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Ying Liu

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32 papers

2 author rows

EAAI Journal 2026 Journal Article

A collaborative approach based on large language model and knowledge graphs for information integration towards smart manufacturing

Ruihao Li
Chong Chen
Ying Liu
Tao Wang
Haidong Shao
Lianglun Cheng

In the era of smart manufacturing, integrating vast amounts of information has become an essential task. Knowledge Graph (KG) is a key technology for improving information integration, which can greatly improve the performance of question-answering for Large Language Models (LLMs). However, the existing approach mainly adopts KG as the plug-in database for Retrieval-Augmented Generation (RAG), which cannot achieve accurate answering due to the imperfections of KG. In order to address this challenge, a collaborative LLM-KG framework is proposed to iteratively update the KG, which can provide fine-grained knowledge for RAG. The methodology firstly constructs a foundational ontology, and adopts LLM for knowledge triples extraction to establish an initial KG based on multi-source data. Then, competency questions (CQs) are designed for the evaluation and optimization of the initial KG. After ontology optimization, a fine-grained KG is obtained to facilitate a robust question-answering mechanism through RAG. The proposed iterative approach can effectively refine the system's decision-support capabilities. An experimental study based on the real-world shipbuilding process data is implemented. The experimental results demonstrate that the answering accuracy can be improved from 86.18% to 93.09% with the enhancement of the proposed approach.

YNIMG Journal 2026 Journal Article

Enhanced visual and auditory inhibitory control in musicians: EEG evidence

Ying Liu
Jiarui Ma
Jing Ning
Jiejia Chen

Musicians' perceptual advantages are often concentrated in the auditory rather than the visual modality. However, whether the relationship between musical training and the core cognitive ability of inhibitory control exhibits auditory modality specificity remains unclear. To address this gap, the present study employed matched visual and auditory Go/No-go tasks combined with electroencephalography (EEG) to compare differences in inhibitory control between university students with long-term musical training (musical training group) and their untrained peers (control group). The results showed that in both visual and auditory inhibitory control tasks, even after controlling for eight potential confounding variables including age, socioeconomic status, IQ, and the Big Five personality traits, the musical training group not only demonstrated behavioral advantages (higher d' scores) but also exhibited enhanced neural activity during conflict monitoring (smaller N2 amplitudes and increased theta power) and motor inhibition (larger P3 amplitudes). These findings suggest a modality-general effect in the relationship between musical training and enhanced inhibitory control. Meanwhile, the musically trained group showed specific advantages only in the early processing stage of auditory stimuli, reflecting the potential strengthening effect of the auditory system brought about by musical experience. This study is the first to reveal, from a dual-modality perspective, the relationship between musical training and cross-modal inhibitory control. It contributes to our understanding of the cognitive mechanisms associated with musical training and provides empirical evidence for its potential applications in music therapy and education.

YNIMG Journal 2026 Journal Article

Gestational age-specific DTI templates of the neonatal brain: Application in preterm developmental study

Xiaochen Jiang
Mengyi Wang
Ying Liu
Tianhao Zhang
Guangjuan Mao
Qi Zhou
Shilun Zhao
Baoci Shan

Due to significant differences in brain volume, morphology, and white matter integrity among neonates of varying gestational ages, using a single full-term template for preterm analysis inevitably introduces analytical errors. To address this, we aimed to develop gestational-age-specific stereotaxic DTI templates using retrospective diffusion MRI scans from 161 neonates acquired between August 2021 and January 2024. The cohort was stratified into four WHO-defined subgroups: extremely preterm (n = 31), very preterm (n = 29), moderate to late preterm (n = 28), and full-term (n = 73). Templates were constructed via iterative registration, with corresponding atlases transformed from JHU space and manually corrected. Quantitative evaluation using the Jacobian determinant and standard deviation revealed that our age-specific templates demonstrated significantly lower deformation magnitude and registration error compared to a standard full-term template. When applied to investigate developmental differences, we observed progressively more extensive fractional anisotropy reductions from moderate-to-late to extremely preterm neonates. Notably, commissural fibers, particularly the corpus callosum body (0.194 ± 0.005 in extremely preterm vs. 0.230 ± 0.003 in full-term, p < 0.001), exhibited significant developmental gradients. Consequently, these constructed gestational-age-specific DTI templates offer a robust tool to improve the accuracy of morbidity risk predictions and facilitate multicenter studies of preterm neonates.

