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Ye Tian

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37 papers

2 author rows

JBHI Journal 2026 Journal Article

Causality-Adjusted Data Augmentation for Domain Continual Medical Image Segmentation

Zhanshi Zhu
Qing Dong
Gongning Luo
Wei Wang
Suyu Dong
Kuanquan Wang
Ye Tian
Guohua Wang

In domain continual medical image segmentation, distillation-based methods mitigate catastrophic forgetting by continuously reviewing old knowledge. However, these approaches often exhibit biases towards both new and old knowledge simultaneously due to confounding factors, which can undermine segmentation performance. To address these biases, we propose the Causality-Adjusted Data Augmentation (CauAug) framework, introducing a novel causal intervention strategy called the Texture-Domain Adjustment Hybrid-Scheme (TDAHS) alongside two causality-targeted data augmentation approaches: the Cross Kernel Network (CKNet) and the Fourier Transformer Generator (FTGen). (1) TDAHS establishes a domain-continual causal model that accounts for two types of knowledge biases by identifying irrelevant local textures (L) and domain-specific features (D) as confounders. It introduces a hybrid causal intervention that combines traditional confounder elimination with a proposed replacement approach to better adapt to domain shifts, thereby promoting causal segmentation. (2) CKNet eliminates confounder L to reduce biases in new knowledge absorption. It decreases reliance on local textures in input images, forcing the model to focus on relevant anatomical structures and thus improving generalization. (3) FTGen causally intervenes on confounder D by selectively replacing it to alleviate biases that impact old knowledge retention. It restores domain-specific features in images, aiding in the comprehensive distillation of old knowledge. Our experiments show that CauAug significantly mitigates catastrophic forgetting and surpasses existing methods in various medical image segmentation tasks.

AAAI Conference 2026 Conference Paper

PocketLLM: Ultimate Compression of Large Language Models via Meta Networks

Ye Tian
Chengcheng Wang
Jing Han
Yehui Tang
Kai Han

As Large Language Models (LLMs) continue to grow in size, storing and transmitting them on edge devices becomes increasingly challenging. Traditional methods like quantization and pruning struggle to achieve extreme compression of LLMs without sacrificing accuracy. In this paper, we introduce PocketLLM, a novel approach to compress LLMs in a latent space via meta-networks. A simple encoder network is proposed to project the weights of LLMs into discrete latent vectors, which are then represented using a compact codebook. A lightweight decoder network is employed to map the codebook's representative vectors back to the original weight space. This method allows for significant compression of the large weights in LLMs, consisting solely of a small decoder, a concise codebook, and an index. Extensive experiments show that PocketLLM achieves superior performance even at significantly high compression ratios, e.g., compressing Llama 2-7B by 10x with a negligible drop in accuracy.

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EAAI Journal 2026 Journal Article

The role of transformer models in advancing blockchain technology: A systematic survey

Tianxu Liu
Yanbin Wang
Jianguo Sun
Ye Tian
Yanyu Huang
Tao Xue
Peiyue Li
Yiwei Liu

As blockchain technology evolves, the demand for improved efficiency, security, and scalability increases, with Transformer models demonstrating significant potential to address these challenges. However, a systematic review of their blockchain applications is lacking. This paper fills this gap by surveying over 200 relevant studies, offering a comprehensive analysis of Transformer applications across four key areas: anomaly detection, smart contract vulnerability detection, cryptocurrency prediction, and code summarization. We adopt a domain-oriented classification framework that systematically organizes research progress and challenges, enhancing clarity and identifying trends. Furthermore, we offer granular sub-classification within each domain based on algorithmic types, data modalities, or information sources, delivering deeper insights into methodological advancements. Additionally, we conduct a dual-layered comparative analysis, contrasting Transformers with traditional deep learning methods and assessing variations among Transformer approaches within each domain to uncover best practices. We also explore challenges such as data privacy and model complexity, propose future research directions to tailor Transformers to blockchain-specific needs. We will continue to update the latest articles and their released source codes at https: //github. com/LTX001122/Transformers-Blockchain.

