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Zhao Wang

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21 papers

1 author row

AAAI Conference 2026 Conference Paper

AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control

Xinyue Guo
Xiaoran Yang
Lipan Zhang
Jianxuan Yang
Zhao Wang
Jian Luan

Sound effect editing—modifying audio by adding, removing, or replacing elements—remains constrained by existing approaches that rely solely on low-level signal processing or coarse text prompts, often resulting in limited flexibility and suboptimal audio quality. To address this, we propose AV-Edit, a generative sound effect editing framework that enables fine-grained editing of existing audio tracks in videos by jointly leveraging visual, audio, and text semantics. Specifically, the proposed method employs a specially designed contrastive audio-visual masking autoencoder (CAV-MAE-Edit) for multimodal pre-training, learning aligned cross-modal representations. These representations are then used to train an editorial Multimodal Diffusion Transformer (MM-DiT) capable of removing visually irrelevant sounds and generating missing audio elements consistent with video content through a correlation-based feature gating training strategy. Furthermore, we construct a dedicated video-based sound editing dataset as an evaluation benchmark. Experiments demonstrate that the proposed AV-Edit generates high-quality audio with precise modifications based on visual content, achieving state-of-the-art performance in the field of sound effect editing and exhibiting strong competitiveness in the domain of audio generation.

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TCS Journal 2026 Journal Article

The g-good-neighbor diagnosability of lexicographic product networks under the PMC model

Ayun Zhang
Zhao Wang
Jinning Zhao
Yaping Mao
Eddie Cheng

In 2012, Peng et al. introduced g-good-neighbor conditional diagnosability, a groundbreaking metric for fault diagnosis in systems. Central to this concept is the requirement that each fault-free node must be adjacent to at least g fault-free nodes. In this paper, we give sharp upper and lower bounds of g-good-neighbor conditional diagnosability of two general lexicographic product networks under the PMC model. Furthermore, we obtain the exact values of g-good-neighbor conditional diagnosability of grid and torus graphs, mesh-connected k-ary n-cubes, and torus-connected k-ary n-cubes in terms of lexicographic products.

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AAMAS Conference 2025 Conference Paper

Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits

Briti Gangopadhyay
Zhao Wang
Alberto Silvio Chiappa
Shingo Takamatsu

Optimizing budget allocation is vital for digital advertising, yet practical algorithms remain scarce due to limited public datasets and realistic simulation environments. While multi-armed bandit (MAB) algorithms are well-studied, they struggle in non-stationary settings requiring rapid adaptation. This paper introduces three key contributions: (1) a simulation environment that emulates multichannel advertising campaigns using logged real-world data; (2) an enhanced combinatorial bandit strategy with efficient exploration, and change-point detection to adapt dynamically to market shifts; and (3) Empirical validation showing superior performance over baselines in reward and regret metrics across real-world campaigns.

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JBHI Journal 2025 Journal Article

Improving Foundation Model for Endoscopy Video Analysis via Representation Learning on Long Sequences

Zhao Wang
Chang Liu
Lingting Zhu
Tongtong Wang
Shaoting Zhang
Qi Dou

Recent advancements in endoscopy video analysis have relied on the utilization of relatively short video clips extracted from longer videos or millions of individual frames. However, these approaches tend to neglect the domain-specific characteristics of endoscopy data, which is typically presented as a long stream containing valuable semantic spatial and temporal information. To address this limitation, we propose EndoFM-LV, a foundation model developed under a minute-level pre-training framework upon long endoscopy video sequences. To be specific, we propose a novel masked token modeling scheme within a teacher-student framework for self-supervised video pre-training, which is tailored for learning representations from long video sequences. For pre-training, we construct a large-scale long endoscopy video dataset comprising 6, 469 long endoscopic video samples, each longer than 1 minute and totaling over 13 million frames. Our EndoFM-LV is evaluated on four types of endoscopy tasks, namely classification, segmentation, detection, and workflow recognition, serving as the backbone or temporal module. Extensive experimental results demonstrate that our framework outperforms previous state-of-the-art video-based and frame-based approaches by a significant margin, surpassing Endo-FM (5. 6% F1, 9. 3% Dice, 8. 4% F1, and 3. 3% accuracy for classification, segmentation, detection, and workflow recognition) and EndoSSL (5. 0% F1, 8. 1% Dice, 9. 3% F1 and 3. 1% accuracy for classification, segmentation, detection, and workflow recognition).

