Author name cluster

Wei Meng

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6 papers

2 author rows

YNIMG Journal 2026 Journal Article

Baicalin reactivates ocular dominance plasticity to restore vision from amblyopia in adult mice

Fei Yin
Wei Meng
Chenchen Ma
Yupeng Yang

Amblyopia is a neurodevelopmental disorder characterized by reduced visual acuity due to abnormal visual experience during critical periods. In adulthood, the diminished plasticity of the primary visual cortex (V1) presents a major barrier to effective treatment. Here, we investigate whether baicalin, a flavonoid derived from Scutellaria baicalensis, can restore ocular dominance plasticity (ODP) and promote functional recovery in a mouse model of adult amblyopia. Using intrinsic signal optical imaging and electrophysiological recording, we demonstrate that 10 mg/kg baicalin treatment reactivates ODP in adult mice, whereas 5mg/kg or Scutellaria water extract fails to do so. Furthermore, baicalin combined with reverse suturing in adult amblyopic mice restored both ocular dominance distribution and visual acuity to normal levels. Baicalin treatment reduced the expression of two major GABA synthetic enzymes (glutamate decarboxylase, GAD65/67) and perineuronal nets in V1, while administration of the GABAA receptor agonist muscimol during the baicalin treatment blocked the rescued ODP. These findings suggested that a reduction in cortical inhibition might underlie the restoration of visual plasticity in adults. Our results suggest that baicalin may serve as a potential therapy for adult amblyopia.

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IROS Conference 2025 Conference Paper

Learning-Based Quadruped Robot Framework for Locomotion on Dynamic Rigid Platforms

Kai Huang 0001
Heming Feng
Wei Meng
Tianqi Wei
Tianjiang Hu

Typical robot controllers assume firm ground, limiting their effectiveness in controlling robots on dynamic platforms such as trucks or ships. To address this limitation, we propose a reinforcement learning framework for robot locomotion on dynamic rigid platforms and a simulation in which 6-DoF dynamic platforms emulating ship oscillation. The framework enables a reinforcement learning model to estimate platform motion during robot locomotion control. In the simulation, our framework significantly reduces the quadruped robot’s fall rate and trajectory deviation compared to baseline controllers. Experiments on a real robot show that our framework enabled a quadruped robot to adapt to platform motions, including those that threw the robot into the air, while baseline models struggled in this case. Thus, our framework can advance the deployment of robots in real-world marine and vehicular applications.

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EAAI Journal 2025 Journal Article

Prediction and rationality analysis of new energy vehicle sales in China with a novel intelligent buffer operator

Bo Zeng
Fengfeng Yin
Jianzhou Wang
Wei Meng

Despite the industry's booming development, there is a concern that new energy vehicles (NEVs) overcapacity and resource and environmental constraints pose a risk to Chinese new energy vehicle industry. Accordingly, it is essential to achieve accurate prediction of future sales to promote the healthy and expeditious growth of Chinese new energy vehicle industry. On the one hand, starting from the data-oriented perspective, a novel intelligent buffer operator (which is an important data processing method in the field of Artificial Intelligence) that integrates the principle of prioritizing new information and introduces a power exponent is proposed, which addresses the issue of systematic prediction traps resulting from perturbations in the data sets of new energy vehicle sales in China. Then a whitenization grey prediction model has been formulated by optimizing parameters combination, which has high compatibility, adaptability, and modelling capability. The new grey prediction model utilizing an intelligent buffer operator has been employed to simulate the data of sales for Chinese new energy vehicles. The new grey prediction model based on intelligent buffer operator is applied to model new energy vehicle sales, and its comprehensive error is better than that of the models based on the weighted geometric average buffer operator and the full information buffer operator, providing a better description of the data characteristics and development trend of new energy vehicle sales in China. The latest forecast indicates that the sales of new energy vehicles in China will continue to increase from 2023 to 2025, surpassing 10 million units in 2024.

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AAAI Conference 2024 Conference Paper

Enhancing Evolving Domain Generalization through Dynamic Latent Representations

Binghui Xie
Yongqiang Chen
Jiaqi Wang
Kaiwen Zhou
Bo Han
Wei Meng
James Cheng

Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several stationary domains to enable generalization to new domains. However, in non-stationary tasks where new domains evolve in an underlying continuous structure, such as time, merely extracting the invariant features is insufficient for generalization to the evolving new domains. Nevertheless, it is non-trivial to learn both evolving and invariant features within a single model due to their conflicts. To bridge this gap, we build causal models to characterize the distribution shifts concerning the two patterns, and propose to learn both dynamic and invariant features via a new framework called Mutual Information-Based Sequential Autoencoders (MISTS). MISTS adopts information theoretic constraints onto sequential autoencoders to disentangle the dynamic and invariant features, and leverage an adaptive classifier to make predictions based on both evolving and invariant information. Our experimental results on both synthetic and real-world datasets demonstrate that MISTS succeeds in capturing both evolving and invariant information, and present promising results in evolving domain generalization tasks.

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NeurIPS Conference 2024 Conference Paper

HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection

Binghui Xie
Yixuan Wang
Yongqiang Chen
Kaiwen Zhou
Yu Li
Wei Meng
James Cheng

Subset selection tasks, such as anomaly detection and compound selection in AI-assisted drug discovery, are crucial for a wide range of applications. Learning subset-valued functions with neural networks has achieved great success by incorporating permutation invariance symmetry into the architecture. However, existing neural set architectures often struggle to either capture comprehensive information from the superset or address complex interactions within the input. Additionally, they often fail to perform in scenarios where superset sizes surpass available memory capacity. To address these challenges, we introduce the novel concept of the Identity Property, which requires models to integrate information from the originating set, resulting in the development of neural networks that excel at performing effective subset selection from large supersets. Moreover, we present the Hierarchical Representation of Neural Subset Selection (HORSE), an attention-based method that learns complex interactions and retains information from both the input set and the optimal subset supervision signal. Specifically, HORSE enables the partitioning of the input ground set into manageable chunks that can be processed independently and then aggregated, ensuring consistent outcomes across different partitions. Through extensive experimentation, we demonstrate that HORSE significantly enhances neural subset selection performance by capturing more complex information and surpasses state-of-the-art methods in handling large-scale inputs by a margin of up to 20%.

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EAAI Journal 2016 Journal Article

A self-adaptive intelligence grey predictive model with alterable structure and its application

Bo Zeng
Wei Meng
Mingyu Tong

The adaptability of the traditional GM (1, 1) model is poor because it is a rigorous homogenous exponent model with a single fixed structure. To improve the adaptability of the traditional grey model, a self-adaptive intelligence grey predictive model with an alterable structure is proposed in this paper. The proposed model has the advantages of adjustable parameters and is characterised by its variable structure as a homogenous/non-homogenous exponent model or as a single-variable linear–auto-regression model. It can be used to automatically compute the relative optimal modelling parameters and adaptively choose a more reasonable model structure based on the real data characteristics of a modelling sequence. Hence, this novel model outperforms traditional grey models with a single fixed structure. To verify its efficiency and applicability, the proposed model was used to simulate China’s electricity consumption from 2001 to 2013 and to forecast it in 2014 using real data; the results indicate that the novel model has better simulative and predictive accuracy than the GM (1, 1) and DGM (1, 1) models.

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