YNIMG Journal 2026 Journal Article

Hierarchical neurobehavioral model reveals that shared flexibility, not individual stability, supports rhythmic coordination

Ruoyu Niu
Yanan Li
Lei Liu
Yafeng Pan
Ying Liu

Interpersonal coordination requires balancing individual control with interaction-derived synergy, yet it remains unclear when neural coupling contributes beyond behavior. Using an fNIRS hyperscanning paradigm, we examined dyadic rhythmic coordination and jointly modeled behavioral stability, dispositional structure, and interbrain synchrony within a hierarchical neurobehavioral framework. Across models, mean individual stability was negatively associated with dyadic performance, whereas interaction-derived shared flexibility (i.e., dyad-level behavioral stability synergy) was the most robust positive predictor. Incorporating dispositional structure showed that larger within-dyad differences in figure-embedding performance impaired coordination, whereas higher dyad-level self-esteem facilitated coordination. The neural coupling index showed no reliable main effect after accounting for behavioral and trait factors, but moderation analyses indicated a conditional contribution: interbrain synchrony compensated when shared flexibility was low, with diminishing benefit as synergy increased. Together, these findings support a hierarchical neurobehavioral architecture in which shared flexibility provides the primary foundation of coordination, dispositional structure shapes the conditions for synergy, and interbrain synchrony contributes in a context-dependent manner.

AAAI Conference 2026 Conference Paper

PointSLAM++: Robust Dense Neural Gaussian Point Cloud-based SLAM

Xu Wang
Boyao Han
Xiaojun Chen
Ying Liu
Ruihui Li

Real-time 3D reconstruction is crucial for robotics and augmented reality, yet current simultaneous localization and mapping(SLAM) approaches often struggle to maintain structural consistency and robust pose estimation in the presence of depth noise. This work introduces PointSLAM++, a novel RGB-D SLAM system that leverages a hierarchically constrained neural Gaussian representation to preserve structural relationships while generating Gaussian primitives for scene mapping. It also employs progressive pose optimization to mitigate depth sensor noise, significantly enhancing localization accuracy. Furthermore, it utilizes a dynamic neural representation graph that adjusts the distribution of Gaussian nodes based on local geometric complexity, enabling the map to adapt to intricate scene details in real time. This combination yields high-precision 3D mapping and photorealistic scene rendering. Experimental results show PointSLAM++ outperforms existing 3DGS-based SLAM methods in reconstruction accuracy and rendering quality, demonstrating its advantages for large-scale AR and robotics.

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AAAI Conference 2026 Conference Paper

Sparse-Scale Transformer with Bidirectional Awareness for Time Series Forecasting

Ying Liu
Bo Liu
Sheng Huang
Gang Luo
Wenbo Hu
Meng Wang
Richang Hong

Time series forecasting (TSF) plays a crucial role in many real-world applications, such as weather prediction and economic planning. While Transformer-based models have shown strong capabilities in modeling long-range dependencies, effectively capturing the multi-scale temporal dynamics inherent in time series remains a major challenge. Existing methods often adopt time-windows of varying sizes, which may introduce noisy or irrelevant representations when mismatched with the underlying temporal patterns, potentially leading to overfitting. In this paper, we propose Sparse-Scale Transformer (SSformer) with Bidirectional Awareness for Time Series Forecasting to enhance the multi-scale modeling for time series. Specifically, we propose a novel Sparse-Scale Convolution (SSC) block that imposes sparsity on scales to obtain the informative representations by evaluating the intra-scale segment similarity of time series, and utilizes scale-specific convolutions to extract local patterns. Furthermore, we design a Bidirectional-Scale Interaction (BSI) block to explicitly model scale correlations in both coarse-to-fine and fine-to-coarse directions. Finally, scale predictions are ensembled to fully exploit the complementary forecasting capabilities across scales. Extensive experiments on various real-world datasets demonstrate that SSformer achieves state-of-the-art performance with superior efficiency.

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EAAI Journal 2025 Journal Article

Cross-database facial expression recognition based on Multi-feature Representation Multi-layer Domain Adaptive Fusion Network

Jingjie Yan
Chengkun Du
Biao Li
Xiaoyang Zhou
Ying Liu

In order to extract richer comprehensive facial expression features and reduce domain distribution differences in cross-database experiments, this paper proposes a Multi-feature Representation Multi-layer Domain Adaptive Fusion Network (MRMDAFNet) and applies it to cross-database facial expression recognition research. MRMDAFNet extracts three different types of features including residual network features, Haar features, and Graph Convolutional Network features. Residual network features represent deep feature information, Haar features represent local contrast information, and Graph Convolutional Network features represent topological structure information. The three types of features are aligned between the source domain and the target domain through the application of sub-domain adaptation. Moreover, MRMDAFNet integrates these features using a multi-head attention mechanism and employs global adaptation to align features, and further presents a Multi-layer Domain Adaptation module to achieve domain adaptation at different levels. A variety of transfer learning tasks and extensive cross-database facial expression recognition experiments are implemented on the Extended Cohn-Kanade dataset (CK+), Facial Expression Recognition 2013 dataset (FER2013), and Real-world Affective Faces database (RAF). The experimental results demonstrate the effectiveness of MRMDAFNet, achieving accuracies of 73. 58%, 56. 59%, and 63. 25% in cross-database facial expression recognition experiments with the three databases as target domains respectively. The proposed MRMDAFNet model may be applicable to intelligent human–computer interaction in artificial intelligence.