NeurIPS Conference 2025 Conference Paper

Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning

Yinjie Wang
Ling Yang
Ye Tian
Ke Shen
Mengdi Wang

Mathematical reasoning in large language models has been successfully incentivized through reinforcement learning with verifiable rewards, leading to improved one-shot precision. In this work, we turn our focus to the coding domain. Beyond one-shot precision, we highlight unit test generation as another key factor for enhancing coding ability, since accurate unit tests are essential for enabling self-checking and self-correction during inference. Traditional approaches for fine-tuning LLMs on unit test generation rely heavily on ground-truth code solutions in the training data. We propose CURE, a novel reinforcement learning framework with a dedicated reward design that co-evolves coding and unit test generation capabilities based on their interaction outcomes—without any ground-truth code as supervision. This approach enables flexible and scalable training and allows the unit tester to learn directly from the coder’s mistakes. Through extensive evaluations, we demonstrate that our CURE models, derived from base models of varying sizes, excel in both code generation and unit test generation. They naturally extend to downstream tasks such as test-time scaling—achieving a 6. 2\% improvement over the base model—and agentic unit test generation, with a 25. 1\% improvement. Our 4B model consistently outperforms Qwen3-4B while achieving 64. 8\% inference efficiency in unit test generation. Notably, we also find that the CURE model can serve as an effective reward model for reinforcement learning on base models, even in the absence of any labeled supervision.

IJCAI Conference 2025 Conference Paper

Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks

Shangshang Yang
Linrui Qin
Xiaoshan Yu
Ziwen Wang
Xueming Yan
Haiping Ma
Ye Tian

Cognitive diagnosis is crucial for intelligent education because of its ability to reveal students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diagnosis is criticized for the poor model interpretability due to the multi-layer perceptron(MLP) employed, even with the monotonicity assumption. Therefore, this paper proposes to empower the interpretability of neural cognitive diagnosis models through efficient Kolmogorov-Arnold networks (KANs), named KAN2CD, where KANs are used to enhance interpretability in two manners. Specifically, in the first manner, KANs are directly used to replace the used MLPs in existing neural CDMs; while in the second manner, the student embedding, exercise embedding, and concept embedding are directly processed by several KANs, and then their outputs are further combined and learned in a unified KAN to get final predictions. Besides, the implementation of original KANs is modified without affecting the interpretability to overcome the problem of training KANs slowly. Extensive experiments show KAN2CD outperforms traditional CDMs and slightly surpasses existing neural CDMs, and its learned structures ensure interpretability on par with traditional CDMs and better than neural CDMs. The datasets, associated code, and more experimental results are available at https: //github. com/null233QAQ/KAN2CD.

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NeurIPS Conference 2025 Conference Paper

HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation

Ling Yang
Xinchen Zhang
Ye Tian
Shiyi Zhang
Chenming Shang
Minghao Xu
Wentao Zhang
Bin Cui

The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion and Emu3 made notable strides in unified image understanding and generation. For the first time, we uncover a common phenomenon: the understanding capability of MLLMs is usually stronger than their generative capability, with a significant gap between them. Building on this insight, we propose HermesFlow, a simple and general framework designed to seamlessly bridge the gap between understanding and generation in MLLMs. Specifically, we take the homologous data as input to curate homologous preference data of both understanding and generation. Through Pair-DPO and self-play iterative optimization, HermesFlow effectively aligns multimodal understanding and generation using homologous preference data. Extensive experiments demonstrate the significant superiority of our approach over prior methods, particularly in narrowing the gap between multimodal understanding and generation. These findings highlight the potential of HermesFlow as a general alignment framework for next-generation multimodal foundation models.

ICLR Conference 2025 Conference Paper

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Yuheng Zhang
Dian Yu 0001
Baolin Peng
Linfeng Song
Ye Tian
Mingyue Huo
Nan Jiang 0008
Haitao Mi

Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In this paper, we explore RLHF under a general preference framework and approach it from a game-theoretic perspective. Specifically, we formulate the problem as a two-player game and propose a novel online algorithm, iterative Nash policy optimization (INPO). The key idea is to let the policy play against itself via no- regret learning, thereby approximating the Nash policy. Unlike previous methods, INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead, we introduce a new loss objective that is directly minimized over a preference dataset. We provide theoretical analysis for our approach and demonstrate its effectiveness through experiments on various representative benchmarks. With an LLaMA-3-8B-based SFT model, INPO achieves a 42.6% length-controlled win rate on AlpacaEval 2.0 and a 37.8% win rate on Arena-Hard, showing substantial improvement over the state-of-the-art online RLHF algorithms.