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AAAI Conference 2025 Conference Paper

Large Images Are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian Splatting

Lingting Zhu
Guying Lin
Jinnan Chen
Xinjie Zhang
Zhenchao Jin
Zhao Wang
Lequan Yu

While Implicit Neural Representations (INRs) have demonstrated significant success in image representation, they are often hindered by large training memory and slow decoding speed. Recently, Gaussian Splatting (GS) has emerged as a promising solution in 3D reconstruction due to its highquality novel view synthesis and rapid rendering capabilities, positioning it as a valuable tool for a broad spectrum of applications. In particular, a GS-based representation, 2DGS, has shown potential for image fitting. In our work, we present Large Images are Gaussians (LIG), which delves deeper into the application of 2DGS for image representations, addressing the challenge of fitting large images with 2DGS in the situation of numerous Gaussian points, through two distinct modifications: 1) we adopt a variant of representation and optimization strategy, facilitating the fitting of a large number of Gaussian points; 2) we propose a Level-of-Gaussian approach for reconstructing both coarse low-frequency initialization and fine high-frequency details. Consequently, we successfully represent large images as Gaussian points and achieve high-quality large image representation, demonstrating its efficacy across various types of large images.

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EAAI Journal 2025 Journal Article

Robust Bi-temporal cross-scene land cover map updating via curriculum-guided self-training and adversarial learning

Zhao Wang
Yue Zhao
Maoguo Gong
Hao Li
Gaogao Liu
Jianlong Tang

Bi-temporal land cover maps updating extends the traditional remote sensing image classification task to analyze land cover and land use (LC&LU) with unavailable ground truth and distribution shifts across the time scenarios. Difference-assisted knowledge transfer can alleviate the heavy annotation dependency on the target domain (newly collected image over the same region) to some extent. The inaccurate difference information obtained in an unsupervised manner often introduces a large number of mislabels for the training data on the target domain, while land cover transitions may contribute to the shortage of samples for specific ground categories on the target domain, i. e. , imbalance between categories. This paper proposes a collaborative curriculum self-training framework, which is considerably robust to inaccurate difference knowledge transferring, to learn a more generalized classifier for the target domain. Specifically, curriculum guided adversarial learning consists of three players, i. e. , curricula-classifier, curricula-generator, and discriminator, which aims at efficiently and reliably adapting to the negative effects of incorrect cross-domain mapping and the imbalanced long-tail distribution on self-training framework. Sample-level and task-level curriculum constraints are designed to collaboratively facilitate sample selection and task learning. The experimental results on four datasets (two pairs of bi-temporal images) show that the above method is able to perform robust learning and achieve remarkable classification performance. The code is released at https: //github. com/bling2beyond/Curricula-GAN.

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I&C Journal 2025 Journal Article

The g-good-neighbor diagnosability of product networks under the PMC model

Zhao Wang
Yaping Mao
Sun-Yuan Hsieh
Ralf Klasing

The concept of neighbor connectivity originated from the assessment of the subversion of espionage networks caused by underground resistance movements, and it has now been applied to measure the disruption of networks caused by cascading failures through neighbors. In this paper, we give two necessary and sufficient conditions of the existence of g-good-neighbor diagnosability. We introduce a new concept called g-good neighbor cut-component number (gc number for short), which has close relation with g-good-neighbor diagnosability. Sharp lower and upper bounds of the gc number of general graphs in terms of the g-good neighbor connectivity have been proposed, which provide a formula to compute the g-good-neighbor diagnosability for general graphs (therefore for Cartesian product graphs). As their applications, we get the exact values or bounds for the gc numbers and g-good-neighbor diagnosability of grid, torus networks and generalized cubes.