ICRA Conference 2025 Conference Paper

Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark

Ying Liu
Yijing Hua
Haojiang Chai
Yanbo Wang
TengQi Ye

Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary data used for open-vocabulary learning can lead to unfair and unreliable evaluations. Recent evaluation methods have attempted to address this issue by incorporating object properties or adding locations and characteristics to the captions. Nevertheless, since these properties and locations depend on the specific details of the images instead of classes, detectors can not make accurate predictions without precise descriptions provided through human annotation. This paper introduces 3F-OVD, a novel task that extends supervised fine-grained object detection to the open-vocabulary setting. Our task is intuitive and challenging, requiring a deep understanding of Fine-grained captions and careful attention to Fine-grained details in images in order to accurately detect Fine-grained objects. Additionally, due to the scarcity of qualified fine-grained object detection datasets, we have created a new dataset, NEU-171K, tailored for both supervised and open-vocabulary settings. We benchmark state-of-the-art object detectors on our dataset for both settings. Furthermore, we propose a simple yet effective post-processing technique. Our data, annotations and codes are available at https://github.com/tengerye/3FOVD.

JBHI Journal 2025 Journal Article

Image-enhanced Multi-Modal Contrastive Transformer for subcellular spatial transcriptomics

Wanwan Shi
Ying Liu
Qiu Xiao
Yuting Bai
Xiao Liang
Xinling Zeng
Chee Keong Kwoh
Jiawei Luo

Recent advances in spatial molecular imaging technologies have enabled gene expression profiling alongside high-resolution imaging, providing unprece dented opportunities to resolve molecular heterogeneity at subcellular resolution. However, these technologies fail to fully capture cellular characteristics due to the limited number of genes they can detect, which hinderdownstream analysis. Spatial imaging data provide high-resolution and fine-grained morphology information, developing computational methods that effectively integrate image features with transcriptomic profiles is crucial for enabling comprehensive subcellular data analysis. In this study, we present SIMMT, an image-enhanced multi-modal contrastivetrans former framework for identifying spatial domains and en hancing subcellular data. In the framework, we design a dual transformer architecture to learn multi-modal representations for cells by modeling transcriptomics and morphological images respectively. To fully capture modality interactions within spatial contexts, we introduce a contrastive learning module that enhances cell representation by aligning tissue morphology and gene expression at the cell level. We tested SIMMT on subcellular spatial transcriptomics datasets from human lung cancer tissue, mouse brain tissue, human colorectal cancer tissue, and human ovarian cancer tissue. The results demonstrated that SIMMT consistently outperformed state-of-the-art methods in spatial clustering and gene expression pattern analysis. Our method also effectively demonstrated its ability to identify tumor spatial heterogeneity and uncover potential gene biomarkers in the human bronchiolar adenoma (BA) dataset. The code and dataset of SIMMT can be downloaded from https://github.com/LWanzi/SIMMT

YNIMG Journal 2025 Journal Article

Impaired glymphatic transport in hypoxic-ischemic encephalopathy

Jieyi Shen
Ying Yang
Fangfang Chen
Yang Zuo
Yidong Yang
Wei Wei
Ying Liu
Jie Wen

Hypoxic-ischemic encephalopathy (HIE) is a major cause of neonatal brain injury. The glymphatic system aids in waste clearance via perivascular pathways and is crucial in maintaining brain functions. While studies have shown that diseases such as stroke and traumatic brain injury disrupt glymphatic function, the impact of HIE on this system remains largely unexplored. We utilized an HIE mouse model with dynamic contrast-enhanced MRI (DCE-MRI) to conduct both qualitative and quantitative assessment of glymphatic transports dysfunction in different brain regions. Fluorescent cerebrospinal fluid (CSF) tracers were used to investigate the effects of HIE on glymphatic system development. Mice brain sections were subjected to Aquaporin-4 (AQP4) immunohistochemical staining, allowing for detailed morphological assessment of AQP4 polarization in affected brain regions. HIE mice exhibited delayed glymphatic transport dynamics, with prolonged time-to-peak tracer enhancement and increased retention in olfactory bulb, basal forebrain, and hypothalamus regions. Quantitative kinetic analysis showed significant reductions in Kf (CSF-to-perivascular space transfer constants) and Ks (perivascular-to-parenchyma transfer constants), alongside elevated Vf (perivascular volume fractions) across cortical and subcortical structures. Fluorescent CSF tracer analysis indicates that HIE impaired glymphatic system maturation in neonatal mice. This impairment progressed to persistent glymphatic dysfunction. Histologically validated via immunofluorescence, HIE-induced astrocytic AQP4 mis-polarization directly correlates with glymphatic transport dysfunction, underscoring AQP4′s critical role in glymphatic system integrity. Our multimodal imaging study combining DCE-MRI and CSF tracer analysis indicates that HIE can cause regional impairments of glymphatic function and adversely affect brain development.

