Author name cluster

Hao Ding

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

15 papers

2 author rows

EAAI Journal 2026 Journal Article

Tooth generative adversarial network: Anatomical optimisation using Wasserstein generative adversarial network for tooth generation hyphenated dental 3-dimensional precision printing

Wuyuan Zhao
Yushu Liu
Walter Y.H. Lam
Benny C.F. Cheung
Hao Ding
James K.H. Tsoi

Objectives Deep learning (DL) has been applied to reconstruct missing tooth surfaces. Although promising, no current method ensures that DL-generated prosthesis simultaneously meet clinical requirements for accuracy, surface roughness, anatomical morphology, and mechanical properties across fabrication techniques. Furthermore, while both natural tooth and technician-designed prosthesis datasets are available, there has been no research on how to better use these two datasets. The purpose of this study is to address these issues. Methods We developed a geometric processing method that combines modified Delaunay triangulation (DT) reconstruction to achieve accurate, mechanically suitable results from 256 × 256 depth maps. A Tooth Generative Adversarial Network (ToothGAN) was trained with specialized loss functions for anatomical features and smoothness using both natural and technician-designed datasets. The output was validated via 3D printing and in vitro testing. Results ToothGAN outperformed prior algorithms on natural tooth data across metrics including Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), 3-Dimensional Route Mean Square Error (3DRMSE), and Visual Assessment (VA) score. The generated crowns met the mechanical standards such as roughness, and Sharp Mesh Corner Ratio (SMCR), making them suitable for precision 3-Dimensional manufacturing. Blending natural and technician-designed data improved learning of anatomical features like cusps and grooves, though some metrics such as groove distance and occlusal contact points were altered. Conclusions ToothGAN satisfies precision manufacturing demands and shows strong potential for clinical application in crown generation.

Details DOI

IROS Conference 2025 Conference Paper

Design of an Affordable, Fully-Actuated Biomimetic Hand for Dexterous Teleoperation Systems

Zhaoliang Wan
Zida Zhou
Zetong Bi
Zehui Yang
Hao Ding
Hui Cheng

This paper addresses the scarcity of affordable, fully-actuated five-fingered hands for dexterous teleoperation, which is crucial for collecting large-scale real-robot data within the "Learning from Demonstrations" paradigm. We introduce the prototype version of the RAPID Hand, the first low-cost, 20-degree-of-actuation (DoA) dexterous hand that integrates a novel anthropomorphic actuation and transmission scheme with an optimized motor layout and structural design to enhance dexterity. Specifically, the RAPID Hand features a universal phalangeal transmission scheme for the non-thumb fingers and an omnidirectional thumb actuation mechanism. Prioritizing affordability, the hand employs 3D-printed parts combined with custom gears for easier replacement and repair. We assess the RAPID Hand’s performance through quantitative metrics and qualitative testing in a dexterous teleoperation system, which is evaluated on three challenging tasks: multi-finger retrieval, ladle handling, and human-like piano playing. The results indicate that the RAPID Hand’s fully actuated 20-DoF design holds significant promise for dexterous teleoperation.

Details

EAAI Journal 2025 Journal Article

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

Qinghui Chen
LunQian Wang
Zekai Zhang
XingHua Wang
Weilin Liu
Bo Xia
Hao Ding
Jinglin Zhang

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

Details DOI

YNICL Journal 2025 Journal Article

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke

Hongchuan Zhang
Jun Guo
Jingchun Liu
Caihong Wang
Hao Ding
Tong Han
Jingliang Cheng
Chunshui Yu

Patients with chronic subcortical stroke undergo regional and network morphometric reorganizations beyond the lesion site, but the interplay between network and regional reorganization remains poorly understood. We aimed to clarify the reorganization patterns of the individualized differential structural covariance networks (IDSCN) in chronic subcortical stroke and investigate their associations with regional gray matter volume (GMV) changes and functional recovery. Structural MRI from four datasets enrolled 112 patients with chronic subcortical stroke (81 male, age: 55.82 ± 7.79) and 122 matched healthy controls (HC) (74 male; age: 55.28 ± 7.54). Network-based statistics were employed to identify aberrant IDSCN, Spearman correlation was conducted to assess the association between IDSCN and regional GMV alterations, and partial correlation was utilized to investigate the association between abnormal IDSCN and functional recovery. We identified 133 connections with balanced increased and decreased IDSCN. Aberrant IDSCN involved more regions than local GMV alterations, local GMV alteration exhibited intricate correlations with IDSCN, which could explain partly IDSCN reorganization (p < 0.05, corrected). Finally, abnormal IDSCN showed a weak association with long-term clinical recovery (p < 0.01). These findings reinforce the theory of adaptive network reorganization post-stroke and suggest that IDSCN may provide further insights into cortical reorganization and functional rehabilitation beyond regional morphometric measures.