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NeurIPS Conference 2024 Conference Paper

$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation

Weiquan Wang
Jun Xiao
Chunping Wang
Wei Liu
Zhao Wang
Long Chen

Diffusion models have demonstrated their effectiveness in addressing the inherent uncertainty and indeterminacy in monocular 3D human pose estimation (HPE). Despite their strengths, the need for large search spaces and the corresponding demand for substantial training data make these models prone to generating biomechanically unrealistic poses. This challenge is particularly noticeable in occlusion scenarios, where the complexity of inferring 3D structures from 2D images intensifies. In response to these limitations, we introduce the **Di**screte **Di**ffusion **Pose** (**$\text{Di}^2\text{Pose}$**), a novel framework designed for occluded 3D HPE that capitalizes on the benefits of a discrete diffusion model. Specifically, **$\text{Di}^2\text{Pose}$** employs a two-stage process: it first converts 3D poses into a discrete representation through a pose quantization step, which is subsequently modeled in latent space through a discrete diffusion process. This methodological innovation restrictively confines the search space towards physically viable configurations and enhances the model’s capability to comprehend how occlusions affect human pose within the latent space. Extensive evaluations conducted on various benchmarks (e. g. , Human3. 6M, 3DPW, and 3DPW-Occ) have demonstrated its effectiveness.

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EAAI Journal 2024 Journal Article

Adversarial deep reinforcement learning based robust depth tracking control for underactuated autonomous underwater vehicle

Zhao Wang
Xianbo Xiang
Yu Duan
Shaolong Yang

In this paper, an adversarial deep reinforcement learning-based control method is proposed to address the issue of robust depth tracking of an underactuated autonomous underwater vehicle in the presence of intrinsic coupled dynamics and external disturbances. First, long-short-term-memory neural network is presented to memorize and predict the changes in the state of vehicle, and a cascaded multilayer perception projects the output into action space of vehicle. Subsequently, adversarial deep reinforcement learning scheme is applied to the training of control agent by introducing an adversary which counteracts the control behavior, whereby the agent is enabled to learn the control strategy in different distributions of state transition. For evaluation of the performance, a control agent is pre-trained in simulation environment based on the reliable digital model of a real vehicle, and the simulation environment is paced at one iteration per second to align with real-time operations to ensure the portability of training result. Furthermore, the training cost is also extremely reduced. Finally, experiments are conducted with time-varying disturbances to further prove the feasibility of the proposed learning-based control scheme on a prototype of underwater vehicle in towing tank. Moreover, comparative experiment results show the better robustness performance of the learning-based control agent than that of classic line-of-sight based proportional–integral–derivative and adaptive line-of-sight based proportional–integral–derivative controllers in different scenarios.

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AAAI Conference 2024 Conference Paper

Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

Zhihao Wang
Yulin Zhou
Ningyu Zhang
Xiaosong Yang
Jun Xiao
Zhao Wang

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training samples with L2 loss in current training pipeline. However, little attention has been paid to the uncertainty property which is crucial to the prediction task. We argue that the recorded motion in training data could be an observation of possible future, rather than a predetermined result. In addition, existing works calculate the predicted error on each future frame equally during training, while recent work indicated that different frames could play different roles. In this work, a novel computationally efficient encoder-decoder model with uncertainty consideration is proposed, which could learn proper characteristics for future frames by a dynamic function. Experimental results on benchmark datasets demonstrate that our uncertainty consideration approach has obvious advantages both in quantity and quality. Moreover, the proposed method could produce motion sequences with much better quality that avoids the intractable shaking artefacts. We believe our work could provide a novel perspective to consider the uncertainty quality for the general motion prediction task and encourage the studies in this field. The code will be available in https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB.

