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Mei Li

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11 papers
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11

YNIMG Journal 2026 Journal Article

Social status modulates cooperative feedback processing: Electrophysiological evidence from an event-related potential study

  • Mei Li
  • Wenbin Pan
  • Xukai Zhang
  • Jialu Li
  • Jin Li
  • Qingfeng Peng
  • Hong Li

Collaboration is essential, as both one's own and others' performance impact outcomes. However, little is known about how social status affects performance and reward processing during cooperation. This study used event-related potentials (ERPs) to investigate neural responses during cooperative tasks with high- and low-status partners, where participants were assigned middle status after a math competition. ERP signals were recorded as they observed self-performance, partner performance, and cooperative outcomes. Results revealed asymmetric processing: participants referenced their performance when evaluating others', while self-performance processing was less influenced by others'. In the self-other performance order, participants showed more negative feedback-related negativity (FRN) following others' incorrect performance after their own correct performance, and larger P2 to others' correct performance after their own incorrect performance. In the other-self order, smaller P2 and more negative FRN were found for self-incorrect performance regardless of partners' performance, with only the P2 effect larger when partners were incorrect. Moreover, high-status partners elicited more negative FRN than low-status partners when others performed correctly; this difference disappeared when others performed incorrectly. For order-independent cooperative outcomes, with high-status partners, the FRN was more negative and the P3a was smaller for self-loss than self-gain when partners gained, reversing when partners lost. With low-status partners, only the P3a difference persisted when partners gained, while both components mirrored the high-status pattern when partners lost. These findings suggest that social status shapes sequential cooperative feedback processing, particularly during the later integrative stage in which cooperative outcomes are evaluated under prior knowledge of self and partner performance.

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ICML Conference 2025 Conference Paper

BECAME: Bayesian Continual Learning with Adaptive Model Merging

  • Mei Li
  • Yuxiang Lu
  • Qinyan Dai
  • Suizhi Huang
  • Yue Ding 0001
  • Hongtao Lu 0001

Continual Learning (CL) strives to learn incrementally across tasks while mitigating catastrophic forgetting. A key challenge in CL is balancing stability (retaining prior knowledge) and plasticity (learning new tasks). While representative gradient projection methods ensure stability, they often limit plasticity. Model merging techniques offer promising solutions, but prior methods typically rely on empirical assumptions and carefully selected hyperparameters. In this paper, we explore the potential of model merging to enhance the stability-plasticity trade-off, providing theoretical insights that underscore its benefits. Specifically, we reformulate the merging mechanism using Bayesian continual learning principles and derive a closed-form solution for the optimal merging coefficient that adapts to the diverse characteristics of tasks. To validate our approach, we introduce a two-stage framework named BECAME, which synergizes the expertise of gradient projection and adaptive merging. Extensive experiments show that our approach outperforms state-of-the-art CL methods and existing merging strategies https: //github. com/limei0818/BECAME.

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ICML Conference 2024 Conference Paper

Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data

  • Yafei Wang
  • Bo Pan
  • Mei Li
  • Jianya Lu
  • Lingchen Kong
  • Bei Jiang
  • Linglong Kong

Conditional Stochastic Optimization (CSO) is a powerful modelling paradigm for optimization under uncertainty. The existing literature on CSO is mainly based on the independence assumption of data, which shows that the solution of CSO is asymptotically consistent and enjoys a finite sample guarantee. The independence assumption, however, does not typically hold in many important applications with dependence patterns, such as time series analysis, operational control, and reinforcement learning. In this paper, we aim to fill this gap and consider a Sample Average Approximation (SAA) for CSO with dependent data. Leveraging covariance inequalities and independent block sampling technique, we provide theoretical guarantees of SAA for CSO with dependent data. In particular, we show that SAA for CSO retains asymptotic consistency and a finite sample guarantee under mild conditions. In addition, we establish the sample complexity $O(d / \varepsilon^4)$ of SAA for CSO, which is shown to be of the same order as independent cases. Through experiments on several applications, we verify the theoretical results and demonstrate that dependence does not degrade the performance of the SAA approach in real data applications.

