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Zhong Yang

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4 papers

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AAAI Conference 2026 Conference Paper

SGP4SR: Separated-Modality Guided User Preference Learning for Multimodal Sequential Recommendation

Changhong Li
Zhiqiang Guo
Guohui Li
Zhong Yang
Chuhang Hong

With the booming development of multimodal data (e.g., image, text) on internet platforms, multimodal sequential recommendation methods continue to emerge. Most existing methods incorporate item modal features as auxiliary information, typically concatenating them to learn unified user representations. However, these methods directly use modal features for representation learning, neglecting the impact of inherent modal noise. We argue that internal-modal noise and cross-modal noise hinder the acquisition of more accurate user representations. To address this problem, we propose SGP4SR - Separated-modality Guided user Preference learning for multimodal Sequential Recommendation. Globally, the user preference modeling is carried out from a separated-modality perspective to alleviate cross-modal noise. Locally, for each individual modality, we use item relationship graphs and user interest centers, aggregated with ID embeddings, to replace direct modal features, thereby mitigating internal-modal noise. Finally, user representations from both separated-modality and multimodal perspectives participate in prediction independently. In experiments conducted on four real-world datasets, our method outperforms state-of-the-art approaches, achieving an average performance improvement of up to 8.84% over the best baseline. The comprehensive experiments further validate the superior noise tolerance and robustness of our method.

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YNIMG Journal 2025 Journal Article

Neural mechanisms of fairness decision-making: An EEG comparative study on opportunity equity and outcome equity

Qi Li
Ya Zheng
Jing Xiao
Kesong Hu
Zhong Yang

Social equity consists of opportunity equity and outcome equity, where outcome equity refers to the equitable distribution of resource, while opportunity equity refers to equivalent sets of opportunities to obtain a satisfactory outcome, ensuring equality in expected payoffs rather than the actual payoffs. Previous studies showed the existence of inequity aversion for opportunity inequality and identified some differences between opportunity equity and outcome equity in the behavior pattern of evaluation and reaction processes. However, the commonalities and distinctions in brain activity during the fairness decision-making of opportunity equity and outcome equity remain unclear. Our study used a modified version of the ultimatum game (UG) and the classic UG, and recorded electroencephalogram (EEG) data to investigate underlying neural mechanisms of fairness decision-making of opportunity equity and outcome equity. The EEG results revealed that both shared the same components of the P300 and delta bands associated with reward processing. Compared to outcome equity, opportunity equity did not identify conflict-related medial frontal negativity (MFN) and theta bands, but showed differences in the P2 and beta bands. Moreover, we used a computational modeling approach to estimate the utility for each trial, and found that larger P2 amplitudes were associated with lower utility in opportunity distribution, while larger P300 amplitudes were associated with higher utility in outcome distribution. Our findings provide electrophysiological evidence for the existence of opportunity equity and shed light on the cognitive processing disparities between opportunity equity and outcome equity. These results not only validate and expand the theory of inequality aversion but also empirically support the targeted resolution of social inequalities in various contexts.

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ICRA Conference 2019 Conference Paper

ChevBot - An Untethered Microrobot Powered by Laser for Microfactory Applications

Ruoshi Zhang
Andriy Sherehiy
Zhong Yang
Danming Wei
Cindy K. Harnett
Dan O. Popa

In this paper, we introduce a new class of submillimeter robot (ChevBot) for microfactory applications in dry environments, powered by a 532 nm laser beam. ChevBot is an untethered microrobot propelled by a thermal Micro Electro Mechanical (MEMS) actuator upon exposure to the laser light. Novel models for opto-thermal-mechanical energy conversion are proposed to describe the microrobot's locomotion mechanism. First, an opto-thermal simulation model is presented which is experimentally validated with static displacement measurements with microrobots tethered to the substrate. Then, stick and slip motion of the microrobot was predicted using a dynamic extension of our simulation model, and experiments were conducted to validate this model in one dimension. Promising microrobot designs were fabricated on a silicon on insulator (SOI) wafer with 20 μm device layer and a dimple was assembled at the bottom to initiate directional locomotion on a silicon substrate. Validation experiments demonstrate that exposure to laser power below 2W and repetition frequencies below 60 kHz can generate actuator displacements of a few microns, and 46 μm/s locomotion velocity.

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YNIMG Journal 2016 Journal Article

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia

Zirui Huang
Jun Zhang
Jinsong Wu
Pengmin Qin
Xuehai Wu
Zhiyao Wang
Rui Dai
Yuan Li

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which “couple” the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

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