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Yiran Li

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6 papers

1 author row

EAAI Journal 2026 Journal Article

An emotion recognition approach using peripheral physiological signals based on hierarchical gated residuals and receptive field attention

Yonghui Yu
Hongji Xu
Zhikai Xu
Yupeng Duan
Renzhuo Wang
Hao Zheng
Yiran Li
Yipeng Xu

The field of emotion recognition (ER) based on peripheral physiological signals (PPSs) has gained significant attention due to the analytical capabilities of artificial intelligence (AI) and its extensive applications. However, a common challenge in many existing ER networks lies in effectively extracting and integrating features from the PPSs. Some networks rely solely on single-channel features, while others struggle with efficient multi-channel fusion, often resulting in suboptimal accuracy. Moreover, the lack of PPS-based public ER datasets, with most existing datasets focusing on the electroencephalogram signal, continues to pose an obstacle. The hierarchical gated residual and receptive field attention fusion (HGR-RFAF) network is proposed to address the above challenges. The HGR-RFAF network improves feature extraction and fusion through multiple multi-branch hierarchical residual fusion convolution (MB-HRFC) layers and a multi-scale dilated convolution (MS-DC) layer. Additionally, the I + Lab Emotion (ILEmo) dataset is constructed to address the scarcity of public ER datasets based on PPSs. To evaluate the performance of the HGR-RFAF network, experiments are conducted on the public K-EmoCon, DEAP, and self-constructed ILEmo datasets. By applying the valence-arousal (V-A) model for validation, the HGR-RFAF network achieves accuracies of 91. 15 % (A)/93. 03 % (V) for the K-EmoCon dataset and 81. 44 % (A)/82. 07 % (V) for the DEAP dataset, respectively. On the ILEmo dataset, HGR-RFAF achieves an accuracy of 91. 25 % with the discrete emotion model. Moreover, various PPSs and their combinations are evaluated, highlighting the benefits of fusing multi-channel PPSs along with their complementarities and contributions to ER.

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EAAI Journal 2026 Journal Article

Temporal-spatial parallel multiscale network with sparse three-channel mixed attention for wearable sensor-based human activity recognition

Renzhuo Wang
Hongji Xu
Yiran Li
Yonghui Yu
Yupeng Duan
Zhikai Xu
Wentao Ai
Xinya Li

Human activity recognition (HAR) based on wearable sensors using deep learning (DL) models has garnered significant attention in recent years. However, existing models encounter several challenges in fully exploiting the information from multi-source sensor positions. Notably, they often fail to provide adequate interpretability in terms of both single-channel attention extraction and inter-channel attention mixing. This paper proposes a novel temporal-spatial parallel multiscale network with sparse three-channel mixed attention (TSPM-STCMA) designed to independently and in parallel learn temporal-spatial features from both single-channel and inter-channel relationships derived from multi-source sensors at various body positions. Specifically, the multiscale dynamic convolution with sparse three-channel mixed attention (MDC-STCMA) module is designed to enhance the interpretability of both single-channel attention and inter-channel attention from the same sensor position. Furthermore, the MDC-STCMA module integrates three components based on an attention mechanism. The three-channel dynamic convolution based on improved squeeze-and-excitation (TCDC-ISE) enables a single channel to generate distinct attention responses. The three-channel mixed attention (TCMA) extracts inter-channel correlations. The sparse attention (SA) mitigates overfitting by retraining features with low contributions. Compared with previous models, the TSPM-STCMA achieves superior recognition accuracies of 98. 72 %, 96. 83 %, and 98. 40 %, on three publicly available datasets, i. e. , Physical Activity Monitoring for Aging People (PAMAP2), OPPORTUNITY activity recognition (OPPORTUNITY), and University of California Irvine HAR (UCI-HAR), respectively, while requiring significantly fewer parameters.

