Fang Wang

EAAI Journal 2026 Journal Article

A whole-life fatigue crack growth rate prediction method based on active learning and physics-informed loss

• Qixuan Zhang
• Wei Zhang
• Rui Huang
• Xinghui Chen
• Bingbing Li
• Fang Wang
• Yiming Zheng
• Changyu Zhou

Whole-life fatigue crack growth presents a critical challenge in structural integrity assessment, particularly under complex loading conditions. To address the limitations of standard physics-informed neural networks (PINNs) in capturing the temporal dynamics of fatigue crack growth, this study proposes an active learning-based physics-informed recurrent neural network (AC-PI-RNN). Specifically, a recurrent neural network (RNN) is integrated with a fully connected network, where dynamic features (stress intensity factor range) and static features (stress ratio, load amplitude, and pre-strain) are fused at the RNN input layer to provide comprehensive loading information. To optimize sample selection under data-limited conditions, a query-by-committee active learning strategy is employed. Furthermore, a modified Jones physical model is embedded into the network's loss function to enforce adherence to the underlying physics of fatigue crack growth. Comprehensive evaluations validate the efficacy of the proposed framework, demonstrating enhanced predictive fidelity, robust generalization, and improved physical consistency. A comparative analysis reveals that the AC-PI-RNN significantly outperforms traditional RNN and PINN models, showing a distinct advantage in capturing the complete trajectory of whole-life crack propagation with high precision. The proposed framework provides an effective and interpretable approach for whole-life fatigue crack growth rate prediction under complex loading conditions.

JBHI Journal 2026 Journal Article

Multimodal Graph Learning With Multi-Hypergraph Reasoning Networks for Focal Liver Lesion Classification in Multimodal Magnetic Resonance Imaging

• Shaocong Mo
• Ming Cai
• Lanfen Lin
• Ruofeng Tong
• Fang Wang
• Qingqing Chen
• Wenbin Ji
• Yinhao Li

Multimodal magnetic resonance imaging (MRI) is instrumental in differentiating liver lesions. The major challenge involves modeling reliable connections and simultaneously learning complementary information across various MRI sequences. While previous studies have primarily focused on multimodal integration in a pair-wise manner using few modalities, our research seeks to advance a more comprehensive understanding of interaction modeling by establishing complex high-order correlations among the diverse modalities in multimodal MRI. In this paper, we introduce a multimodal graph learning with multi-hypergraph reasoning network to capture the full spectrum of both pair-wise and group-wise relationships among different modalities. Specifically, a weight-shared encoder extracts features from regions of interest (ROI) images across all modalities. Subsequently, a collection of uniform hypergraphs are constructed with varying vertex configurations, allowing for the modeling of not only pair-wise correlations but also the high-order collaborations for relational reasoning. Following information propagation through the hypergraph message passing, adaptive intra-modality fusion module is proposed to effectively fuse feature representations from different hypergraphs of the same modality. Finally, all refined features are concatenated to prepare for the classification task. Our experimental evaluations, including focal liver lesions classification using the LLD-MMRI2023 dataset and early recurrence prediction of hepatocellular carcinoma using our internal datasets, demonstrate that our method significantly surpasses the performance of existing approaches, indicating the effectiveness of our model in handling both pair-wise and group-wise interactions across multiple modalities.

EAAI Journal 2026 Journal Article

Optimizing potential-based reward automata in partially observable reinforcement learning using genetic local search

