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Guangyu Chen

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6 papers

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NeurIPS Conference 2025 Conference Paper

Universal Visuo-Tactile Video Understanding for Embodied Interaction

Yifan Xie
Mingyang Li
Shoujie Li
Xingting Li
Guangyu Chen
Fei Ma
Fei Yu
Wenbo Ding

Tactile perception is essential for embodied agents to understand the physical attributes of objects that cannot be determined through visual inspection alone. While existing methods have made progress in visual and language modalities for physical understanding, they fail to effectively incorporate tactile information that provides crucial haptic feedback for real-world interaction. In this paper, we present VTV-LLM, the first multi-modal large language model that enables universal Visuo-Tactile Video (VTV) understanding, bridging the gap between tactile perception and natural language. To address the challenges of cross-sensor and cross-modal integration, we contribute VTV150K, a comprehensive dataset comprising 150, 000 video frames from 100 diverse objects captured across three different tactile sensors (GelSight Mini, DIGIT, and Tac3D), annotated with four fundamental tactile attributes (hardness, protrusion, elasticity, and friction). We develop a novel three-stage training paradigm that includes VTV enhancement for robust visuo-tactile representation, VTV-text alignment for cross-modal correspondence, and text prompt finetuning for natural language generation. Our framework enables sophisticated tactile reasoning capabilities including feature assessment, comparative analysis, and scenario-based decision-making. Extensive experimental evaluations demonstrate that VTV-LLM achieves superior performance in tactile reasoning tasks, establishing a foundation for more intuitive human-machine interaction in tactile domains.

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EAAI Journal 2024 Journal Article

Accurate identification and confidence evaluation of automatic generation control command execution effect based on deep learning fusion model

Guangyu Chen
Hongtong Liu
Haiyang Jiang
Qing Li
Yangfei Zhang
Sipeng Hao
Wenhe Zhao

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces “deep learning” technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0. 5 MV, which can support AGC online decision-making. © 2017 Elsevier Inc. All rights reserved.

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YNIMG Journal 2017 Journal Article

Propofol attenuates low-frequency fluctuations of resting-state fMRI BOLD signal in the anterior frontal cortex upon loss of consciousness

Xiaolin Liu
Kathryn K. Lauer
B. Douglas Ward
Christopher Roberts
Suyan Liu
Suneeta Gollapudy
Robert Rohloff
William Gross

Recent studies indicate that spontaneous low-frequency fluctuations (LFFs) of resting-state functional magnetic resonance imaging (rs-fMRI) blood oxygen level-dependent (BOLD) signals are driven by the slow (<0. 1Hz) modulation of ongoing neuronal activity synchronized locally and across remote brain regions. How regional LFFs of the BOLD fMRI signal are altered during anesthetic-induced alteration of consciousness is not well understood. Using rs-fMRI in 15 healthy participants, we show that during administration of propofol to achieve loss of behavioral responsiveness indexing unconsciousness, the fractional amplitude of LFF (fALFF index) was reduced in comparison to wakeful baseline in the anterior frontal regions, temporal pole, hippocampus, parahippocampal gyrus, and amygdala. Such changes were absent in large areas of the motor, parietal, and sensory cortices. During light sedation characterized by the preservation of overt responsiveness and therefore consciousness, fALFF was reduced in the subcortical areas, temporal pole, medial orbital frontal cortex, cingulate cortex, and cerebellum. Between light sedation and deep sedation, fALFF was reduced primarily in the medial and dorsolateral frontal areas. The preferential reduction of LFFs in the anterior frontal regions is consistent with frontal to sensory-motor cortical disconnection and may contribute to the suppression of consciousness during general anesthesia.

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YNIMG Journal 2015 Journal Article

Nature of functional links in valuation networks differentiates impulsive behaviors between abstinent heroin-dependent subjects and nondrug-using subjects

Tianye Zhai
Yongcong Shao
Gang Chen
Enmao Ye
Lin Ma
Lubin Wang
Yu Lei
Guangyu Chen

