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Ying Gao

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12 papers

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EAAI Journal 2026 Journal Article

A quantum chemistry-driven machine learning model for predicting solubility of carbon dioxide in ionic liquids

Tianxiong Liu
Wenguang Zhu
Ying Gao
Runqi Zhang
Yusen Chen
Chao Guo
Hongru Zhang
Jianguang Qi

Ionic liquids (ILs) are promising eco-friendly solvents for carbon dioxide (CO2) dissolution and capture. Utilizing deep neural network modeling to accelerate the design and screening of ILs can contribute to promoting green and sustainable development. In this study, a quantitative structure-property relationship (QSPR) model was constructed to link the structure of ionic liquids with their CO2 solvation ability. The deep neural network model was driven using two environmental descriptors, temperature and pressure, as well as 16 quantum chemical descriptors calculated from the Conductor-like Screening Model for Real Solvents (COSMO-RS). This model uniquely utilizes the sparsity of IL σ-profile curves for descriptor classification. The study explored the impact on machine learning modeling using two data splitting methods: “point-based” and “component-based”. The former randomly divides the entire dataset into a training set and a test set, yielding Coefficient of Determination(R2), Root Mean Square Error(RMSE), and Mean Absolute Error(MAE) values of 0. 9904, 0. 0216, and 0. 0133, respectively, on the test set. The latter splits the dataset based on the type of ILs into set1 and set2, yielding R2, RMSE, and MAE values of 0. 9297, 0. 0631, and 0. 0450, respectively, on the test set. The model was further validated and explained using Applicability Domain (AD) and SHapley Additive exPlanations (SHAP) methods. This model provides accurate predictions of CO2 solubility in ILs and offers guidance for designing ILs for CO2 capture.

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TCS Journal 2026 Journal Article

Matchmaking encryption for NC1 circuits without obfuscation

Ying Gao
Xinrui Yang
Jie Chen
Yijian Zhang
Yu Li

Matchmaking encryption (ME) is a new form of encryption proposed by Ateniese et al. (CRYPTO, 2019). Constructing an ME scheme that supports complex functions without relying on obfuscation is an important area of research, but it has seen limited success despite significant effort. Existing ME schemes either focus on very restricted policies (i. e. , for identity matching), or require obfuscation techniques. In this paper, we propose the first ME construction that supports NC 1 circuits without using obfuscation. Our results can be summarized as follows. (1) We propose an ME scheme for NC 1 circuits from LWE and pairings, with provable security in the generic group model (GGM). (2) We further propose an ME scheme for NC 1 circuits in the standard model, by leveraging inner product functional encryption and using the KOALA knowledge assumption. Technically, we follow the blueprint of Francati et al. (Eurocrypt, 2023) but start from the two-input attribute-based encryption by Agrawal et al. (CRYPTO, 2022), which allows for a form of “linking” between two independently generated ciphertexts. In terms of security, our schemes protect the sender’s privacy, prove the authenticity of sender data, and ensure that receivers without access privileges remain uninformed about any information.

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EAAI Journal 2025 Journal Article

A strongly supervised hyperspectral unmixing framework for precise mineral composition and coal ash content estimation

Yao Cui
Ziqi Lv
Ying Gao
Yuxin Wu
Xuan Zhao
Qingxuan Meng
Jun Dong
Zhiqiang Xu

Accurate coal ash content detection is essential for advancing intelligent clean coal processing and holds significant practical value across mining, washing, combustion, and conversion technologies. This paper introduces a strongly supervised hyperspectral unmixing (SSHU) framework designed to estimate mineral composition proportions and ash content. We conducted systematic ablation experiments on concentrated coal and tailings coal datasets to evaluate the method's effectiveness and analyze the mechanisms of proportional prior information and reconstruction decoders. Results demonstrate that proportional prior information effectively constrains the proportional encoder, making estimated mineral and pure coal distributions closer to actual material distributions. The reconstruction decoder enhances the proportional encoder's feature extraction ability, guides model convergence, and improves both proportion and ash content estimation accuracy. Compared to existing hyperspectral unmixing methods, our approach incorporates pure substance spectral information during model training and combines proportional prior constraints. This provides a robust solution for complex mixture analysis and demonstrates significant potential in hyperspectral unmixing applications.