• Zhengwei Zhu
• Zhixuan Chen
• Chenyang Zhu
• Wen Si
• Fang Wang

Partially observable reinforcement learning extends the reinforcement learning framework to environments in which agents have limited visibility of the state space, making it particularly relevant for applications in robotics and autonomous vehicle navigation. However, a primary challenge in partially observable reinforcement learning is defining effective reward functions that can guide the learning process despite partial observability. To address this challenge, this paper introduces a novel approach for constructing potential-based reward automata by employing genetic local search methods. Specifically, our method constructs these automata from compressed representations of exploration trajectories, which succinctly capture critical decision points and essential state transitions while eliminating redundant steps. By optimizing trajectory samples and shortening agent trajectories to their crucial transitions, our technique significantly reduces computational overhead. Formally, we define the learning objective as an optimization problem aimed at maximizing the log-likelihood of future observations while simultaneously minimizing the structural complexity of the learned reward automata. Furthermore, by incorporating value-based strategies to estimate potential values within the reward automata, our approach improves learning efficiency and facilitates the identification of optimal reward structures. We empirically evaluate our proposed method on seven partially observable grid-world benchmarks. Experimental results demonstrate that our method achieves superior performance relative to state-of-the-art reward automata-based techniques, exhibiting both accelerated learning speeds and higher accumulated rewards. Additionally, our genetic local search algorithm consistently outperforms comparative heuristic methods in terms of learning curves and reward accumulation.

AAAI Conference 2026 Conference Paper

WikiMAG: A Multi-Agent Guided Framework for Generating Structured Wikipedia-like Articles

• Xiuli Kang
• Yinlong Xiao
• Minghao Hu
• Yuan Huang
• Bin Mao
• Ming Wang
• Fang Wang
• Zhunchen Luo

Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table. To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions. Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

NeurIPS Conference 2025 Conference Paper

DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction

• Yuxiang Li
• Yang Zhang
• Li Li
• Mengxuan Chen
• Meng Jin
• Fang Wang
• Haohuan Fu
• Juepeng Zheng

Modern precipitation forecasting systems, including reanalysis datasets, numerical models, and AI-based approaches, typically produce coarse-resolution gridded outputs. The process of converting these outputs to station-level predictions often introduces substantial spatial biases relative to station-level observations, especially in complex terrains or under extreme conditions. These biases stem from two core challenges: (i) $\textbf{station-level heterogeneity}$, with site-specific temporal and spatial dynamics; and (ii) $\textbf{oversmoothing}$, which blurs fine-scale variability in graph-based models. To address these issues, we propose $\textbf{DiffLiG}$ ($\underline{Diff}$usion-enhanced $\underline{Li}$quid $\underline{G}$raph with Attention Propagation), a graph neural network designed for precise spatial correction from gridded forecasts to station observations. DiffLiG integrates a GeoLiquidNet that adapts temporal encoding via site-aware OU dynamics, a graph neural network with a dynamic edge modulator that learns spatially adaptive connectivity, and a Probabilistic Diffusion Selector that generates and refines ensemble forecasts to mitigate oversmoothing. Experiments across multiple datasets show that DiffLiG consistently outperforms other methods, delivering more accurate and robust corrections across diverse geographic and climatic settings. Moreover, it achieves notable gains on other key meteorological variables, underscoring its generalizability and practical utility.

EAAI Journal 2025 Journal Article

Exploiting Non-likelihood Adversarial Training for Chinese Counterfactual Data Augmentation

• Dezhi An
• Fang Wang
• Shengcai Zhang
• Yan Li

Chinese natural language processing (NLP) models often struggle with out-of-distribution generalization due to data biases and shortcut learning. Existing counterfactual data augmentation (CDA) methods heavily rely on manual intervention, limiting their scalability. To address this issue, we propose CAT-CDA (Chinese Non-likelihood Adversarial Training for Counterfactual Data Augmentation), an automated approach that generates counterfactual data with opposite labels while maintaining semantic consistency. CAT-CDA employs a classifier to identify causal features and optimizes a generator through non-likelihood adversarial training, ensuring both diversity and fluency. Experimental results demonstrate that CAT-CDA significantly improves model robustness, enhances out-of-distribution performance, and mitigates shortcut learning while requiring lower computational resources than existing methods, making it highly applicable to Chinese NLP tasks.