Advanced neuroimaging studies have identified brain correlates of pathological impulsivity in a variety of neuropsychiatric disorders. However, whether and how these spatially separate and functionally integrated neural correlates collectively contribute to aberrant impulsive behaviors remains unclear. Building on recent progress in neuroeconomics toward determining a biological account of human behaviors, we employed resting-state functional MRI to characterize the nature of the links between these neural correlates and to investigate their impact on impulsivity. We demonstrated that through functional connectivity with the ventral medial prefrontal cortex, the δ-network (regions of the executive control system, such as the dorsolateral prefrontal cortex) and the β-network (regions of the reward system involved in the mesocorticolimbic pathway), jointly influence impulsivity measured by the Barratt impulsiveness scale scores. In control nondrug-using subjects, the functional link between the β- and δ-networks is balanced, and the δ-network competitively controls impulsivity. However, in abstinent heroin-dependent subjects, the link is imbalanced, with stronger β-network connectivity and weaker δ-network connectivity. The imbalanced link is associated with impulsivity, indicating that the β- and δ-networks may mutually reinforce each other in abstinent heroin-dependent subjects. These findings of an aberrant link between the β- and δ-networks in abstinent heroin-dependent subjects may shed light on the mechanism of aberrant behaviors of drug addiction and may serve as an endophenotype to mark individual subjects' self-control capacity.

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YNIMG Journal 2012 Journal Article

A clustering-based method to detect functional connectivity differences

Gang Chen
B. Douglas Ward
Chunming Xie
Wenjun Li
Guangyu Chen
Joseph S. Goveas
Piero G. Antuono
Shi-Jiang Li

Recently, resting-state functional magnetic resonance imaging (R-fMRI) has emerged as a powerful tool for investigating functional brain organization changes in a variety of neurological and psychiatric disorders. However, the current techniques may need further development to better define the reference brain networks for quantifying the functional connectivity differences between normal and diseased subject groups. In this study, we introduced a new clustering-based method that can clearly define the reference clusters. By employing group difference information to guide the clustering, the voxels within the reference clusters will have homogeneous functional connectivity changes above predefined levels. This method identified functional clusters that were significantly different between the amnestic mild cognitively impaired (aMCI) and age-matched cognitively normal (CN) subjects. The results indicated that the distribution of the clusters and their functionally disconnected regions resembled the altered memory network regions previously identified in task fMRI studies. In conclusion, the new clustering method provides an advanced approach for studying functional brain organization changes associated with brain diseases.

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YNIMG Journal 2012 Journal Article

Abnormal insula functional network is associated with episodic memory decline in amnestic mild cognitive impairment

Chunming Xie
Feng Bai
Hui Yu
Yongmei Shi
Yonggui Yuan
Gang Chen
Wenjun Li
Guangyu Chen

Abnormalities of functional connectivity in the default mode network (DMN) recently have been reported in patients with amnestic mild cognitive impairment (aMCI), Alzheimer's disease (AD) or other psychiatric diseases. As such, these abnormalities may be epiphenomena instead of playing a causal role in AD progression. To date, few studies have investigated specific brain networks, which extend beyond the DMN involved in the early AD stages, especially in aMCI. The insula is one site affected by early pathological changes in AD and is a crucial hub of the human brain networks. Currently, we explored the contribution of the insula networks to cognitive performance in aMCI patients. Thirty aMCI and 26 cognitively normal (CN) subjects participated in this study. Intrinsic connectivity of the insula networks was measured, using the resting-state functional connectivity fMRI approach. We examined the differential connectivity of insula networks between groups, and the neural correlation between the altered insula networks connectivity and the cognitive performance in aMCI patients and CN subjects, respectively. Insula subregional volumes were also investigated. AMCI subjects, when compared to CN subjects, showed significantly reduced right posterior insula volumes, cognitive deficits and disrupted intrinsic connectivity of the insula networks. Specifically, decreased intrinsic connectivity was primarily located in the frontal–parietal network and the cingulo-opercular network, including the anterior prefrontal cortex (aPFC), anterior cingulate cortex, operculum, inferior parietal cortex and precuneus. Increased intrinsic connectivity was primarily situated in the visual–auditory pathway, which included the posterior superior temporal gyrus and middle occipital gyrus. Conjunction analysis was performed; and significantly decreased intrinsic connectivity in the overlapping regions of the anterior and posterior insula networks, including the bilateral aPFC, left dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, and anterior temporal pole was found. Furthermore, the disrupted intrinsic connectivity was associated with episodic memory (EM) deficits in the aMCI patients and not in the CN subjects. These findings demonstrated that the functional integration of the insula networks plays an important role in the EM process. They provided new insight into the neural mechanism underlying the memory deficits in aMCI patients.

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