AAAI Conference 2025 Conference Paper

Iterative Self-Training with Class-Aware Text-to-Image Synthesis for Visual Task Learning

Xiang Zhang
Wanqing Zhao
Pengyang Li
Ying Liu
Hangzai Luo
Sheng Zhong
Jinye Peng
Jianping Fan

Generative models are widely used to produce synthetic images with annotations, alleviating the burden of image collection and annotation for training deep visual models. However, challenges such as limited image diversity, noisy pseudo labels, and domain gaps between synthetic and real images often undermine their effectiveness in downstream visual tasks. This paper introduces the Iterative Self-Training with Class-Aware Text-to-Image Synthesis (IST-CATS) framework, which addresses these challenges by integrating a class-aware text-to-image synthesis (CATS) component with an iterative self-training (IST) strategy. CATS innovatively introduces a class-aware chain approach to generate detailed descriptions. These descriptions act as prompts for a diffusion model, enabling the creation of a diverse of images accompanied by distinguishable objects against the background. The generated images can be easily pseudo-labeled by an unsupervised instance segmentation method, and then noisy pseudo labels can be effectively purified by a novel feature similarity-based filtering mechanism. The generated images underpin our IST, which progressively enhances vision models and refines pseudo labels through self-training and our proposed label filtering strategy (LabFilt). LabFilt meticulously improves the quality of pseudo labels by employing class-adaptive techniques at both the pixel and object levels, ensuring refined pseudo-label accuracy. IST-CATS demonstrates superior performance in object detection and semantic segmentation compared to traditional synthetic and semi/weakly-supervised methods, effectively addressing data collection and annotation challenges.

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AAAI Conference 2025 Conference Paper

Semi-Implicit Neural Ordinary Differential Equations

Hong Zhang
Ying Liu
Romit Maulik

Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine learning. We present a semi-implicit neural ODE approach that exploits the partitionable structure of the underlying dynamics. Our technique leads to an implicit neural network with significant computational advantages over existing approaches because of enhanced stability and efficient linear solves during time integration. We show that our approach outperforms existing approaches on a variety of applications including graph classification and learning complex dynamical systems. We also demonstrate that our approach can train challenging neural ODEs where both explicit methods and fully implicit methods are intractable.

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YNIMG Journal 2025 Journal Article

Uncovering the neural basis of risk preferences in cooperative Dyads: A fNIRS study

Qianlan Yin
Jing Wen
Shuo Chen
Tianya Hou
Ying Liu
Danni Yang
Guorui Liu
Peiqi Shi

BACKGROUND: Individuals' risk preferences have been shown to influence their decision-making in various contexts. However, the neural mechanisms underlying the relationship between risk preference and decision-making in a social setting remain unclear. This study utilized functional near-infrared spectroscopy (fNIRS) to investigate the neural correlates of dyadic decision-making under risk and the modulating effect of individual risk preference. METHOD: This study examined the impact of risk preference on group decision-making using a two-phase experimental design. Based on G-power software calculations, 168 right-handed participants (62 males, 106 females, mean age 21.26±1.70) were recruited. Participants first completed a single-player Sequential Risk Task to measure risk preference, followed by group classification into three groups: Risky&Risky, Risky&Safe, and Safe&Safe. Task performance and decision-making behavior were recorded. Functional Near-Infrared Spectroscopy (fNIRS) was employed to measure cortical activation in the prefrontal cortex, focusing on inter-brain synchrony and coupling directionality using wavelet coherence and Granger causality(GC) analyses. Data were preprocessed to remove noise, and statistical analyses included repeated measures ANOVAs, Support Vector Regression and multiple regression analyses. RESULTS: = 0.173 and 0.191). CONCLUSION: This study employed fNIRS hyperscanning to investigate how individual differences in risk preference impact decision-making in dyadic contexts. The results indicated that variations in connectivity and information transfer between the orbitofrontal and medial prefrontal cortices underlie the distinct risk-taking behaviors exhibited by dyadic pairs. These findings underscore the pivotal role of affective and cognitive control mechanisms and individual risk personality traits in cooperative decision-making under conditions of uncertainty.

AIIM Journal 2024 Journal Article

A few-shot disease diagnosis decision making model based on meta-learning for general practice

Qianghua Liu
Yu Tian
Tianshu Zhou
Kewei Lyu
Ran Xin
Yong Shang
Ying Liu
Jingjing Ren

Background Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the “gatekeepers” of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. Methods and materials In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n -way, k -shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n × k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. Result Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90. 02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29. 13 % and 21. 63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. Conclusion The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.