IJCAI Conference 2025 Conference Paper

Incorporating Legal Logic into Deep Learning: An Intelligent Approach to Probation Prediction

• Qinghua Wang
• Xu Zhang
• Lingyan Yang
• Rui Shao
• Bonan Wang
• Fang Wang
• Cunquan Qu

Probation is a crucial institution in modern criminal law, embodying the principles of fairness and justice while contributing to the harmonious development of society. Despite its importance, the current Intelligent Judicial Assistant System (IJAS) lacks dedicated methods for probation prediction, and research on the underlying factors influencing probation eligibility remains limited. In addition, probation eligibility requires a comprehensive analysis of both criminal circumstances and remorse. Much of the existing research in IJAS relies primarily on data-driven methodologies, which often overlooks the legal logic underpinning judicial decision-making. To address this gap, we propose a novel approach that integrates legal logic into deep learning models for probation prediction, implemented in three distinct stages. First, we construct a specialized probation dataset that includes fact descriptions and probation legal elements (PLEs). Second, we design a distinct probation prediction model named the Multi-Task Dual-Theory Probation Prediction Model (MT-DT), which is grounded in the legal logic of probation and the Dual-Track Theory of Punishment. Finally, our experiments on the probation dataset demonstrate that the MT-DT model outperforms baseline models, and an analysis of the underlying legal logic further validates the effectiveness of the proposed approach.

IROS Conference 2025 Conference Paper

LGDD: Local-Global Synergistic Dual-Branch 3D Object Detection Using 4D Radar

• Xiaokai Bai
• Qin Yang
• Zili Zhou
• Fuyi Zhang
• Zhe Wu
• Si-Yuan Cao
• Lianqing Zheng
• Beinan Yu

4D millimeter-wave radar plays a pivotal role in autonomous driving due to its cost-effectiveness and robustness in adverse weather. However, the application of 4D radar point cloud in 3D perception tasks is hindered by its inherent sparsity and noise. To address these challenges, we propose LGDD, a novel local-global synergistic dual-branch 3D object detection framework using 4D radar. Specifically, we first introduce a point-based branch, which utilize a voxel-attended point feature extractor (VPE) to integrate semantic segmentation with cluster voting, thereby mitigating radar noise and extracting local-clustered instances features. Then, for the conventional pillar-based branch, we design a query-based feature pre-fusion (QFP) to address the sparsity and enhance global context representation. Additionally, we devise a proposal mask to filter out noisy points, enabling more focused clustering on regions of interest. Finally, we align the local instances with global context through semantics-geometry aware fusion (SGF) module to achieve comprehensive scene understanding. Extensive experiments demonstrate that LGDD achieves state-of-the-art performance on the public View-of-Delft and TJ4DRadSet datasets. Source code is available at https://github.com/shawnnnkb/LGDD.

AAAI Conference 2025 Conference Paper

M^3EL: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking

• Fang Wang
• Shenglin Yin
• Xiaoying Bai
• Minghao Hu
• Tianwei Yan
• Yi Liang

Multi-modal Entity Linking (MEL) is a fundamental component for various downstream tasks. However, existing MEL datasets suffer from small scale, scarcity of topic types and limited coverage of tasks, making them incapable of effectively enhancing the entity linking capabilities of multi-modal models. To address these obstacles, we propose a dataset construction pipeline and publish M^3EL, a large-scale dataset for MEL. M^3EL includes 79,625 instances, covering 9 diverse multi-modal tasks, and 5 different topics. In addition, to further improve the model's adaptability to multi-modal tasks, We propose a modality-augmented training strategy. Utilizing M^3EL as a corpus, train the CLIP_ND model based on CLIP (ViT-B-32), and conduct a comparative analysis with an existing multi-modal baselines. Experimental results show that the existing models perform far below expectations (ACC of 49.4%-75.8%), After analysis, it was obtained that small dataset sizes, insufficient modality task coverage, and limited topic diversity resulted in poor generalization of multi-modal models. Our dataset effectively addresses these issues, and the CLIP_ND model fine-tuned with M^3EL shows a significant improvement in accuracy, with an average improvement of 9.3% to 25% across various tasks. Our dataset publicly available to facilitate future research.