JMLR Journal 2025 Journal Article

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Ye Tian
Yuqi Gu
Yang Feng

Representation multi-task learning (MTL) has achieved tremendous success in practice. However, the theoretical understanding of these methods is still lacking. Most existing theoretical works focus on cases where all tasks share the same representation, and claim that MTL almost always improves performance. Nevertheless, as the number of tasks grows, assuming all tasks share the same representation is unrealistic. Furthermore, empirical findings often indicate that a shared representation does not necessarily improve single-task learning performance. In this paper, we aim to understand how to learn from tasks with similar but not exactly the same linear representations, while dealing with outlier tasks. Assuming a known intrinsic dimension, we propose a penalized empirical risk minimization method and a spectral method that are adaptive to the similarity structure and robust to outlier tasks. Both algorithms outperform single-task learning when representations across tasks are sufficiently similar and the proportion of outlier tasks is small. Moreover, they always perform at least as well as single-task learning, even when the representations are dissimilar. We provide information-theoretic lower bounds to demonstrate that both methods are nearly minimax optimal in a large regime, with the spectral method being optimal in the absence of outlier tasks. Additionally, we introduce a thresholding algorithm to adapt to an unknown intrinsic dimension. We conduct extensive numerical experiments to validate our theoretical findings. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

AAAI Conference 2025 Conference Paper

LiteSearch: Efficient Tree Search with Dynamic Exploration Budget for Math Reasoning

Ante Wang
Linfeng Song
Ye Tian
Baolin Peng
Dian Yu
Haitao Mi
Jinsong Su
Dong Yu

Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they often require more than 10 times the computational resources of greedy decoding due to wasteful search strategies, making them difficult to be deployed in practical applications. This study introduces a novel guided tree search algorithm with a goal-directed heuristic function and node-level exploration budget (maximum number of children) calculation to tackle this issue. By considering the search progress towards the final answer (history) and the guidance from a value network (future) trained without any step-wise annotations, our algorithm iteratively selects the most promising tree node before expanding it within the boundaries of the allocated computational budget. Experiments conducted on the GSM8K, TabMWP, and MATH datasets demonstrate that our method not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods.

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NeurIPS Conference 2025 Conference Paper

MMaDA: Multimodal Large Diffusion Language Models

Ling Yang
Ye Tian
Bowen Li
Xinchen Zhang
Ke Shen
Yunhai Tong
Mengdi Wang

We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal understanding, and text-to-image generation. The approach is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality-agnostic design, eliminating the need for modality-specific components. This architecture ensures seamless integration and processing across different data types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning strategy that curates a unified CoT format across modalities. By aligning reasoning processes between textual and visual domains, this strategy facilitates cold-start training for the final reinforcement learning (RL) stage, thereby enhancing the model's ability to handle complex tasks from the outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm specifically tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and generation tasks, ensuring consistent performance improvements. Experimental results demonstrate that MMaDA-8B exhibits strong generalization capabilities as a unified multimodal foundation model. It surpasses powerful models like LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in multimodal understanding, and excels over SDXL and Janus in text-to-image generation. These achievements highlight MMaDA's effectiveness in bridging the gap between pretraining and post-training within unified diffusion architectures, providing a comprehensive framework for future research and development. We open-source our code and trained models at: https: //github. com/Gen-Verse/MMaDA

NeurIPS Conference 2025 Conference Paper

Multi-Agent Collaboration via Evolving Orchestration

Yufan Dang
Chen Qian
Xueheng Luo
Jingru Fan
Zihao Xie
Ruijie Shi
Weize Chen
Cheng Yang

Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving. While recent research explores multi-agent collaboration among LLMs, most approaches rely on static organizational structures that struggle to adapt as task complexity and agent numbers grow, resulting in coordination overhead and inefficiencies. To this end, we propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states. This orchestrator is trained via reinforcement learning to adaptively sequence and prioritize agents, enabling flexible and evolvable collective reasoning. Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs. Analyses further reveal that the key improvements consistently stem from the emergence of more compact, cyclic reasoning structures under the orchestrator’s evolution. Our code is available at https: //github. com/OpenBMB/ChatDev/tree/puppeteer.

