over several state-of-the-art methods.

IJCAI Conference 2015 Conference Paper

Learning to Hash on Partial Multi-Modal Data

• Qifan Wang
• Luo Si
• Bin Shen

Hashing approach becomes popular for fast similarity search in many large scale applications. Real world data are usually with multiple modalities or having different representations from multiple sources. Various hashing methods have been proposed to generate compact binary codes from multi-modal data. However, most existing multimodal hashing techniques assume that each data example appears in all modalities, or at least there is one modality containing all data examples. But in real applications, it is often the case that every modality suffers from the missing of some data and therefore results in many partial examples, i. e. , examples with some modalities missing. In this paper, we present a novel hashing approach to deal with Partial Multi-Modal data. In particular, the hashing codes are learned by simultaneously ensuring the data consistency among different modalities via latent subspace learning, and preserving data similarity within the same modality through graph Laplacian. We then further improve the codes via orthogonal rotation based on the orthogonal invariant property of our formulation. Experiments on two multi-modal datasets demonstrate the superior performance of the proposed approach over several state-of-the-art multi-modal hashing methods.

AAAI Conference 2015 Conference Paper

Learning to Hash on Structured Data

• Qifan Wang
• Luo Si
• Bin Shen

Hashing techniques have been widely applied for large scale similarity search problems due to the computational and memory efficiency. However, most existing hashing methods assume data examples are independently and identically distributed. But there often exists various additional dependency/structure information between data examples in many real world applications. Ignoring this structure information may limit the performance of existing hashing algorithms. This paper explores the research problem of learning to Hash on Structured Data (HSD) and formulates a novel framework that considers additional structure information. In particular, the hashing function is learned in a unified learning framework by simultaneously ensuring the structural consistency and preserving the similarities between data examples. An iterative gradient descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error. Experimental results on two datasets clearly demonstrate the advantages of the proposed method over several state-of-the-art hashing methods.

AAAI Conference 2015 Conference Paper

Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation

• Zenglin Xu
• Rong Jin
• Bin Shen
• Shenghuo Zhu

Nyström approximation is an effective approach to accelerate the computation of kernel matrices in many kernel methods. In this paper, we consider the Nyström approximation for sparse kernel methods. Instead of relying on the low-rank assumption of the original kernels, which sometimes does not hold in some applications, we take advantage of the restricted eigenvalue condition, which has been proved to be robust for sparse kernel methods. Based on the restricted eigenvalue condition, we have provided not only the approximation bound for the original kernel matrix but also the recovery bound for the sparse solutions of sparse kernel regression. In addition to the theoretical analysis, we also demonstrate the good performance of the Nyström approximation for sparse kernel regression on real world data sets.

AAAI Conference 2015 Conference Paper

SP-SVM: Large Margin Classifier for Data on Multiple Manifolds

• Bin Shen
• Bao-Di Liu
• Qifan Wang
• Yi Fang
• Jan Allebach

As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc. , due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP- SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on realworld benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.

YNIMG Journal 2010 Journal Article

[18F]FTC-146: A novel and highly selective PET ligand for visualizing sigma-1 receptors in living subjects

• Michelle L. James
• Bin Shen
• Cristina Zavaleta
• Rhona A. Berganos
• Christophe Mesangeau
• Jamaluddin Shaikh
• Sanjiv S. Gambhir
• Rae R. Matsumoto

AAAI Conference 2010 Conference Paper

Non-Negative Matrix Factorization Clustering on Multiple Manifolds

• Bin Shen
• Luo Si

Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as clustering. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two nonnegative matrices. In many applications with data such as human faces or digits, data often reside on multiple manifolds, which may overlap or intersect. But the traditional NMF method and other existing variants of NMF do not consider this. This paper proposes a novel clustering algorithm that explicitly models the intrinsic geometrical structure of the data on multiple manifolds with NMF. The idea of the proposed algorithm is that a data point generated by several neighboring points on a specific manifold in the original space should be constructed in a similar way in the low dimensional subspace. A set of experimental results on two real world datasets demonstrate the advantage of the proposed algorithm.