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YNIMG Journal 2023 Journal Article

Electrocortical correlates of hypersensitivity to large immediate rewards in sensation seeking

Ya Zheng
Chenlu Guan
Zhao Wang
Wendeng Yang
Bo Gao

Sensation seeking and delay discounting are strong predictors of various risk-taking behaviors. However, the relationship between sensation seeking and delay discounting remains elusive. Here, we addressed this issue by examining how high sensation seekers (HSS; N = 40) and low sensation seekers (LSS; N = 40) evaluated immediate and delayed rewards with low and high amounts during a behavioral task and an EEG task of delay discounting. Although HSS and LSS exhibited comparable discounting preference at the behavioral level, HSS relative to LSS was associated with a greater delay discounting effect at the neural level when earned rewards were large. This abnormality of reward magnitude was further corroborated by an electrocortical hypersensitivity to large immediate rewards and a stronger neural coding of reward magnitude for HSS as compared to LSS. Our findings support both the hyperactive approach theory and the optimal arousal theory in sensation seeking and have implications for the prevention and intervention targeting sensation seeking to reduce maladaptive risk-taking behaviors.

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JBHI Journal 2021 Journal Article

Computer-Aided Intraoperative Toric Intraocular Lens Positioning and Alignment During Cataract Surgery

Yuxuan Zhai
Guanghua Zhang
Longsheng Zheng
Guangqian Yang
Ke Zhao
Yubin Gong
Zhe Zhang
Ximei Zhang

Cataract causes more than half of all blindness worldwide. The most effective treatment is surgery, where cataract is often replaced by intraocular lens (IOL). Beyond saving vision, toric IOL implantation is becoming increasingly popular to correct corneal astigmatism. It is important to precisely position and align the axis of IOL during surgery to achieve optimal post-operative astigmatism correction. Comparing with conventional manual marking, automated markerless IOL alignment can be faster, more accurate and non-invasive. Here we propose a framework for computer-assisted intraoperative IOL positioning and alignment based on detection and tracking. Firstly, the iris boundary was segmented and the eye center was determined. A statistical sampling method was developed to segment iris and generate training labels, and both conventional algorithms and deep convolutional neural network (CNN) methods were evaluated. Then, regions of interests (ROIs) containing high density of scleral capillaries were used for tracking eye rotations. Both correlation filter and CNN methods were evaluated for tracking. Cumulative errors during long-term tracking were corrected using a reference image. Validation studies against manual labeling using 7 clinical cataract surgical videos demonstrated that the proposed algorithm achieved an average position error around 0. 2 mm, an axis alignment error of $^{\circ}$, and a frame rate of > 25 FPS, and can be potentially used intraoperatively for markerless IOL positioning and alignment during cataract surgery.

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AAAI Conference 2021 Conference Paper

Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals

Zhao Wang
Aron Culotta

Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data, it can quickly degrade when the test distribution changes. For example, it has been shown that classifiers perform poorly when humans make minor modifications to change the label of an example. One solution to increase model reliability and generalizability is to identify causal associations between features and classes. In this paper, we propose to train a robust text classifier by augmenting the training data with automatically generated counterfactual data. We first identify likely causal features using a statistical matching approach. Next, we generate counterfactual samples for the original training data by substituting causal features with their antonyms and then assigning opposite labels to the counterfactual samples. Finally, we combine the original data and counterfactual data to train a robust classifier. Experiments on two classification tasks show that a traditional classifier trained on the original data does very poorly on human-generated counterfactual samples (e. g. , 10%-37% drop in accuracy). However, the classifier trained on the combined data is more robust and performs well on both the original test data and the counterfactual test data (e. g. , 12%-25% increase in accuracy compared with the traditional classifier). Detailed analysis shows that the robust classifier makes meaningful and trustworthy predictions by emphasizing causal features and de-emphasizing non-causal features.