JBHI Journal 2024 Journal Article

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

Qianghua Liu
Yu Tian
Tianshu Zhou
Kewei Lyu
Zhixiao Wang
Yixiao Zheng
Ying Liu
Jingjing Ren

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients’ electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0. 7873, recall@10 of 0. 9020 and hits@10 of 0. 9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

EAAI Journal 2024 Journal Article

Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot

Chong Chen
Tao Wang
Kaijie Lu
Ying Liu
Lianglun Cheng

The safe operation of Industrial robots is a major concern in intelligent manufacturing. Accurate compound fault diagnosis is essential to the safe operation of industrial robots, while it is challenging to achieve since the compound fault samples are hard to be collected. Generative adversarial network (GAN) is a useful tool for addressing the data imbalance issue. However, the computation efficiency of GAN in addressing the data imbalance issue has not been investigated. Hence, this study proposes a lightweight GAN named compact convolutional Transformers-GAN (CCT-GAN) to alleviate the data imbalance issue in compound fault diagnosis modelling. Firstly, the feedback current signals collected from the industrial robot are transformed into time-frequency images via continuous wavelet transformation (CWT). Secondly, CCT-GAN is designed to achieve high-quality fake data generation and compound fault diagnosis modelling without large computational costs. Thirdly, the relation between a single fault and the compound fault is considered in the compound fault diagnosis modelling via multi-hot representation to alleviate the data imbalance issue. An experimental study based on the real-world compound fault dataset of industrial robots reveals that the proposed CCT-GAN shows merits in compound fault diagnosis modelling in comparison with the prevailing algorithms. The results indicate that CCT-GAN can performance of compound fault diagnosis when only 100 data samples from each compound fault category are available.

YNICL Journal 2024 Journal Article

Quantitative comparison of CSVD imaging markers between patients with possible amyloid small vessel disease and with non-amyloid small vessel disease

Chun-Qiang Lu
Ying Liu
Jia-Rong Huang
Meng-Shuang Li
Yan-Shuang Wang
Yan Gu
Di Chang

The spatial distribution patterns of cerebral microbleeds are associated with different types of cerebral small vessel disease (CSVD). This study aims to examine the disparities in brain imaging markers of CSVD among patients diagnosed with possible amyloid and non-amyloid small vessel disease. The head MR scans including susceptibility-weighted imaging (SWI) sequences from 351 patients at our institute were collected for analysis. CSVD imaging markers were quantified or graded across various CSVD dimensions in the patient images. Patients were categorized into the cerebral amyloid angiopathy group (CAA), hypertensive arteriopathy group (HA), or mixed small vessel disease group (Mixed), based on the spatial distribution of microbleeds. White matter lesions (WML) were segmented using an artificial neural network and assessed via a voxel-wise approach. Significant differences were observed among the three groups in several indices: microbleed count, lacune count at the centrum semiovale and basal ganglia levels, grade of enlarged perivascular space (EPVS) at the basal ganglia, and white matter lesion volume. These indices were substantially higher in the Mixed group compared to the other groups. Additionally, the incidences of cerebral hemorrhages (χ2 = 7.659, P = 0.006) and recent small subcortical infarcts (χ2 = 4.660, P = 0.031) were significantly more frequent in the HA group than in the CAA group. These results indicate that mixed spatial distribution patterns of microbleeds demonstrated the highest burden of cerebral small vessel disease. Microbleeds located in the deep brain regions were associated with a higher incidence of recent small subcortical infarcts and cerebral hemorrhages compared to those in the cortical areas.

EAAI Journal 2024 Journal Article

Recent advances in Transformer technology for agriculture: A comprehensive survey

Weijun Xie
Maocheng Zhao
Ying Liu
Deyong Yang
Kai Huang
Chenlong Fan
Zhandong Wang

Intelligent agriculture is critical for guiding agricultural production and enhancing efficiency through early disease diagnosis, yield estimation, automatic harvest, and postharvest efficient treatment. The conventional methods, including manual, image processing, and CNN (convolutional neural network), have some shortcomings of high labor consumption, subjectivity, poor robustness, and low efficiency. Transformer, one of the latest technological advances in deep learning, has gained widespread adoption in agriculture since its universal modeling capabilities. This paper is the first comprehensive survey of the recent advancements in Transformer-based models within the agricultural domain. Six research questions are proposed and addressed by reviewing relevant literature from different aspects. Two types of Transformer-based models (pure and hybrid Transformers) are reviewed to outline the architecture of Transformer-based models adopted in agriculture. And different applications of Transformer-based models in agriculture are summarized to display the current development of Transformer in agriculture. It also highlights the main challenges faced by Transformer technology in agriculture and discusses the future directions for its application in agricultural sector. This survey is expected to leave readers with deeper thoughts about Transformer-based models in agriculture and help them perform in-deep explorations on Transformer-based models for agricultural applications.