EAAI Journal 2025 Journal Article

Multiple scales fusion and query matching stabilization for detection with transformer

Shenyu Du
Xijun Liang
Kun Wu
Ye Tian
Yue Liu
Ling Jian

Recent advances in object detection with Transformer-based models like Detection with Transformer (DETR) have improved performance, but challenges remain with multi-scale fused features. These features introduce redundant tokens and bias toward larger objects, slowing down training. To overcome these issues, we propose two novel encoders: the Similarity-based Deduplication Encoder (SDE) and the Hybrid Multi-object Encoder (HMoE). HMoE employs an offset-based attention window to enhance local attention for objects of varying sizes across feature maps, while SDE reduces redundancy by calculating attention scores across multiple scales. Additionally, we introduce a One-to-many Positive Matching (OmPM) strategy to improve query stability. OmPM generates query vectors from multiple positive samples, resulting in more diverse and semantically meaningful queries. Our model demonstrates substantial performance improvements. On the Visual Object Classes Challenge 2007 dataset, it achieves a +5. 04 mean Average Precision (mAP) and +5. 1 Average Precision for small objects (APs) for small objects in just 24 epochs. On the Microsoft Common Objects in Context (COCO) dataset, the model reaches 50. 1 mAP and 34. 2 APs in only 8 epochs, and 52. 4 mAP and 35. 6 APs in 24 epochs. This significantly accelerates convergence, reducing training time by 66% compared to benchmarks while maintaining or exceeding detection accuracy. Furthermore, our model achieves 27 Frames Per Second (FPS) on the COCO dataset, setting a new record among DETR-like methods with high detection accuracies.

NeurIPS Conference 2025 Conference Paper

OCTDiff: Bridged Diffusion Model for Portable OCT Super-Resolution and Enhancement

Ye Tian
Angela McCarthy
Gabriel Gomide
Nancy Liddle
Jedrzej Golebka
Royce Chen
Jeff Liebmann
Kaveri Thakoor

Medical imaging super-resolution is critical for improving diagnostic utility and reducing costs, particularly for low-cost modalities such as portable Optical Coherence Tomography (OCT). We propose OCTDiff, a bridged diffusion model designed to enhance image resolution and quality from portable OCT devices. Our image-to-image diffusion framework addresses key challenges in the conditional generation process of denoising diffusion probabilistic models (DDPMs). We introduce Adaptive Noise Aggregation (ANA), a novel module to improve denoising dynamics within the reverse diffusion process. Additionally, we integrate Multi-Scale Cross-Attention (MSCA) into the U-Net backbone to capture local dependencies across spatial resolutions. To address overfitting on small clinical datasets and to preserve fine structural details essential for retinal diagnostics, we design a customized loss function guided by clinical quality scores. OCTDiff outperforms convolutional baselines and standard DDPMs, achieving state-of-the-art performance on clinical portable OCT datasets. Our model and its downstream applications have the potential to generalize to other medical imaging modalities and revolutionize the current workflow of ophthalmic diagnostics. The code is available at https: //github. com/AI4VSLab/OCTDiff.

NeurIPS Conference 2025 Conference Paper

RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis

YANG SONGXIAO
Haolin Wang
Yao Fu
Ye Tian
Tamostu Kamishima
Masayuki Ikebe
Yafei Ou
Masatoshi Okutomi

Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations. (i) The wrist comprises numerous small bones with narrow joint spaces, complex structures, and frequent overlaps, requiring detailed anatomical knowledge for accurate annotation. (ii) Disease progression in RA often leads to osteophyte, bone erosion (BE), and even bony ankylosis, which alter bone morphology and increase annotation difficulty, necessitating expertise in rheumatology. This work presents a multi-task dataset for wrist bone in CR, including two tasks: (i) wrist bone instance segmentation and (ii) Sharp/van der Heijde (SvdH) BE scoring, which is the first public resource for wrist bone instance segmentation. This dataset comprises 1048 wrist conventional radiographs of 388 patients from six medical centers, with pixel-level instance segmentation annotations for 618 images and SvdH BE scores for 800 images. This dataset can potentially support a wide range of research tasks related to RA, including joint space narrowing (JSN) progression quantification, BE detection, bone deformity evaluation, and osteophyte detection. It may also be applied to other wrist-related tasks, such as carpal bone fracture localization. We hope this dataset will significantly lower the barrier to research on wrist RA and accelerate progress in CAD research within the RA-related domain. Benchmark & Code: https: //github. com/YSongxiao/RAM-W600Data & Dataset Card: https: //huggingface. co/datasets/TokyoTechMagicYang/RAM-W600

