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Ying Guo

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12 papers

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EAAI Journal 2026 Journal Article

You only look once meets asymmetric pronged convolution: A unified framework for efficient fire and smoke detection

Shibo Dong
Ying Guo
Zhe Wu
Guokang Xie
Wei Xie
Yonghao Gu
Weifeng Xu

Fire detection remains a critical challenge in global public safety. Traditional sensor-based methods often suffer from delayed response and high false alarms, especially in early fire stages. To address these limitations, we propose several novel enhancements to the You Only Look Once (YOLO) object detection models. First, we introduce the Asymmetric pronged Convolution (APConv) module—a flexible plug-in that can replace standard convolution layers, improving multi-scale feature extraction and fine-grained recognition. Second, we design a Cross-stage Attention Feature Fusion (C2f-AFF) module along with a lightweight, memory-efficient self-attention detection head (LSADetect). Integrating these components into YOLO models (version 8–12) results in significant performance improvements, with mean Average Precision at 50% Intersection over Union threshold (mAP@50) gains ranging from 2. 6% to 4. 9%. Additionally, we propose the Efficient Cross-phase Network (ECN) module to further enhance feature fusion and computational efficiency. To enable robust evaluation, a new multi-source high-quality fire and smoke dataset Finaldata is constructed, which addresses common issues in existing open-source datasets like image duplication and watermarking. Experimental results on Finaldata show that our method improves mAP@50 by 4. 7% and 4. 5% in mean Average Precision at Intersection over Union thresholds from 0. 50 to 0. 95 (mAP@50–95) compared to the baseline, while achieving an impressive inference speed of 174 Frames Per Second (FPS). Moreover, we conducted additional experiments on two other public datasets. These results highlight the effectiveness of our approach in significantly enhancing detection accuracy and adaptability in complex fire and smoke environments. Our codes and dataset are publicly available at https: //github. com/123dsb-ux/Finaldata. git.

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YNICL Journal 2025 Journal Article

Abnormal myelin could be a mediator of childhood-trauma-induced depression: A quantitative synthetic MRI study

Junyan Wen
Shuqiong Zheng
Zhimin Chen
Xuecong Lin
Shanshan Yang
Wei Cui
Liaoming Gao
Ziqi Wu

OBJECTIVES: Major depressive disorder (MDD) is a prevalent mental illness with unclear underlying pathophysiology that is often linked to childhood trauma. Emerging evidence suggests a potential association between myelin abnormalities and depression, highlighting the need for further understanding of these neurobiological connections in MDD patients. This study investigated the mediating role of abnormal myelin in the pathophysiology of MDD induced by childhood trauma via quantitative Synthetic Magnetic Resonance Imaging (SyMRI) technique. METHODS: MDD patients and healthy controls underwent SyMRI scanning to assess myelin content, T1, T2 and proton density (PD) values in the brain. Statistical analyses compared these values between groups and correlated them with clinical symptom scores. Mediation analysis was used to determine the role of myelin content in the relationship between childhood trauma and clinical symptoms. RESULTS: Thirty-five MDD patients (median age, 23[18-34] years, 7 men) and forty-nine healthy controls (median age, 24[21-35] years, 18 men) were included. Compared with healthy controls, MDD patients presented significantly lower myelin content (FDR-corrected p 0.05). A lower myelin content was significantly correlated with higher depression and anxiety scores (p < 0.05; R = 0.1309-0.2550). A myelin reduction in the left lateral area 5 and left ventral dysgranular and granular insula mediated the association between childhood trauma and MDD symptoms (p < 0.05; β = 0.0643-0.0807). CONCLUSION: Our findings suggest that reduced myelin content in the left lateral area 5 and left ventral dysgranular and granular insula, due to childhood trauma, mediated 6.4% to 8.1% of the variance of MDD. However, the cross-sectional design limits causal inference, and the influence of comorbid anxiety was not controlled.

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TCS Journal 2024 Journal Article

Space limited linear-time graph algorithms on big data

Jianer Chen
Zirui Chu
Ying Guo
Wei Yang

We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time ω ( N ) or linear space Θ ( N ) would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k in dealing with the large graphs. In particular, we are interested in strict linear-time algorithms using space O ( k O ( 1 ) ). In our case studies, as examples, we present (1) a randomized greedy algorithm of time O ( N ) and space O ( k 2 ) for a parameterized version of the Maximal Matching problem; and (2) randomized kernelization algorithms of time O ( N ) and space O ( k O ( 1 ) ) for a number of well-known NP-hard problems. Our kernelization algorithms have their kernel sizes match the best kernel sizes by known polynomial-time kernelization algorithms with no space constraints for the problems. We also study the relationship between our proposed model and the streaming model. This study motivates a new streaming kernelization algorithm for the famous Vertex Cover problem that has an optimal update time complexity while matches the best known space complexity of streaming algorithms for the problem.

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NeurIPS Conference 2022 Conference Paper

Brain Network Transformer

Xuan Kan
Wei Dai
Hejie Cui
Zilong Zhang
Ying Guo
Carl Yang

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https: //github. com/Wayfear/BrainNetworkTransformer.