JBHI Journal 2025 Journal Article

SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data

• Yang Ai
• Jing Liu
• Yinhao Li
• Fang Wang
• Xiuju Du
• Rahul Kumar Jain
• Lanfen Lin
• Yen-Wei Chen

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

NeurIPS Conference 2025 Conference Paper

SeasonBench-EA: A Multi-Source Benchmark for Seasonal Prediction and Numerical Model Post-Processing in East Asia

• Mengxuan Chen
• Li Li
• Zou Ziheng
• Fang Wang
• Jinxiao Zhang
• Runmin Dong
• Juepeng Zheng
• Haohuan Fu

Seasonal-scale climate prediction plays a critical role in supporting agricultural planning, disaster prevention, and long-term decision making. In particular, reliable forecasts issued 1-6 months in advance are essential for early warning of flood and drought risks associated with precipitation during the East Asian summer monsoon season. However, while the use of machine learning techniques has advanced rapidly in weather and subseasonal-to-seasonal forecasting, partly driven by the availability of benchmark datasets, their application to seasonal-scale prediction remains limited. Existing seasonal prediction primarily relies on ensemble forecasts from numerical models, which, while physically grounded, are subject to biases and uncertainties at long lead times. Motivated by these challenges, we propose SeasonBench-EA, a benchmark dataset for seasonal prediction in East Asia region. It features multi-resolution, multi-source data with both regional and global coverage, integrating ERA5 reanalysis data and ensemble forecasts from multiple leading forecast centers. Beyond key atmospheric fields, the dataset also includes boundary-related variables, such as ocean state, soil and solar radiation, that are essential for capturing seasonal-scale atmospheric variability. Two tasks are defined and evaluated: 1) machine learning-based seasonal prediction using ERA5 reanalysis, and 2) post-processing of seasonal forecasts from numerical model ensembles. A suite of deterministic and probabilistic metrics is provided for tasks evaluation, along with a hindcast assessment focused on precipitation during the East Asian summer monsoon, aligned with model evaluation protocols used in operations. By offering a unified data and evaluation framework, SeasonBench-EA aims to promote the development and application of data-driven methods for seasonal prediction, a challenging yet highly impactful task with board implications for society and public well-being. Our benchmark is available at https: //github. com/SauryChen/SeasonBench-EA.

ICRA Conference 2025 Conference Paper

Structure-Aware Radar-Camera Depth Estimation

• Fuyi Zhang
• Zhu Yu 0001
• Chunhao Li
• Runmin Zhang
• Xiaokai Bai
• Zili Zhou
• Si-Yuan Cao
• Fang Wang

Radar has gained much attention in autonomous driving due to its accessibility and robustness. However, its standalone application for depth perception is constrained by issues of sparsity and noise. Radar-camera depth estimation offers a more promising complementary solution. Despite significant progress, current approaches fail to produce satisfactory dense depth maps, due to the unsatisfactory processing of the sparse and noisy radar data. They constrain the regions of interest for radar points in rigid rectangular regions, which may introduce unexpected errors and confusions. To address these issues, we develop a structure-aware strategy for radar depth enhancement, which provides more targeted regions of interest by leveraging the structural priors of RGB images. Furthermore, we design a Multi-Scale Structure Guided Network to enhance radar features and preserve detailed structures, achieving accurate and structure-detailed dense metric depth estimation. Building on these, we propose a structure-aware radar-camera depth estimation framework, named SA-RCD. Extensive experiments demonstrate that our SA-RCD achieves state-of-the-art performance on the nuScenes dataset. Our code will be available at https://github.com/FreyZhangYeh/SA-RCD.

AAAI Conference 2024 Conference Paper

Graph Neural Prompting with Large Language Models

• Yijun Tian
• Huan Song
• Zichen Wang
• Haozhu Wang
• Ziqing Hu
• Fang Wang
• Nitesh V. Chawla
• Panpan Xu

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented generation, remains an open question. In this work, we propose Graph Neural Prompting (GNP), a novel plug-and-play method to assist pre-trained LLMs in learning beneficial knowledge from KGs. GNP encompasses various designs, including a standard graph neural network encoder, a cross-modality pooling module, a domain projector, and a self-supervised link prediction objective. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks across different LLM sizes and settings. Code is available at https://github.com/meettyj/GNP.