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YNIMG Journal 2022 Journal Article

A novel technology for in vivo detection of cell type-specific neural connection with AQP1-encoding rAAV2-retro vector and metal-free MRI

  • Ning Zheng
  • Mei Li
  • Yang Wu
  • Challika Kaewborisuth
  • Zhen Li
  • Zhu Gui
  • Jinfeng Wu
  • Aoling Cai

A mammalian brain contains numerous neurons with distinct cell types for complex neural circuits. Virus-based circuit tracing tools are powerful in tracking the interaction among the different brain regions. However, detecting brain-wide neural networks in vivo remains challenging since most viral tracing systems rely on postmortem optical imaging. We developed a novel approach that enables in vivo detection of brain-wide neural connections based on metal-free magnetic resonance imaging (MRI). The recombinant adeno-associated virus (rAAV) with retrograde ability, the rAAV2-retro, encoding the human water channel aquaporin 1 (AQP1) MRI reporter gene was generated to label neural connections. The mouse was micro-injected with the virus at the Caudate Putamen (CPU) region and subjected to detection with Diffusion-weighted MRI (DWI). The prominent structure of the CPU-connected network was clearly defined. In combination with a Cre-loxP system, rAAV2-retro expressing Cre-dependent AQP1 provides a CPU-connected network of specific type neurons. Here, we established a sensitive, metal-free MRI-based strategy for in vivo detection of cell type-specific neural connections in the whole brain, which could visualize the dynamic changes of neural networks in rodents and potentially in non-human primates.

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AAAI Conference 2022 Conference Paper

Noninvasive Lung Cancer Early Detection via Deep Methylation Representation Learning

  • Xiangrui Cai
  • Jinsheng Tao
  • Shichao Wang
  • Zhiyu Wang
  • Jiaxian Wang
  • Mei Li
  • Hong Wang
  • Xixiang Tu

Early detection of lung cancer is crucial for five-year survival of patients. Compared with the pathological analysis and CT scans, the circulating tumor DNA (ctDNA) methylation based approach is noninvasive and cost-effective, and thus is one of the most promising methods for early detection of lung cancer. Existing studies on ctDNA methylation data measure the methylation level of each region with a predefined metric, ignoring the positions of methylated CpG sites and methylation patterns, thus are not able to capture the early cancer signals. In this paper, we propose a blood-based lung cancer detection method, and present the first ever study to represent methylation regions by continuous vectors. Specifically, we propose DeepMeth to regard each region as a one-channel image and develop an auto-encoder model to learn its representation. For each ctDNA methylation sample, DeepMeth achieves its representation via concatenating the region vectors. We evaluate DeepMeth on a multicenter clinical dataset collected from 14 hospitals. The experiments show that DeepMeth achieves about 5%-8% improvements compared with the baselines in terms of Area Under the Curve (AUC). Moreover, the experiments also demonstrate that DeepMeth can be combined with traditional scalar metrics to enhance the diagnostic power of ctDNA methylation classifiers. DeepMeth has been clinically deployed and applied to 450 patients from 94 hospitals nationally since April 2020.

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ICRA Conference 2021 Conference Paper

Direct Sparse Stereo Visual-Inertial Global Odometry

  • Ziqiang Wang
  • Mei Li
  • Dingkun Zhou
  • Ziqiang Zheng

Robust and accurate localization plays a key role in autonomous driving and robot applications. To utilize the complementary properties of different sensors, we present a novel tightly-coupled approach to combine the local (stereo cameras, IMU) and global sensors (magnetometer, GNSS). We jointly optimize all the model parameters through one active window. The visual part integrates constraints from static stereo into the photometric bundle adjustment pipeline of dynamic multiview stereo. Accumulating IMU information between keyframes, magnetometer and GNSS measurements are all inserted into the active window as additional constrains among all the keyframes. Through these, our method can realize globally drift-free and locally accurate state estimation. We evaluate the effectiveness of our system on public datasets under with real-world experiments.

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JBHI Journal 2021 Journal Article

Predicting Recurrence for Patients With Ischemic Cerebrovascular Events Based on Process Discovery and Transfer Learning

  • Haifeng Xu
  • Jianfei Pang
  • Weiliang Zhang
  • Xuemeng Li
  • Mei Li
  • Dongsheng Zhao

The recurrence of Ischemic cerebrovascular events (ICE) often results in a high rate of mortality and disability. However, due to the lack of labeled follow-up data in hospitals, prediction methods using traditional machine learning are usually not available or reliable. Therefore, we propose a new framework for predicting the long-term recurrence risk in patients with ICE after discharge from hospitals based on process mining and transfer learning, to point out high-risk patients for intervention. First, process models are discovered from clinical guidelines for analyzing the similarity of ICE population data collected by different medical institutions, and the control flow found are taken as added characteristics of patients. Then we use the in-hospital data (target domain) and the national stroke screening data (source domain), to develop risk prediction models applying instance filter and weight-based transfer learning method. To verify our method, 205 cases from a tertiary hospital and 2954 cases from the screening cohort (2015-2017) are tested. Experimental results show that our framework can improve the performance of three instance-based transfer algorithms. This study provides a comprehensive and efficient approach for applying transfer learning, to alleviate the limitation of insufficient labeled follow-up data in hospitals.

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