Details DOI

AAAI Conference 2024 Conference Paper

Focus-Then-Decide: Segmentation-Assisted Reinforcement Learning

Chao Chen
Jiacheng Xu
Weijian Liao
Hao Ding
Zongzhang Zhang
Yang Yu
Rui Zhao

Visual Reinforcement Learning (RL) is a promising approach to achieve human-like intelligence. However, it currently faces challenges in learning efficiently within noisy environments. In contrast, humans can quickly identify task-relevant objects in distraction-filled surroundings by applying previously acquired common knowledge. Recently, foundational models in natural language processing and computer vision have achieved remarkable successes, and the common knowledge within these models can significantly benefit downstream task training. Inspired by these achievements, we aim to incorporate common knowledge from foundational models into visual RL. We propose a novel Focus-Then-Decide (FTD) framework, allowing the agent to make decisions based solely on task-relevant objects. To achieve this, we introduce an attention mechanism to select task-relevant objects from the object set returned by a foundational segmentation model, and only use the task-relevant objects for the subsequent training of the decision module. Additionally, we specifically employed two generic self-supervised objectives to facilitate the rapid learning of this attention mechanism. Experimental results on challenging tasks based on DeepMind Control Suite and Franka Emika Robotics demonstrate that our method can quickly and accurately pinpoint objects of interest in noisy environments. Consequently, it achieves a significant performance improvement over current state-of-the-art algorithms. Project Page: https://www.lamda.nju.edu.cn/chenc/FTD.html Code: https://github.com/LAMDA-RL/FTD

PDF Details DOI

AAAI Conference 2024 Short Paper

Multi-Expert Distillation for Few-Shot Coordination (Student Abstract)

Yujian Zhu
Hao Ding
Zongzhang Zhang

Ad hoc teamwork is a crucial challenge that aims to design an agent capable of effective collaboration with teammates employing diverse strategies without prior coordination. However, current Population-Based Training (PBT) approaches train the ad hoc agent through interaction with diverse teammates from scratch, which suffer from low efficiency. We introduce Multi-Expert Distillation (MED), a novel approach that directly distills diverse strategies through modeling across-episodic sequences. Experiments show that our algorithm achieves more efficient and stable training and has the ability to improve its behavior using historical contexts. Our code is available at https://github.com/LAMDA-RL/MED.

PDF Details DOI

IROS Conference 2024 Conference Paper

OPG-Policy: Occluded Push-Grasp Policy Learning with Amodal Segmentation

Hao Ding
Yiming Zeng 0008
Zhaoliang Wan
Hui Cheng 0002

Goal-oriented grasping in dense clutter, a fundamental challenge in robotics, demands an adaptive policy to handle occluded target objects and diverse configurations. Previous methods typically learn policies based on partially observable segments of the occluded target to generate motions. However, these policies often struggle to generate optimal motions due to uncertainties regarding the invisible portions of different occluded target objects across various scenes, resulting in low motion efficiency. To this end, we propose OPG-Policy, a novel framework that leverages amodal segmentation to predict occluded portions of the target and develop an adaptive push-grasp policy for cluttered scenarios where the target object is partially observed. Specifically, our approach trains a dedicated amodal segmentation module for diverse target objects to generate amodal masks. These masks and scene observations are mapped to the future rewards of grasp and push motion primitives via deep Q-learning to learn the motion critic. Afterward, the push and grasp motion candidates predicted by the critic, along with the relevant domain knowledge, are fed into the coordinator to generate the optimal motion implemented by the robot. Extensive experiments conducted in both simulated and real-world environments demonstrate the effectiveness of our approach in generating motion sequences for retrieving occluded targets, outperforming other baseline methods in success rate and motion efficiency.

Details

YNIMG Journal 2024 Journal Article

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

Liyuan Lin
Zhongyu Chang
Yu Zhang
Kaizhong Xue
Yingying Xie
Luli Wei
Xin Li
Zhen Zhao

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Details DOI

AAAI Conference 2023 Short Paper

Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)

Feng Chen
Chenghe Wang
Fuxiang Zhang
Hao Ding
Qiaoyong Zhong
Shiliang Pu
Zongzhang Zhang

Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems

Hao Wang
Yifei Ma
Hao Ding
Yuyang Wang

Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. REN tries to balance relevance and exploration while taking into account the uncertainty in the representations. Our theoretical analysis shows that REN can preserve the rateoptimal sublinear regret even when there exists uncertainty in the learned representations. Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, outperforming state-of-the-art models.