EAAI Journal 2025 Journal Article

Residual generative adversarial network-driven Data enhancement for magnetic flux leakage-based defect recognition in oil and gas pipelines

Bin Liu
Liying Ding
Luyao He
Lijian Yang
Xiaobei Zhang
Ye Tian

Magnetic flux leakage (MFL) in-line inspection technology is widely regarded as one of the most effective techniques for assessing the health of long-distance oil and gas pipelines. However, the limited availability of MFL data for defects presents remarkable challenges in training defect recognition models based on deep learning. Consequently, this paper proposed a generative adversarial network (Res-CosGAN), integrating deep residual modules with cosine similarity loss. In comparison to existing data enhancement techniques for defect recognition networks, Res-CosGAN possesses three key advantages. Firstly, it directly utilizes the MFL data matrix as the input, thereby significantly reducing the time required to convert MFL data into images. Secondly, the method incorporates skip connections in the generator and introduces cosine similarity loss, mitigating the vanishing gradient problem. Lastly, utilizing the characteristics of MFL data, it integrates the physical information of defects into the generation loss, thereby minimizing the loss of data feature information and enhancing the quality of the generated data. Experimental results across multiple datasets indicated that, when applied to defect recognition networks, this data enhancement method could increase the average recognition accuracy by 25. 1 % compared with using raw MFL data and by 7. 4 % compared with the best results achieved with other enhancement networks. These findings demonstrate the effectiveness of the proposed method in pipeline MFL defect detection and its potential contribution to pipeline transportation safety analysis and assessment.

JBHI Journal 2025 Journal Article

Utilizing Artificial Intelligence (AI) and Image Recognition Technologies to achieve Potential Wireless Sensing of Surgical Resection Boundaries for Benign Prostatic Hyperplasia (BPH)

Meishan Zhao
Jingcheng Lv
Xuanhao Li
Tianming Cheng
Fangzhou Zhao
Liangshuo Zhang
Yichen Zhu
Mingjun Shi

Background and Objective: Precise intraoperative sensing is critical for optimizing surgical outcomes and patient safety. This study proposes an AI driven model, leveraging the nnU-Net architecture, to achieve real-time identification of surgical resection boundaries during transurethral resection of the prostate (TURP). While currently relying on image-based recognition, this work lays the foundation for integrating wireless sensing technologies, potentially transforming non-contact surgical guidance and biomedical applications. Methods: In this research, we presented an nnU Net model capable of automatically predicting benign prostatic hyperplasia tissues, specifically encompassing the prostatic capsule, verumontanum, and bladder neck. The model was constructed using a contracting path and an expanding path, with Leaky ReLUs employed to fine-tune the learning rate. We conducted separate evaluations to assess the AI aided recognition performance for the prostatic capsule, verumontanum, and bladder neck. Results: This study employed a 5-fold cross-validation approach to process each binary dataset. Specifically, for the prostate surgery capsule group, 228 images comprised the training set, while 58 images constituted the validation set. In the verumontanum group, 210 images were designated for training and 53 for validation. Similarly, in the bladder neck group, 236 samples were allocated to the training set and 59 to the validation set. The nnU-Net model was then trained and validated using these datasets, with its performance being assessed through the use of Dice coefficient, mean Intersection over Union (mIoU), mean Accuracy (mAcc), and overall Accuracy (aAcc) metrics. Prostate surgical capsule: mDice = 0. 67, mIoU = 0. 579, mAcc = 0. 7, aAcc = 0. 926; Verumontanum: mDice = 0. 837, mIoU = 0. 742, mAcc = 0. 835, aAcc = 0. 943; Bladder neck: mDice = 0. 76, mIoU = 0. 667, mAcc = 0. 748, aAcc = 0. 967These results demonstrate the model's performance in predicting and delineating these surgical boundaries. Conclusons Our findings highlight the potential of AI in real-time human sensing and pave the way for future integration with wireless technologies, enabling non-contact detection and precision healthcare solutions. This approach aligns with emerging trends in personalized biomedical sensing and wireless healthcare technologies.