EAAI Journal 2024 Journal Article

Observer-based adaptive neural consensus control of nonlinear multi-agent systems under input and output quantization

• Xinyi Lu
• Wentong Zhang
• Fang Wang
• Guanyu Lai

In this article, for a series of nonlinear multi-agent systems under input and output quantization, a novel observer-based adaptive neural leader-following consensus control strategy is raised. Different from the existing output feedback consensus control strategies, in this raised strategy, the output and input of the agent are communicated through a directed network and quantized before communication. First of all, according to the quantized input and output information, a neural networks (NNs)-based distributed state observer is built by using the NNs to approximate the unknown functions. Secondly, in the backstepping process, the partial derivatives of the virtual control signals are non-existent because of the quantized output’s discontinuity. To avoid this issue, a command filtering technique is applied. Moreover, by constructing an intermediate auxiliary control signal, an actual adaptive consensus controller is designed. Thirdly, to compensate for the impact of quantization errors, Lemma 3 is presented. On this basis, the raised strategy guarantees that all signals of the closed-loop system are semi-globally bounded and the followers’ outputs converge to a neighborhood of the output of the leader. Lastly, two examples are applied to demonstrate the feasibility of this strategy.

JBHI Journal 2024 Journal Article

Segmentation Guided Crossing Dual Decoding Generative Adversarial Network for Synthesizing Contrast-Enhanced Computed Tomography Images

• Yulin Yang
• Qingqing Chen
• Yinhao Li
• Fang Wang
• Xian-Hua Han
• Yutaro Iwamoto
• Jing Liu
• Lanfen Lin

Although contrast-enhanced computed tomography (CE-CT) images significantly improve the accuracy of diagnosing focal liver lesions (FLLs), the administration of contrast agents imposes a considerable physical burden on patients. The utilization of generative models to synthesize CE-CT images from non-contrasted CT images offers a promising solution. However, existing image synthesis models tend to overlook the importance of critical regions, inevitably reducing their effectiveness in downstream tasks. To overcome this challenge, we propose an innovative CE-CT image synthesis model called Segmentation Guided Crossing Dual Decoding Generative Adversarial Network (SGCDD-GAN). Specifically, the SGCDD-GAN involves a crossing dual decoding generator including an attention decoder and an improved transformation decoder. The attention decoder is designed to highlight some critical regions within the abdominal cavity, while the improved transformation decoder is responsible for synthesizing CE-CT images. These two decoders are interconnected using a crossing technique to enhance each other's capabilities. Furthermore, we employ a multi-task learning strategy to guide the generator to focus more on the lesion area. To evaluate the performance of proposed SGCDD-GAN, we test it on an in-house CE-CT dataset. In both CE-CT image synthesis tasks–namely, synthesizing ART images and synthesizing PV images–the proposed SGCDD-GAN demonstrates superior performance metrics across the entire image and liver region, including SSIM, PSNR, MSE, and PCC scores. Furthermore, CE-CT images synthetized from our SGCDD-GAN achieve remarkable accuracy rates of 82. 68%, 94. 11%, and 94. 11% in a deep learning-based FLLs classification task, along with a pilot assessment conducted by two radiologists.

YNIMG Journal 2024 Journal Article

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device

• Yuchen Lin
• Shaojia Huang
• Jidong Mao
• Meijia Li
• Naem Haihambo
• Fang Wang
• Yuping Liang
• Wufang Chen

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

YNIMG Journal 2021 Journal Article

Failure of resting-state frontal–occipital connectivity in linking visual perception with reading fluency in Chinese children with developmental dyslexia

• Xiujie Yang
• Jia Zhang
• Yaping Lv
• Fang Wang
• Guosheng Ding
• Manli Zhang
• Xiangzhi Meng
• Yan Song