YNIMG Journal 2022 Journal Article

No smoking signs with strong smoking symbols induce weak cravings: an fMRI and EEG study

Wanwan Lü
Qichao Wu
Ying Liu
Ying Wang
Zhengde Wei
Yu Li
Chuan Fan
An-Li Wang

No smoking signs (NSSs) that combine smoking symbols (SSs) and prohibition symbols (PSs) represent common examples of reward and prohibition competition. To evaluate how SSs within NSSs influence their effectiveness in guiding reward vs. prohibition, we studied 93 male smokers. We collected self-reported craving ratings (N=30), cue reactivity under fMRI/EEG (N=33), and smoking-behavior anticipation for paired NSSs and SSs (N=30). We found that NSS-induced cravings were negatively correlated with SS-induced cravings and PS-induced inhibition. fMRI indicated that both correlations were mediated by activation of the inferior frontal gyrus and precuneus, suggesting that the effects of SSs and PSs interact with each other. EEG revealed that the prohibition response occurs after the cigarette response, indicating that the cigarette response might be precluded by the prohibition, supporting the effect of SSs in discouraging smoking. Moreover, stronger SSs induced stronger slow positive waves and late positive potentials, and the stronger the late positive potentials, the stronger the late positive potentials. Both the amplitudes of late positive potentials and slow positive waves were positively correlated with the amplitude of N2, which was positively correlated with the attention grabbed score by the NSS. In addition, the weaker the NSS-induced craving, the greater the smoking behavior anticipation reduction, indicating the capability of NSSs to decrease smoking behavior. Our study provides empirical evidence for selecting the most effective NSSs: those combining strong SS and PS, offering insights about competition between cigarette reward and prohibition and providing neural evidence on how cigarette reward and prohibition interact.

ICRA Conference 2021 Conference Paper

Elevation control of a soft jumping robot

Huimin Chen
Jiaming Liang
Zicong Miao
Guo Zhou
Ying Liu
Min Zhang 0031

Jumping with controllable elevation is significant for insect-scale robots to improve terrain adaptability and to escape from risks. However, jumping robots based on soft materials with low stiffness cannot transmit displacement precisely, exhibiting poor control of jumping. Here, we propose a modified two-bars catapult mechanism combined with an asynchronous sequential releasing strategy to realize elevation controllable jumping. In this work, an 80 mg prototype robot, 56 (long) × 29 mm (wide) × 3 mm (high) mm in size, is designed with the controllable elevation range from 63° to 112°. The soft robot is mainly composed of a shape memory alloy actuator and four electrostatic pads acting as the lock/release structures. Elevation control is realized by asynchronously releasing the electrostatic pads in a small time interval (about 10 ms). A maximum jump height of 62 mm and a maximum half-distance of 41 mm are also achieved.

YNIMG Journal 2019 Journal Article

Distinct neuronal entrainment to beat and meter: Revealed by simultaneous EEG-fMRI

Qiang Li
Guangyuan Liu
Dongtao Wei
Ying Liu
Guangjie Yuan
Gaoyuan Wang

Rhythm perception refers to the mental interpretation of rhythm by a listener. Musical rhythm perception typically involves two steps: beat extraction and metrical structure assignment (meter perception). The entrainment theories propose that different neuronal oscillations entrain to different levels of metrical structure in the rhythm (e. g. , beat and meter) and thereby form a representation of the rhythm in the mind. Thus, neuronal populations that entrain to beat and meter should theoretically be different. However, although entrainment theories have been supported by many studies, the neuronal populations that entrain to beat and meter remain largely unknown. In this study, we used a paradigm to induce neuronal entrainment to beat and meter and obtained images of the neuronal populations with an electroencephalogram functional magnetic resonance imaging (EEG-fMRI) fusion method. We observed that some neuronal populations, including the bilateral putamen, bilateral caudate, left thalamus, and supplementary motor area (SMA), entrain to both beat and meter. We also observed that the bilateral putamen entrains more to meter and the SMA entrains more to beat. Our results suggest that the bilateral putamen plays an important role in meter perception.

JBHI Journal 2019 Journal Article

Inferring MicroRNA Targets Based on Restricted Boltzmann Machines

Ying Liu
Jiawei Luo
Pingjian Ding

Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.

IJCAI Conference 2019 Conference Paper

LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

Weibin Meng
Ying Liu
Yichen Zhu
Shenglin Zhang
Dan Pei
Yuqing Liu
Yihao Chen
Ruizhi Zhang

Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.