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I&C Journal 2022 Journal Article

Linear-time parameterized algorithms with limited local resources

Jianer Chen
Ying Guo
Qin Huang

We propose a new computational model for the study of massive data processing. Our model measures the complexity of reading the input data in terms of their very large size N and analyzes the computational cost in terms of a parameter k that characterizes the computational power provided by limited local computing resources. We develop new algorithmic techniques for solving well-known computational problems on the model. In particular, randomized algorithms of running time O ( N + g 1 ( k ) ) and space O ( k 2 ), with very high probability, are developed for the famous graph matching problem on unweighted and weighted graphs. More specifically, our algorithm for unweighted graphs finds a k-matching (i. e. , a matching of k edges) in a general unweighted graph in time O ( N + k 2. 5 ), and our algorithm for weighted graphs finds a maximum weighted k-matching in a general weighted graph in time O ( N + k 3 log ⁡ k ).

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YNIMG Journal 2019 Journal Article

A hierarchical independent component analysis model for longitudinal neuroimaging studies

Yikai Wang
Ying Guo

In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions, to study neurodevelopment or to evaluate treatment effects on neural processing. One of the important goals in longitudinal imaging analysis is to study changes in brain functional networks across time and how the changes are modulated by subjects' clinical or demographic variables. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA), which separates multivariate signals into linear mixture of independent components. However, existing ICA methods are only applicable to cross-sectional studies and not suited for modeling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of changes in brain functional networks on both the population- and individual-level, borrow information across repeated scans within the same subject to increase statistical power in detecting covariate effects on the networks, and allow for model-based prediction for brain networks changes caused by disease progression, treatment or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain maximum likelihood estimates of L-ICA. We further develop a subspace-based approximate EM algorithm which greatly reduce the computation time while still retaining high accuracy. Moreover, we present a statistical testing procedure for examining covariate effects on brain network changes. Simulation results demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2 study to investigate changes in brain functional networks in Alzheimer disease. Results from the L-ICA provide biologically insightful findings which are not revealed using existing methods.

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YNIMG Journal 2018 Journal Article

Estimating dynamic brain functional networks using multi-subject fMRI data

Suprateek Kundu
Jin Ming
Jordan Pierce
Jennifer McDowell
Ying Guo

A common assumption in the study of brain functional connectivity is that the brain network is stationary. However it is increasingly recognized that the brain organization is prone to variations across the scanning session, fueling the need for dynamic connectivity approaches. One of the main challenges in developing such approaches is that the frequency and change points for the brain organization are unknown, with these changes potentially occurring frequently during the scanning session. In order to provide greater power to detect rapid connectivity changes, we propose a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points. The number and positioning of the change points are unknown and learned from the data in the first stage, by modeling a time-dependent connectivity metric under a fused lasso approach. In the second stage, the brain functional network for each state phase is inferred via sparse inverse covariance matrices. We compare the performance of the method with existing dynamic connectivity approaches via extensive simulation studies, and apply the proposed approach to a saccade block task fMRI data.

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YNIMG Journal 2018 Journal Article

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

Ixavier A. Higgins
Suprateek Kundu
Ying Guo

Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.

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YNIMG Journal 2017 Journal Article

Predicting individual brain functional connectivity using a Bayesian hierarchical model

Tian Dai
Ying Guo

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods.

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EAAI Journal 2016 Journal Article

Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

Mitchell Yuwono
Yong Qin
Jing Zhou
Ying Guo
Branko G. Celler
Steven W. Su

Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98. 02%, 96. 03%, and 2. 65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation.

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YNIMG Journal 2008 Journal Article

A unified framework for group independent component analysis for multi-subject fMRI data

Ying Guo
Giuseppe Pagnoni

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V. D. , Adali, T. , Pearlson, G. D. , Pekar, J. J. , 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151. ] and tensor PICA [Beckmann, C. F. , Smith, S. M. , 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294–311. ], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation–Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation.

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YNIMG Journal 2008 Journal Article

Modeling dose-dependent neural processing responses using mixed effects spline models: With application to a PET study of ethanol

Ying Guo
F. DuBois Bowman

For functional neuroimaging studies that involve experimental stimuli measuring dose levels, e. g. of an anesthetic agent, typical statistical techniques include correlation analysis, analysis of variance or polynomial regression models. These standard approaches have limitations: correlation analysis only provides a crude estimate of the linear relationship between dose levels and brain activity; ANOVA is designed to accommodate a few specified dose levels; polynomial regression models have limited capacity to model varying patterns of association between dose levels and measured activity across the brain. These shortcomings prompt the need to develop methods that more effectively capture dose-dependent neural processing responses. We propose a class of mixed effects spline models that analyze the dose-dependent effect using either regression or smoothing splines. Our method offers flexible accommodation of different response patterns across various brain regions, controls for potential confounding factors, and accounts for subject variability in brain function. The estimates from the mixed effects spline model can be readily incorporated into secondary analyses, for instance, targeting spatial classifications of brain regions according to their modeled response profiles. The proposed spline models are also extended to incorporate interaction effects between the dose-dependent response function and other factors. We illustrate our proposed statistical methodology using data from a PET study of the effect of ethanol on brain function. A simulation study is conducted to compare the performance of the proposed mixed effects spline models and a polynomial regression model. Results show that the proposed spline models more accurately capture varying response patterns across voxels, especially at voxels with complex response shapes. Finally, the proposed spline models can be used in more general settings as a flexible modeling tool for investigating the effects of any continuous covariates on neural processing responses.

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