PDF Details

YNICL Journal 2021 Journal Article

Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy

Jiahui Zhang
Ling Wang
Hao Ding
Ke Fan
Qin Tian
Meng Liang
Zhihua Sun
Dapeng Shi

OBJECTIVE: The purpose of this study was to investigate whether the large-scale structural rich club organization was abnormal in patients with Leber's hereditary optic neuropathy (LHON) using diffusion tensor imaging (DTI), and the associations among disrupted brain structural connectivity, disease duration, and neuro-ophthalmological impairment. METHODS: Nineteen acute, 34 chronic LHON patients, and 36 healthy controls (HC) underwent DTI and neuro-ophthalmological measurements. The brain structural network and rich club organization were constructed based on deterministic fiber tracking at the individual level. Then intergroup differences among the acute, chronic LHON patients and healthy controls (HC) in three types of structural connections, including rich club, feeder, and local ones, were compared. Network-based Statistics (NBS) was also used to test the intergroup connectivity differences for each fiber. Several linear and nonlinear curve fit models were applied to explore the associations among large-scale brain structural connectivity, disease duration, and neuro-ophthalmological metrics. RESULTS: Compared to the HC, both the acute and chronic LHON patients had consistently significantly lower fractional anisotropy (FA) and higher radial diffusion (RD) for feeder connections (p 0.05, FDR correction). NBS also identified reduced FA of three feeder connections and five local ones linking visual, auditory, and basal ganglia areas in LHON patients (p 0.05, FDR correction). A significant negative correlation was shown between the retinal nerve fiber layer (RNFL) thickness and disease duration (p < 0.05, FDR correction). CONCLUSIONS: Abnormal rich club organization of the structural network was identified in both the acute and chronic LHON. Furthermore, our findings suggest the coexistence of both primary and secondary connectivity damage in the LHON.

Details DOI

YNIMG Journal 2021 Journal Article

Anatomical and functional coupling between the dorsal and ventral attention networks

Xinjun Suo
Hao Ding
Xi Li
Yaodan Zhang
Meng Liang
Yongqiang Zhang
Chunshui Yu
Wen Qin

Studies have indicated that the dorsal attention network (DAN) and the ventral attention network (VAN) functionally interact via several fronto-parietal connector hubs. However, the anatomical connectivity profiles of these connector hubs, and the coupling between the anatomical and functional connectivities of them, are still unknown. In the present study, we found that functional connector hubs anatomically bridged the DAN and VAN based on multimodal magnetic resonance imaging data from the Human Connectome Project (HCP) Consortium and an independent Chinese cohort. The three hubs had unique anatomical connectivity patterns with the attention sub-networks. For each connector hub, the pattern of anatomical connectivity resembled the functional one. Finally, the strength of the anatomical connectivity of these connector hubs was positively associated with the functional connectivity at the group- and individual-levels. Our findings help to better understand the anatomical mechanisms underlying the functional interactions between the DAN and the VAN.

Details DOI

AAAI Conference 2020 Conference Paper

Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

Yunsheng Bai
Hao Ding
Ken Gu
Yizhou Sun
Wei Wang

Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed. Recently, several data-driven approaches based on neural networks have been proposed, most of which model the graphgraph similarity as the inner product of their graph-level representations, with different techniques proposed for generating one embedding per graph. However, using one ﬁxeddimensional embedding per graph may fail to fully capture graphs in varying sizes and link structures—a limitation that is especially problematic for the task of graph similarity computation, where the goal is to ﬁnd the ﬁne-grained difference between two graphs. In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the need to use ﬁxed-dimensional vectors to represent whole graphs for their similarity computation. The model, GRAPH- SIM, achieves the state-of-the-art performance on four realworld graph datasets under six out of eight settings (here we count a speciﬁc dataset and metric combination as one setting), compared to existing popular methods for approximate Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation.

PDF Details

IJCAI Conference 2019 Conference Paper

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

Yunsheng Bai
Hao Ding
Yang Qiao
Agustin Marinovic
Ken Gu
Ting Chen
Yizhou Sun
Wei Wang

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGraphEmb, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGraphEmb achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.

PDF Details

IJCAI Conference 2011 Conference Paper

Control of Robotic Systems for Safe Interaction with Human Operators

Hao Ding

Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: Control of Robotic Systems for Safe Interaction with Human Operators. It consists of online motion generation for robotic manipulators interacting with dynamic obstacles and humans using a moving horizon scheme, modeling and long term prediction of human motion using probabilistic models and reachability analysis, and development of an HRI demonstration platform.

PDF Details DOI