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Wentao Chen

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7 papers

2 author rows

YNIMG Journal 2025 Journal Article

Altered brain network dynamics during rumination in remitted depression

Su Shu
Wenwen Ou
Mohan Ma
Hairuo He
Qianqian Zhang
Mei Huang
Wentao Chen
Aoqian Deng

Rumination is a known risk factor for depression relapse. Understanding its neurobiological mechanisms during depression remission can inform strategies to prevent relapse, yet the temporal dynamics of brain networks during rumination in remitted depression remain unclear. Here, we collected rumination induction fMRI data from 42 patients with remitted depression and 41 healthy controls (HCs). Using an energy landscape approach, we investigated the temporal dynamics of brain networks during rumination. The appearance frequency (AF) and transition frequency (TF) metrics were defined to quantify the dynamic properties of brain states. Patients during remission showed higher levels of rumination than HCs. Both groups exhibited four brain states during rumination, which consisted of complementary network group activation (states 1 and 2, states 3 and 4). In patients, the AFs of and reciprocal TFs between states 1 and 2 during rumination were significantly increased, while AFs of states 3 and 4 and reciprocal TFs involving states 1-3, 1-4, 2-3, and 2-4 were decreased, both when compared to HCs and relative to patients themselves during distraction. Moreover, we found that for patients, the AF of state 1 was negatively correlated with rumination levels and marginally positively associated with attention, while the AF of state 2 was negatively associated with performance on attention tasks. Our study revealed altered dynamic characteristics of brain states composed of network groups during rumination in remitted depression. Additionally, the findings suggest that heightened self-focus linked to rumination may impair the brain's ability to efficiently allocate attentional resources.

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YNICL Journal 2025 Journal Article

Brain network dynamics during rumination relate to relapse of depression

Su Shu
Yumeng Ju
Mi Wang
Wenwen Ou
Mohan Ma
Qianqian Zhang
Mei Huang
Hairuo He

BACKGROUND: Rumination is a maladaptive cognitive style and a risk factor for relapse of depression. However, the clinically relevant pattern of dynamic network reconfiguration during rumination in remitted depression and its implication in relapse remained unclear. METHODS: We employed a rumination induction neuroimaging paradigm in which subjects would be guided into an active rumination state and a distraction state. Forty-two patients with remitted depression were involved. Participants underwent assessments of rumination behavior and imaging tasks, and were then monitored for two year to assess the potential relapse of depression. A time-resolved community detection approach was applied to investigate the temporal dynamics of brain networks, and the dynamic network properties including flexibility and integration were analyzed. RESULTS: = 0.036). Moreover, elastic net regression indicated that dynamic network features could predict two-year relapse outcomes with moderate accuracy (AUC = 0.70). CONCLUSIONS: Our findings reveal a potential mechanistic link between the brain network dynamics during rumination and relapse of depression, shedding light on the intricate relationship between cognitive-affective processes, neural dynamics, and the potential vulnerability to depression recurrence.

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ICLR Conference 2025 Conference Paper

The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model

Jiawei Chen 0011
Wentao Chen
Jing Su
Jingjing Xu
Hongyu Lin
Mengjie Ren
Yaojie Lu 0001
Xianpei Han

Large language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not well understood. In this paper, we use code LLMs as an experimental platform to explore the evolution of multilingual capabilities in LLMs during the pre-training process. Based on our observations, we propose the Babel Tower Hypothesis, which describes the entire process of LLMs acquiring new language capabilities. During the learning process, multiple languages initially share a single knowledge system dominated by the primary language and gradually develop language-specific knowledge systems. We then validate the above hypothesis by tracking the internal states of the LLM using specific methods. Experimental results show that the internal state changes of the LLM are consistent with our Babel Tower Hypothesis. Building on these insights, we propose a novel method to construct an optimized pre-training corpus for multilingual code LLMs, which significantly outperforms LLMs trained on the original corpus. The proposed Babel Tower Hypothesis provides new insights into designing pre-training data distributions to achieve optimal multilingual capabilities in LLMs.