YNIMG Journal 2018 Journal Article

Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system

Zhengde Wei
Long Han
Xiuying Zhong
Ying Liu
Rujing Zha
Ying Wang
Li-Zhuang Yang
Junjie Bu

Deficits in the computational processes of reinforcement learning have been suggested to underlie addiction. Additionally, environmental uncertainty, which is encoded in the anterior cingulate cortex (ACC), modulates reward prediction errors (RPEs) during reinforcement learning and exacerbates addiction. The present study tested whether and how the ACC would have an essential role in drug addiction by failing to use uncertainty to modulate the RPEs during reinforcement learning. In Experiment I, we found that the ACC/medial prefrontal cortex (MPFC) did not modulate RPE learning according to uncertainty in smokers. The effect of uncertainty × RPE in the ACC/MPFC was correlated with the learning rate of RPEs and the duration of nicotine use. Experiment II demonstrated that serotonin, but not dopamine, receptor mRNA expression significantly decreased in the ACC of the nicotine exposed compared to the control rats. Furthermore, there was a positive correlation between learning rate and serotonin receptor mRNA expression in the ACC. Therefore, all present results suggest that impairments in uncertainty modulation in the ACC disrupt reinforcement learning processes in chronic nicotine users and contribute to maladaptive decision-making. These findings support interventions for pathological decision-making in drug addiction that strongly focus on the serotonin system in ACC.

YNIMG Journal 2018 Journal Article

Detectability and reproducibility of the olfactory fMRI signal under the influence of magnetic susceptibility artifacts in the primary olfactory cortex

Jiaming Lu
Xin Wang
Zhao Qing
Zhu Li
Wen Zhang
Ying Liu
Lihua Yuan
Le Cheng

For human olfactory functional MRI studies, the primary olfactory cortex (POC) suffers severe magnetic susceptibility artifacts, which adversely influences the detectability and reproducibility of the olfactory fMRI data and its clinical applications. The goal of this work is to assess the impacts of the image artifacts on the detectability and reproducibility of the olfactory activation in the POC. The severity of artifacts in the POC were classified into three levels using a Subjective Artifact score (SA_score). The mean temporal signal-to-noise ratio (tSNR) of the fMRI data acquired by a given MRI sequence and olfactory activation (β value) in POC were evaluated and compared to the concurrent activations in the primary visual cortex (Brodmann area 17, BA17) by an odor-visual association paradigm using ninety-nine normal human subjects. Our study revealed that the mean tSNR in POC was above the threshold for reliable detection of the functional activation signal, and, consequently, the mean olfactory activations in the POC were not significantly different from those in BA17. The reproducibility of the activation in the POC was assessed by a random half-split stimulation of a test-retest experiment. The overlap of the activation maps for all the trials (n = 1000) in the POC were not statistically different from that observed in BA17. These results show that the detectability and reproducibility of olfactory activation in the presence of susceptibility artifacts in the POC was at similar level of that in the visual cortex.

EAAI Journal 2018 Journal Article

Extracting topic-sensitive content from textual documents—A hybrid topic model approach

Yan Liang
Ying Liu
Chong Chen
Zhigang Jiang

When exploring information of a topic, users often concern its different aspects. For instance, product designers are interested in seeking information of specific topic aspects such as technical challenge and usability from online consumer opinions, while potential buyers wish to obtain general sentiment of public opinions. In this paper, we study an interesting problem called topic-sensitive content extraction (TSCE). TSCE aims to extract contents that are relevant to the samples of topic aspects highlighted by users from a single document in a given text collection. To tackle TSCE, we have proposed a new hybrid topic model which integrates different structures in both topic space and context space. It focuses on identifying contents associated with a specified topic aspect from each document. By modeling gradient documents via term profiles for context modeling and by leveraging local and global differences between probability distributions over words in both topic modeling and context modeling, it has better captured the features of various language patterns. Hence, sentence relevance ranking according to a specific topic aspect is largely improved. The experimental studies on extracting critical contents of specific aspects, including motivation and design solution, from technical patents for design analysis have shown the merits of the proposed modeling.

YNIMG Journal 2017 Journal Article

Neural substrates of updating the prediction through prediction error during decision making

Ying Wang
Ning Ma
Xiaosong He
Nan Li
Zhengde Wei
Lizhuang Yang
Rujing Zha
Long Han

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e. g. , in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making.

JBHI Journal 2015 Journal Article

Predicting Days in Hospital Using Health Insurance Claims

Yang Xie
Gunter Schreier
David C. W. Chang
Sandra Neubauer
Ying Liu
Stephen J. Redmond
Nigel H. Lovell

Health-care administrators worldwide are striving to lower the cost of care while improving the quality of care given. Hospitalization is the largest component of health expenditure. Therefore, earlier identification of those at higher risk of being hospitalized would help health-care administrators and health insurers to develop better plans and strategies. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year, based on hospital admissions and procedure claims data. The proposed method performs well in the general population as well as in subpopulations. Results indicate that the proposed model significantly improves predictions over two established baseline methods (predicting a constant number of days for each customer and using the number of days in hospital of the previous year as the forecast for the following year). A reasonable predictive accuracy (AUC $=0. 843$) was achieved for the whole population. Analysis of two subpopulations-namely elderly persons aged 63 years or older in 2011 and patients hospitalized for at least one day in the previous year-revealed that the medical information (e. g. , diagnosis codes) contributed more to predictions for these two subpopulations, in comparison to the population as a whole.