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JBHI Journal 2025 Journal Article

Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance

Wentian Cai
Yijiang Li
Yandan Chen
Jing Lin
Zihao Huang
Ping Gao
Thippa Reddy Gadekallu
Wei Wang

Histopathological whole-slide images (WSIs) segmentation is essential for precise tissue characterization in medical diagnostics. However, traditional approaches require labor-intensive pixel-level annotations. To this end, we study weakly supervised semantic segmentation (WSSS) which uses patch-level classification labels, reducing annotation efforts significantly. However, the complexity of WSIs and the challenge of sparse classification labels hinder effective dense pixel predictions. Moreover, due to the multi-label nature of WSI, existing approaches of single-label contrastive learning designed for the representation of single-category, neglecting the presence of other relevant categories and thus fail to adapt to WSI tasks. This paper presents a novel multi-label contrastive learning method for WSSS by incorporating class-specific embedding extraction with LLM features guidance. Specifically, we propose to obtain class-specific embeddings by utilizing classifier weights, followed by a dot-product-based attention fusion method that leverages LLM features to enrich their semantics, facilitating contrastive learning between different classes from single image. Besides, we propose a Robust Learning approach that leverages multi-layer features to evaluate the uncertainty of pseudo-labels, thereby mitigating the impact of noisy pseudo-labels on the learning process of segmentation. Extensive experiments have been conducted on two histopathological image segmentation datasets, i. e. LUAD dataset and BCSS dataset, demonstrating the effectiveness of our methods with leading performance.

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JBHI Journal 2025 Journal Article

Resting-State Electroencephalographic Signatures Predict Treatment Efficacy of tACS for Refractory Auditory Hallucinations in Schizophrenic Patients

Xiaojuan Wang
Ruxin Hu
Tao Wang
Yuan Chang
Xiaoya Liu
Meijuan Li
Ying Gao
Shuang Liu

Transcranial alternating current stimulation (tACS) has been reported to treat refractory auditory hallucinations in schizophrenia. Despite diligent efforts, it is imperative to underscore that tACS does not uniformly demonstrate efficacy across all patients as with all treatments currently employed in clinical practice. The study aims to find biomarkers predicting individual responses to tACS, guiding treatment decisions, and preventing healthcare resource wastage. We divided 17 schizophrenic patients with refractory auditory hallucinations into responsive(RE) and non-responsive(NR) groups based on their auditory hallucination symptom reduction rates after one month of tACS treatment. The pre-treatment resting-state electroencephalogram(rsEEG) was recorded and then computed absolute power spectral density (PSD), Hjorth parameters (HPs, Hjorth activity (HA), Hjorth mobility (HM), and Hjorth complexity (HC) included) from different frequency bands to portray the brain oscillations. The results demonstrated that statistically significant differences localized within the high gamma frequency bands of the right brain hemisphere. Immediately, we input the significant dissociable features into popular machine learning algorithms, the Cascade Forward Neural Network achieved the best recognition accuracy of 93. 87%. These findings preliminarily imply that high gamma oscillations in the right brain hemisphere may be the main influencing factor leading to different responses to tACS treatment, and incorporating rsEEG signatures could improve personalized decisions for integrating tACS in clinical treatment.

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JBHI Journal 2024 Journal Article

DAST: A Domain-Adaptive Learning Combining Spatio-Temporal Dynamic Attention for Electroencephalography Emotion Recognition

Hao Jin
Ying Gao
Tingting Wang
Ping Gao

Multimodal emotion recognition with EEG-based have become mainstream in affective computing. However, previous studies mainly focus on perceived emotions (including posture, speech or face expression et al.) of different subjects, while the lack of research on induced emotions (including video or music et al.) limited the development of two-ways emotions. To solve this problem, we propose a multimodal domain adaptive method based on EEG and music called the DAST, which uses spatio-temporal adaptive attention (STA-attention) to globally model the EEG and maps all embeddings dynamically into high-dimensionally space by adaptive space encoder (ASE). Then, adversarial training is performed with domain discriminator and ASE to learn invariant emotion representations. Furthermore, we conduct extensive experiments on the DEAP dataset, and the results show that our method can further explore the relationship between induced and perceived emotions, and provide a reliable reference for exploring the potential correlation between EEG and music stimulation.