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JBHI Journal 2020 Journal Article

Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification

Zhao Wang
Quande Liu
Qi Dou

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12. 16% and 14. 23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.

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TCS Journal 2020 Journal Article

On the g-good-neighbor connectivity of graphs

Zhao Wang
Yaping Mao
Sun-Yuan Hsieh
Jichang Wu

Connectivity and diagnosability are two important parameters for the fault tolerant of an interconnection network G. In 1996, Fàbrega and Fiol proposed the g-good-neighbor connectivity of G. In this paper, we show that 1 ≤ κ g ( G ) ≤ n − 2 g − 2 for 0 ≤ g ≤ { Δ ( G ), ⌊ n − 3 2 ⌋ }, and graphs with κ g ( G ) = 1, 2 and trees with κ g ( T n ) = n − t for 4 ≤ t ≤ n + 2 2 are characterized, respectively. In the end, we get the three extremal results for the g-good-neighbor connectivity.

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TCS Journal 2019 Journal Article

Matching preclusion number in product graphs

Zhao Wang
Christopher Melekian
Eddie Cheng
Yaping Mao

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has no perfect matchings or almost-perfect matchings. For many interconnection networks, the matching preclusion number is equal to the minimum degree of a vertex in the network, and the sets of edges attaining the minimum are precisely those incident to a single vertex of minimum degree; we say such networks are super matched. In this paper we derive upper and lower bounds for the matching preclusion number for networks constructed using a variety of binary graph operations, and give sufficient conditions for such networks to be super matched.

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TCS Journal 2019 Journal Article

Matching preclusion number of graphs

Zhao Wang
Yaping Mao
Eddie Cheng
Jinyu Zou

The matching preclusion number of a graph G, denoted by mp ( G ), is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. In this paper, we first give some sharp upper and lower bounds of matching preclusion number. Next, graphs with large and small matching preclusion number are characterized, respectively. In the end, we investigate some extremal problems and the Nordhaus–Gaddum-type relations on matching preclusion number.

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AAAI Conference 2019 Conference Paper

When Do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception Using Individual Treatment Effect Estimation

Zhao Wang
Aron Culotta

Studies across many disciplines have shown that lexical choice can affect audience perception. For example, how users describe themselves in a social media profile can affect their perceived socio-economic status. However, we lack general methods for estimating the causal effect of lexical choice on the perception of a specific sentence. While randomized controlled trials may provide good estimates, they do not scale to the potentially millions of comparisons necessary to consider all lexical choices. Instead, in this paper, we first offer two classes of methods to estimate the effect on perception of changing one word to another in a given sentence. The first class of algorithms builds upon quasi-experimental designs to estimate individual treatment effects from observational data. The second class treats treatment effect estimation as a classification problem. We conduct experiments with three data sources (Yelp, Twitter, and Airbnb), finding that the algorithmic estimates align well with those produced by randomized-control trials. Additionally, we find that it is possible to transfer treatment effect classifiers across domains and still maintain high accuracy.

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TCS Journal 2018 Journal Article

Strong matching preclusion number of graphs

Yaping Mao
Zhao Wang
Eddie Cheng
Christopher Melekian

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. The strong matching preclusion number (or simply, SMP number) s m p ( G ) of a graph G is the minimum number of vertices and/or edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem and has been introduced by Park and Ihm. In this paper, we first study the SMP number of some special graph classes, and give some sharp upper and lower bounds of SMP number. Next, graphs with large and small SMP number are characterized, respectively. In the end, we investigate the Nordhaus–Gaddum-type relations on SMP number.