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AAAI Conference 2025 Conference Paper

RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction

Zhihao Ding
Ting Zhang
Yiran Li
Jieming Shi
Chen Jason Zhang

Organic Solar Cells (OSCs) are a promising technology for sustainable energy production. However, the identification of molecules with desired OSC properties typically involves laborious experimental research. To accelerate progress in the field, it is crucial to develop machine learning models capable of accurately predicting the properties of OSC molecules. While graph representation learning has demonstrated success in molecular property prediction, it remains underexplored for OSC-specific tasks. Existing methods fail to capture the unique structural features of OSC molecules, particularly the intricate ring systems that critically influence OSC properties, leading to suboptimal performance. To fill the gap, we present RingFormer, a novel graph transformer framework specially designed to capture both atom and ring level structural patterns in OSC molecules. RingFormer constructs a hierarchical graph that integrates atomic and ring structures and employs a combination of local message passing and global attention mechanisms to generate expressive graph representations for accurate OSC property prediction. We evaluate RingFormer's effectiveness on five curated OSC molecule datasets through extensive experiments. The results demonstrate that RingFormer consistently outperforms existing methods, achieving a 22.77% relative improvement over the nearest competitor on the CEPDB dataset.

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JBHI Journal 2023 Journal Article

Deeply Accelerated Arterial Spin Labeling Perfusion MRI for Measuring Cerebral Blood Flow and Arterial Transit Time

Yiran Li
Ze Wang

Cerebral blood flow (CBF) indicates both vascular integrity and brain function. Regional CBF can be non-invasively measured with arterial spin labeling (ASL) perfusion MRI. By repeating the same ASL MRI sequence several times, each with a different post-labeling delay (PLD), another important neurovascular index, the arterial transit time (ATT) can be estimated by fitting the acquired ASL signal to a kinetic model. This process however faces two challenges: one is the multiplicatively prolonged scan time, making it impractically for clinical use due to the escalated risk of motions; the other is the reduced signal-to-noise-ratio (SNR) in the long PLD scans due to the T1 decay of the labeled spins. Increasing SNR needs more repetitions which will further increase the total scan time. Currently, there lacks a way to accurately estimate ATT from a parsimonious number of PLDs. In this paper, we proposed a deep learning-based algorithm to reduce the number of PLDs and to accurately estimate ATT and CBF. Two separate deep networks were trained: one is designed to estimate CBF and ATT from ASL data with a single PLD; the other is to estimate CBF and ATT from ASL data with two PLDs. The models were trained and tested using the large Human Connectome Project multiple-PLD ASL MRI. Performance of the DL-based approach was compared to the traditional full dataset-based data fitting approach. Our results showed that ATT and CBF can be reliably estimated using deep networks even with one PLD.

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TCS Journal 2023 Journal Article

Recursive solution of initial value problems with temporal discretization

Abbas Edalat
Amin Farjudian
Yiran Li

We construct a continuous domain, as a model of interval analysis, for temporal discretization of differential equations. By using this domain, and the domain of Lipschitz maps, we formulate a generalization of the Euler operator, which exhibits second-order convergence. We prove computability of the operator within the framework of effectively given domains. The operator only requires the vector field of the differential equation to be Lipschitz continuous, in contrast to the related operators in the literature which require the vector field to be at least continuously differentiable. Within the same framework, we also analyze temporal discretization and computability of another variant of the Euler operator formulated according to Runge-Kutta theory. We prove that, compared with this variant, the second-order operator that we formulate directly, not only imposes weaker assumptions on the vector field, but also exhibits superior convergence rate. We implement the first-order, second-order, and Runge-Kutta Euler operators using arbitrary-precision interval arithmetic, and report on some experiments. The experiments confirm our theoretical results. In particular, we observe the superior convergence rate of our second-order operator compared with the Runge-Kutta Euler and the common (first-order) Euler operators.

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YNIMG Journal 2022 Journal Article

Lower resting brain entropy is associated with stronger task activation and deactivation

Liandong Lin
Da Chang
Donghui Song
Yiran Li
Ze Wang

Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.

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