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TMLR Journal 2024 Journal Article

Towards Understanding Adversarial Transferability in Federated Learning

Yijiang Li
Ying Gao
Haohan Wang

We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but later switching to an adversarial role. They use their data, which is part of the training set, to train a substitute model and conduct transferable adversarial attacks against the federated model. This type of attack is subtle and hard to detect because these clients initially appear to be benign. The key question we address is: How robust is the FL system to such covert attacks, especially compared to traditional centralized learning systems? We empirically show that the proposed attack imposes a high-security risk to current FL systems. By using only 3\% of the client's data, we achieve the highest attack rate of over 80\%. To further offer a full understanding of the challenges the FL system faces in transferable attacks, we provide a comprehensive analysis of the transfer robustness of FL across a spectrum of configurations. Surprisingly, FL systems show a higher level of robustness than their centralized counterparts, especially when both systems are equally good at handling regular, non-malicious data. We attribute this increased robustness to two main factors: 1) Decentralized Data Training: Each client trains the model on its own data, reducing the overall impact of any single malicious client. 2) Model Update Averaging: The updates from each client are averaged together, further diluting any malicious alterations. Both practical experiments and theoretical analyses support our conclusions. This research not only sheds light on the resilience of FL systems against hidden attacks but also raises important considerations for their future application and development。

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AAAI Conference 2023 Conference Paper

SeDepTTS: Enhancing the Naturalness via Semantic Dependency and Local Convolution for Text-to-Speech Synthesis

Chenglong Jiang
Ying Gao
Wing W.Y. Ng
Jiyong Zhou
Jinghui Zhong
Hongzhong Zhen

Self-attention-based networks have obtained impressive performance in parallel training and global context modeling. However, it is weak in local dependency capturing, especially for data with strong local correlations such as utterances. Therefore, we will mine linguistic information of the original text based on a semantic dependency and the semantic relationship between nodes is regarded as prior knowledge to revise the distribution of self-attention. On the other hand, given the strong correlation between input characters, we introduce a one-dimensional (1-D) convolution neural network (CNN) producing query(Q) and value(V) in the self-attention mechanism for a better fusion of local contextual information. Then, we migrate this variant of the self-attention networks to speech synthesis tasks and propose a non-autoregressive (NAR) neural Text-to-Speech (TTS): SeDepTTS. Experimental results show that our model yields good performance in speech synthesis. Specifically, the proposed method yields significant improvement for the processing of pause, stress, and intonation in speech.

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JBHI Journal 2022 Journal Article

DMCGNet: A Novel Network for Medical Image Segmentation With Dense Self-Mimic and Channel Grouping Mechanism

Linsen Xie
Wentian Cai
Ying Gao

Automatic Medical Image Segmentation (MIS) can assist doctors by reducing labor and providing a unified standard. Nowadays, approaches based on Deep Learning have become mainstream for MIS because of their ability of automatic feature extraction. However, due to the plain network design and targets variety in medical images, the semantic features can hardly be extracted adequately. In this work, we propose a novel Dense Self-Mimic and Channel Grouping based Network (DMCGNet) for MIS for better feature extraction. Specifically, we introduce a Pyramid Target-aware Dense Self Mimic (PTDSM) module, which is capable of exploring deeper and better feature representation with no parameter increase. Then, to utilize features efficiently, an effective Channel Split based Feature Fusion Module (CSFFM) is proposed for feature reuse, which strengthens the adaptation of multi-scale targets by utilizing the channel grouping mechanism. Finally, to train the proposed method adequately, Deep Supervision with Group Ensemble Learning (DSGEL) is equipped to the network. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on 4 medical image segmentation datasets.