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YNIMG Journal 2017 Journal Article

Different mechanisms in learning different second languages: Evidence from English speakers learning Chinese and Spanish

Fan Cao
Bethany L. Sussman
Valeria Rios
Xin Yan
Zhao Wang
Gregory J. Spray
Ryan M. Mack

Word reading has been found to be associated with different neural networks in different languages, with greater involvement of the lexical pathway for opaque languages and greater invovlement of the sub-lexical pathway for transparent langauges. However, we do not know whether this language divergence can be demonstrated in second langauge learners, how learner's metalinguistic ability would modulate the langauge divergence, or whether learning method would interact with the language divergence. In this study, we attempted to answer these questions by comparing brain activations of Chinese and Spanish word reading in native English-speaking adults who learned Chinese and Spanish over a 2 week period under three learning conditions: phonological, handwriting, and passive viewing. We found that mapping orthography to phonology in Chinese had greater activation in the left inferior frontal gyrus (IFG) and left inferior temporal gyrus (ITG) than in Spanish, suggesting greater invovlement of the lexical pathway in opaque langauges. In contrast, Spanish words evoked greater activation in the left superior temporal gyrus (STG) than English, suggesting greater invovlement of the sublexical pathway for transparant languages. Furthermore, brain-behavior correlation analyses found that higher phonological awareness and rapid naming were associated with greater activation in the bilateral IFG for Chinese and in the bilateral STG for Spanish, suggesting greater language divergence in participants with higher meta-linguistic awareness. Finally, a significant interaction between the language and learning condition was found in the left STG and middle frontal gyrus (MFG), with greater activation in handwriting learning than viewing learning in the left STG only for Spanish, and greater activation in handwriting learning than phonological learning in the left MFG only for Chinese. These findings suggest that handwriting facilitates assembled phonology in Spanish and addressed phonology in Chinese. In summary, our study suggests different mechanisms in learning different L2s, providing important insights into neural plasticity and important implications in second language education.

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YNIMG Journal 2017 Journal Article

Neural signatures of phonological deficits in Chinese developmental dyslexia

Fan Cao
Xin Yan
Zhao Wang
Yanni Liu
Jin Wang
Gregory J. Spray
Yuan Deng

There has been debate on whether phonological deficits explain reading difficulty in Chinese, since Chinese is a logographic language which does not employ grapheme-phoneme-correspondence rules and remote memorization seems to be the main method to acquire reading. In the current study, we present neuroimaging evidence that the phonological deficit is also a signature of Chinese dyslexia. Specifically, we found that Chinese children with dyslexia (DD) showed reduced brain activation in the left dorsal inferior frontal gyrus (dIFG) when compared to both age-matched controls (AC) and reading-matched controls (RC) during an auditory rhyming judgment task. This suggests that the phonological processing deficit in this region may be a signature of dyslexia in Chinese, rather than a difference due to task performance or reading ability, which was matched on DD and RC. At exactly the same region of the left dIFG, we found a positive correlation between brain activation and reading skill in DD, suggesting that the phonological deficit is associated with the severity of dyslexia. We also found increased brain activation in the right precentral gyrus in DD than both AC and RC, suggesting a compensation of reliance on articulation. Functional connectivity analyses revealed that DD had a weaker connection between the left superior temporal gyrus (STG) and fusiform gyrus (FG) than the two control groups, suggesting that the reduced connection between phonology and orthography is another neural signature of dyslexia. In contrast, DD showed greater connectivity between the left dIFG and the left inferior parietal lobule (IPL) than both control groups, suggesting a reduced segregation between the language network and default mode network in dyslexic children. We also found that connectivity between the left STG and the left dIFG was sensitive to task performance and/or reading skill rather than being dyslexic or not, because AC was greater than both RC and DD, while the connectivity between the left middle occipital gyrus (MOG) and left STG was sensitive to age, because both AC and DD were greater than RC. In summary, our study provides the very first neurological evidence of phonological deficits in Chinese developmental dyslexia and we successfully distinguished variations of brain activity/functional connectivity due to age, performance, and dyslexia by comparing AC, RC, and DD.

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