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EAAI Journal 2024 Journal Article

A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples

Jian Tang
Jiawei Xiao
Wentao Chen
Xuegang Li
Chao Wei
Xiaoxi Ding
Wenbin Huang

Data-driven intelligent fault diagnosis methods automatically construct the laws of mechanical faults via mining and learning from monitoring data. However, the performance of highly accurate intelligent fault diagnosis models severely depends on the extremely limited availability of high-quality labeled data in industrial scenarios. Insufficient training data even cause the data-driven models to learn the wrong classification boundaries. Therefore, this study proposes a prior knowledge-enhanced self-supervised learning framework using time-frequency invariance, aiming to reduce the amount of training data required for the model. First, 12 priori time-domain features and 12 priori frequency-domain features are established as pseudo-labels for the time-domain feature extractor and frequency-domain feature extractor, respectively. Then, inspired by the transformation relationship between the frequency and time domains of the signal, time-frequency invariance is proposed to enhance the feature learning of the model on the signal in the pre-training stage. The time-domain pseudo label, the frequency-domain pseudo label and the time-frequency domain invariance together constitute the pretext task on the unlabeled data in the pre-training stage. The prior knowledge-enhanced pretext task has the potential to mine richer features from unlabeled monitoring data. Three experiments on mechanical failure datasets validate the effectiveness of the framework in small sample tasks and downstream transfer tasks. Furthermore, ablation experiments also validate the effectiveness of the time-frequency domain network framework proposed in this paper. The framework demonstrates great potential for industrial utilization from the perspective of prior diagnostic knowledge.

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AAAI Conference 2024 Conference Paper

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization

Jiahao Qiu
Hui Yuan
Jinghong Zhang
Wentao Chen
Huazheng Wang
Mengdi Wang

While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient, diversity-promoting, and able to find top designs using reasonably small mutation counts.

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IROS Conference 2023 Conference Paper

Polymer-Based Self-Calibrated Optical Fiber Tactile Sensor

Wentao Chen
Youcan Yan
Zeqing Zhang
Lei Yang 0048
Jia Pan 0001

Human skin can accurately sense the self-decoupled normal and shear forces when in contact with objects of different sizes. Although there exist many soft and conformable tactile sensors on robotic applications able to decouple the normal force and shear forces, the impact of the size of object in contact on the force calibration model has been commonly ignored. Here, using the principle that contact force can be derived from the light power loss in the soft optical fiber core, we present a soft tactile sensor that decouples normal and shear forces and calibrates the measurement results based on the object size, by designing a two-layered weaved polymer-based optical fiber anisotropic structure embedded in a soft elastomer. Based on the anisotropic response of optical fibers, we developed a linear calibration algorithm to simultaneously measure the size of the contact object and the decoupled normal and shear forces calibrated the object size. By calibrating the sensor at the robotic arm tip, we show that robots can reconstruct the force vector at an average accuracy of 0. 15N for normal forces, 0. 17N for shear forces in X-axis, and 0. 18N for shear forces in Y-axis, within the sensing range of 0-2N in all directions, and the average accuracy of object size measurement of 0. 4mm, within the test indenter diameter range of 5-12mm.

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IJCAI Conference 2021 Conference Paper

Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images

Wentao Chen
Chenyang Si
Wei Wang
Liang Wang
Zilei Wang
Tieniu Tan

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part. To mitigate the overfitting in few-shot classification caused by data scarcity, we further propose a part augmentation strategy by retrieving extra images from a base dataset. We conduct systematic studies on miniImageNet and tieredImageNet benchmarks. Remarkably, our method yields impressive results, outperforming the previous best unsupervised methods by 7. 74% and 9. 24% under 5-way 1-shot and 5-way 5-shot settings, which are comparable with state-of-the-art supervised methods.

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