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JBHI Journal 2021 Journal Article

Comparative Study of COVID-19 Pandemic Progressions in 175 Regions in Australia, Canada, Italy, Japan, Spain, U.K. and USA Using a Novel Model That Considers Testing Capacity and Deficiency in Confirming Infected Cases

Choujun Zhan
Chi K. Tse
Ying Gao
Tianyong Hao

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4. 309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.

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YNICL Journal 2021 Journal Article

Uncinate fasciculus and its cortical terminals in aphasia after subcortical stroke: A multi-modal MRI study

Binlong Zhang
Jingling Chang
Joel Park
Zhongjian Tan
Lu Tang
Tianli Lyu
Yi Han
Ruiwen Fan

Aphasia, one of the most common cognitive impairments after stroke, is commonly considered to be a cortical deficit. However, many studies have reported cases of post subcortical stroke aphasia (PSSA). The pathology and recovery mechanism of PSSA remain unclear. This study aimed to investigate PSSA mechanism through a multimodal magnetic resonance imaging (MRI) approach and a two-session study design (baseline and one month after treatment). Thirty-six PSSA patients and twenty-four matched healthy controls (HC) were included. All patients had subcortical infarctions involving left subcortical white matter for 1 to 6 months. The patients underwent MRI scan and Western Aphasia Battery (WAB) examination before and after one month's comprehensive treatment. Region-wise lesion-symptom mapping (RLSM), tractography, fractional anisotropy (FA), and amplitude of low-frequency fluctuations (ALFF) analysis were conducted. After MRI preprocessing and exclusion, FA analysis included 35 patients pre-treatment and 16 patients post-treatment. ALFF analysis included 30 patients pre-treatment and 14 patients post-treatment. We found: 1) the amount of damage in the left uncinate fasciculus (UF) was associated with WAB aphasia quotient (AQ); 2) the left UF FA and left temporal pole (TP) ALFF were decreased and positively correlated with WAB-AQ, spontaneous speech, and naming in PSSA patients; and 3) PSSA patients showed increased left TP ALFF when their language ability recovered after treatment. The left TP ALFF change was positively correlated with AQ change. Our results demonstrate the importance of left UF and left TP (one of the cortical terminals of the left UF) in PSSA pathology and recovery. These results may further provide support for the disconnection theory in the mechanism of PSSA.

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YNIMG Journal 2014 Journal Article

Lack of dystrophin results in abnormal cerebral diffusion and perfusion in vivo

Candida L. Goodnough
Ying Gao
Xin Li
Mohammed Q. Qutaish
L. Henry Goodnough
Joseph Molter
David Wilson
Chris A. Flask

Dystrophin, the main component of the dystrophin–glycoprotein complex, plays an important role in maintaining the structural integrity of cells. It is also involved in the formation of the blood–brain barrier (BBB). To elucidate the impact of dystrophin disruption in vivo, we characterized changes in cerebral perfusion and diffusion in dystrophin-deficient mice (mdx) by magnetic resonance imaging (MRI). Arterial spin labeling (ASL) and diffusion-weighted MRI (DWI) studies were performed on 2-month-old and 10-month-old mdx mice and their age-matched wild-type controls (WT). The imaging results were correlated with Evan's blue extravasation and vascular density studies. The results show that dystrophin disruption significantly decreased the mean cerebral diffusivity in both 2-month-old (7. 38±0. 30×10-4 mm2/s) and 10-month-old (6. 93±0. 53×10-4 mm2/s) mdx mice as compared to WT (8. 49±0. 24×10-4, 8. 24±0. 25×10-4 mm2/s, respectively). There was also an 18% decrease in cerebral perfusion in 10-month-old mdx mice as compared to WT, which was associated with enhanced arteriogenesis. The reduction in water diffusivity in mdx mice is likely due to an increase in cerebral edema or the existence of large molecules in the extracellular space from a leaky BBB. The observation of decreased perfusion in the setting of enhanced arteriogenesis may be caused by an increase of intracranial pressure from cerebral edema. This study demonstrates the defects in water handling at the BBB and consequently, abnormal perfusion associated with the absence of dystrophin.

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