EAAI Journal 2015 Journal Article

Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach

Jian Jin
Ping Ji
Ying Liu
S.C. Johnson Lim

Online opinions provide informative customer requirements for product designers. However, the increasing volume of opinions make them hard to be digested entirely. It is expected to translate online opinions for designers automatically when they are launching a new product. In this research, an exploratory study is conducted, in which customer requirements in online reviews are manually translated into engineering characteristics (ECs) for Quality function deployment (QFD). From the exploratory study, a simple mapping from keywords to ECs is observed not able to be built. It is also found that it will be a time-consuming task to translate a large number of reviews. Accordingly, a probabilistic language analysis approach is proposed, which translates reviews into ECs automatically. In particular, the statistic concurrence information between keywords and nearby words is analyzed. Based on the unigram model and the bigram model, an integrated impact learning algorithm is advised to estimate the impacts of keywords and nearby words respectively. The estimated impacts are utilized to infer which ECs are implied in a given context. Using four brands of printer reviews from Amazon. com, comparative experiments are conducted. Finally, an illustrative example is shown to clarify how this approach can be applied by designers in QFD.

NeurIPS Conference 2013 Conference Paper

Learning Gaussian Graphical Models with Observed or Latent FVSs

Ying Liu
Alan Willsky

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles. Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes. We propose efficient structure learning algorithms for two cases: 1) All nodes are observed, which is useful in modeling social or flight networks where the FVS nodes often correspond to a small number of high-degree nodes, or hubs, while the rest of the networks is modeled by a tree. Regardless of the maximum degree, without knowing the full graph structure, we can exactly compute the maximum likelihood estimate in $O(kn^2+n^2\log n)$ if the FVS is known or in polynomial time if the FVS is unknown but has bounded size. 2) The FVS nodes are latent variables, where structure learning is equivalent to decomposing a inverse covariance matrix (exactly or approximately) into the sum of a tree-structured matrix and a low-rank matrix. By incorporating efficient inference into the learning steps, we can obtain a learning algorithm using alternating low-rank correction with complexity $O(kn^{2}+n^{2}\log n)$ per iteration. We also perform experiments using both synthetic data as well as real data of flight delays to demonstrate the modeling capacity with FVSs of various sizes. We show that empirically the family of GGMs of size $O(\log n)$ strikes a good balance between the modeling capacity and the efficiency.

YNIMG Journal 2010 Journal Article

Addiction related alteration in resting-state brain connectivity

Ning Ma
Ying Liu
Nan Li
Chang-Xin Wang
Hao Zhang
Xiao-Feng Jiang
Hu-Sheng Xu
Xian-Ming Fu

It is widely accepted that addictive drug use is related to abnormal functional organization in the user's brain. The present study aimed to identify this type of abnormality within the brain networks implicated in addiction by resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI). With fMRI data acquired during resting state from 14 chronic heroin users (12 of whom were being treated with methadone) and 13 non-addicted controls, we investigated the addiction related alteration in functional connectivity between the regions in the circuits implicated in addiction with seed-based correlation analysis. Compared with controls, chronic heroin users showed increased functional connectivity between nucleus accumbens and ventral/rostral anterior cingulate cortex (ACC), between nucleus accumbens and orbital frontal cortex (OFC), and between amygdala and OFC and reduced functional connectivity between prefrontal cortex and OFC and between prefrontal cortex and ACC. These observations of altered resting-state functional connectivity suggested abnormal functional organization in the addicted brain and may provide additional evidence supporting the theory of addiction that emphasizes enhanced salience value of a drug and its related cues but weakened cognitive control in the addictive state.

AAAI Conference 2007 Conference Paper

TableRank: A Ranking Algorithm for Table Search and Retrieval

Ying Liu
Prasenjit Mitra

Tables are ubiquitous in web pages and scientiﬁc documents. With the explosive development of the web, tables have become a valuable information repository. Therefore, effectively and efﬁciently searching tables becomes a challenge. Existing search engines do not provide satisfactory search results largely because the current ranking schemes are inadequate for table search and automatic table understanding and extraction are rather difﬁcult in general. In this work, we design and evaluate a novel table ranking algorithm – TableRank to improve the performance of our table search engine Table- Seer. Given a keyword based table query, TableRank facilities TableSeer to return the most relevant tables by tailoring the classic vector space model. TableRank adopts an innovative term weighting scheme by aggregating multiple weighting factors from three levels: term, table and document. The experimental results show that our table search engine outperforms existing search engines on table search. In addition, incorporating multiple weighting factors can signiﬁcantly improve the ranking results.