It is widely accepted that impairment in visual perception impedes children's reading development, and further studies have demonstrated significant enhancement in reading fluency after visual perceptual training. However, the mechanism of the neural linkage between visual perception and reading is unclear. The purpose of this study was to examine the intrinsic functional relationship between visual perception (indexed by the texture discrimination task,TDT) and reading ability (character reading and reading fluency) in Chinese children with developmental dyslexia (DD) and those with typical development (TD). The resting-state functional connectivity (RSFC) between the primary visual cortex (V1, BA17) and the entire brain was analyzed. In addition, how RSFC maps are associated with TDT performance and reading ability in the DD and TD groups was examined. The results demonstrated that the strength of the RSFC between V1 and the left middle frontal gyrus (LMFG, BA9/BA46) was significantly correlated with both the threshold (SOA) of the TDT and reading fluency in TD children but not in DD children. Moreover, LMFG-V1 resting-state connectivity played a mediating role in the association of visual texture discrimination and reading fluency, but not in character reading, in TD children. In contrast, this mediation was absent in DD children, albeit their strengths of RSFC between V1 and the left middle frontal gyrus (LMFG) were comparable to those for the TD group. These findings indicate that typically developing children use the linkage of the RSFC between the V1 and LMFG for visual perception skills, which in turn promote fluent reading; in contrast, children with dyslexia, who had higher TDT thresholds than TD children, could not take advantage of their frontal-occipital connectivity to improve reading fluency abilities. These findings suggest that visual perception plays an important role in reading skills and that children with developmental dyslexia lack the ability to use their frontal-occipital connectivity to link visual perception with reading fluency.

YNIMG Journal 2019 Journal Article

Agreeableness modulates group member risky decision-making behavior and brain activity

• Fang Wang
• Xin Wang
• Fenghua Wang
• Li Gao
• Hengyi Rao
• Yu Pan

When facing difficult decisions, people typically believe that “two heads are better than one”. However, findings from previous studies are inconsistent regarding the advantages of decision-making in groups as compared to individual decision-making. We hypothesize that personality traits may modulate risk-taking behavior and brain activity changes during group decision-making. In this study, we used event-related potentials (ERP) with a well-validated balloon analogue risk task (BART) paradigm to examine the relationships between personality traits, decision-making behavior, and brain activity patterns when a cohort of male participants make decisions and take risks both in groups and in isolation. We found significantly increased risk-taking behavior and reduced P300 component during group decision-making as compared to individual decision-making only for participants with high Agreeableness, but not for those with low Agreeableness. Moreover, Agreeableness scores correlated with risk-taking behavior and P300 amplitude changes in group decisions. These findings suggest that Agreeableness personality modulates risk-taking behavior and brain activity when people make decisions in groups, which have implications for future group decision research and practice.

YNIMG Journal 2016 Journal Article

Predicting perceptual learning from higher-order cortical processing

• Fang Wang
• Jing Huang
• Yaping Lv
• Xiaoli Ma
• Bin Yang
• Encong Wang
• Boqi Du
• Wu Li

Visual perceptual learning has been shown to be highly specific to the retinotopic location and attributes of the trained stimulus. Recent psychophysical studies suggest that these specificities, which have been associated with early retinotopic visual cortex, may in fact not be inherent in perceptual learning and could be related to higher-order brain functions. Here we provide direct electrophysiological evidence in support of this proposition. In a series of event-related potential (ERP) experiments, we recorded high-density electroencephalography (EEG) from human adults over the course of learning in a texture discrimination task (TDT). The results consistently showed that the earliest C1 component (68–84ms), known to reflect V1 activity driven by feedforward inputs, was not modulated by learning regardless of whether the behavioral improvement is location specific or not. In contrast, two later posterior ERP components (posterior P1 and P160–350) over the occipital cortex and one anterior ERP component (anterior P160–350) over the prefrontal cortex were progressively modified day by day. Moreover, the change of the anterior component was closely correlated with improved behavioral performance on a daily basis. Consistent with recent psychophysical and imaging observations, our results indicate that perceptual learning can mainly involve changes in higher-level visual cortex as well as in the neural networks responsible for cognitive functions such as attention and decision making.

YNIMG Journal 2016 Journal Article

Temporal context processing within hippocampal subfields

• Fang Wang
• Rachel A. Diana

The episodic memory system can differentiate similar events based on the temporal information associated with the events. Temporal context, which is at least partially determined by the events that precede or follow the critical event, may be a cue to differentiate events. The purpose of the present study is to investigate whether the hippocampal dentate gyrus (DG)/CA3 and CA1 subfields are sensitive to changes in temporal context and, if so, whether the subregions show a linear or threshold-like response to similar temporal contexts. Participants incidentally encoded a series of object picture triplets and 20 of them were included in final analyses. The third picture in each triplet was operationally defined as the target and the first two pictures served as temporal context for the target picture. Each target picture was presented twice with temporal context manipulated to be either repeated, high similarity, low similarity, or new on the second presentation. We extracted beta parameters for the repeated target as a function of the type of temporal context. We expected to see repetition suppression, a reduction in the beta values, in response to repetition of the target. If temporal context information is included in the representation of the target within a given region, this repetition suppression should be greater for target images that were preceded by their original context than for target images preceded by a new context. Neuroimaging results showed that CA1, but not DG/CA3, modifies the target's representation based on its temporal context. Right CA1 did not distinguish high similarity temporal context from repeated context but did distinguish low similarity temporal context from repeated context. These results indicate that CA1 is sensitive to temporal context and suggest that it does not differentiate between a substantially similar temporal context and an identical temporal context. In contrast, DG/CA3 does not appear to process temporal context as defined in the current experiment.

YNIMG Journal 2015 Journal Article

Predicting N2pc from anticipatory HbO activity during sustained visuospatial attention: A concurrent fNIRS–ERP study

• Jing Huang
• Fang Wang
• Yulong Ding
• Haijing Niu
• Fenghua Tian
• Hanli Liu
• Yan Song

Understanding the properties of attentional control, along with the neural mechanisms subserving them, has long invited intense scrutiny in research groups. However, it has not been demonstrated how the top-down anticipatory hemodynamic activation influences the subsequent attentional processing of targets and distractors. Here, with concurrent fNIRS–ERP recording, we explored the potential contribution of anticipatory oxygenated hemoglobin (HbO) based brain activity to attentional control by examining how HbO influences the subsequent ERP N2pc components assumed to reflect attentional selection. We found that expecting a target led to a larger increase of preparatory HbO response over the visual cortex contralateral to the upcoming target, which was positively correlated with the subsequent target-evoked N2pc amplitude. Further, anticipation concerning the presence of a competing distractor resulted in large and prolonged preparatory HbO signals in the visual cortex contralateral to the distractor, indicating that the salient distractor might be actively suppressed by preparatory top-down attentional control. However, the pre-suppressed distractor still captured part of the attention in the subsequent visual search as revealed by a decrease in the N2pc amplitude, and such a distraction effect on N2pc was negatively correlated with preparatory HbO enhancement contralateral to the anticipated distractor. Overall, each individuals attentional shift to the target and resistance to the distractor measured by ERP is predictable in advance via anticipatory hemodynamic activity in the visual cortex measured by fNIRS.

IJCAI Conference 2013 Conference Paper

Learning Visual Symbols for Parsing Human Poses in Images

• Fang Wang
• Yi Li

Parsing human poses in images is fundamental in extracting critical visual information for artificial intelligent agents. Our goal is to learn selfcontained body part representations from images, which we call visual symbols, and their symbolwise geometric contexts in this parsing process. Each symbol is individually learned by categorizing visual features leveraged by geometric information. In the categorization, we use Latent Support Vector Machine followed by an efficient cross validation procedure. Then, these symbols naturally define geometric contexts of body parts in a fine granularity. When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images. Experiments on two large datasets suggest our approach outperforms state of the art methods.

JBHI Journal 2013 Journal Article

Toward a Passive Low-Cost In-Home Gait Assessment System for Older Adults

• Fang Wang
• E. Stone
• M. Skubic
• J. M. Keller
• C. Abbott
• M. Rantz

In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0. 99 and repeatability coefficients between 0. 7% and 6. 6% was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.

AAMAS Conference 2002 Conference Paper

Core Specification and Experiments in DIET: A Decentralised Ecosystem-inspired Mobile Agent System

• Cefn Hoile
• Fang Wang
• Erwin Bonsma
• Paul Marrow

AAMAS Conference 2002 Conference Paper

Self-organising Communities Formed by Middle Agents

• Fang Wang