EAAI Journal 2025 Journal Article

Varied granularity encoding based evolutionary algorithm for multi-objective intensity-modulated radiation therapy optimization

Langchun Si
Xingyi Zhang
Ye Tian
Ruifen Cao
Shangshang Yang
Limiao Zhang

Intensity-modulated radiation therapy is an interesting multi-objective optimization problem, which holds a large number of aperture shape-related variables, posing a stiff challenge to existing algorithms. To efficiently solve this problem, we propose a varied granularity encoding method in this paper, where the granularity of encoding of the shape in the multi-leaf collimator is progressively refined during the optimization. Specifically, at the beginning of the search, a coarse encoding is adopted by dividing the aperture shape-related variables into several groups and representing each group by one bit, which achieves effective search space reduction for the aperture shape. During the evolution, the granularity of encoding aperture shape-related variables is gradually varied from coarse to fine by reducing the size of each group until only one variable is contained in the group. With the proposed varied granularity encoding method, an evolutionary algorithm is suggested based on a popular evolutionary multi-objective framework (NSGA-II), where an adaptive switching method is developed to determine whether the granularity level needs to be reduced according to the convergence status of the population. The experiment empirically investigates the performance of the proposed varied granularity encoding method based evolutionary algorithm on eight clinical instances with the number of aperture shape-related variables ranging from 1932 to 3180. Compared with seven representative evolutionary algorithms and one traditional direct aperture optimization algorithm, the proposed algorithm demonstrates statistically significant improvements in hypervolume, inverted generational distance, and dose-volume histogram. The experimental results reveal that the proposed algorithm not only exhibits competitiveness but reduces computational time in radiotherapy optimization.

EAAI Journal 2024 Journal Article

A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization

Yajie Zhang
Chengming Wu
Ye Tian
Xingyi Zhang

Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co-evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem.

EAAI Journal 2024 Journal Article

Evolution prediction of flame structure in a hydrogen-fueled scramjet combustor based on lightweight deformable convolutional residual neural network

Jiawen Deng
Mingming Guo
Erda Chen
Ye Tian
Chunmei Chen
Hua Zhang

Accurate prediction and fine feature identification of flame structure evolution within scramjet combustors are crucial for exploring stable combustion mechanisms and organizing efficient combustion. This study introduces an innovative method for predicting the dynamic evolution of supersonic flame structures. It presents a cross-temporal prediction model for the flame field that employs a lightweight, deformable convolutional residual neural network (DRCN). Ground-based pulse combustion wind tunnel tests are conducted at 2. 5 Mach with varying equivalence ratios. Synchronous measurements are taken to collect spatiotemporal multisource data, which is employed to compile a dataset of the combustion chamber wall pressure and flame field data. Pressure signals from sensors on the combustion chamber's upper and lower walls, along with signals spanning different time intervals, served as inputs to predict the evolution of the flame field structure. Experiments over multiple and extensive time spans are performed to compare and analyze performance differences between the different models. The experimental results demonstrated that the DRCN model surpasses other models in the test sets, achieving up to a 36. 11% increase in peak signal-to-noise ratio, a 33. 24% improvement in structural similarity index, and a 3. 81% enhancement in the correlation coefficient. Predictions on the dynamic evolution of initial flame kernels, flame contours, and flame distribution show superior performance. This research also investigates the network's lightweight design, achieving a model size of merely 1 MB to further enhance the model's inference speed. It presents significant engineering value for the real-time prediction of flame propagation in supersonic combustor.

NeurIPS Conference 2024 Conference Paper

RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Xinchen Zhang
Ling Yang
Yaqi Cai
Zhaochen Yu
Kai-Ni Wang
Jiake Xie
Ye Tian
Minkai Xu

Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantages of text-to-image models and spatial-aware image diffusion models (e. g. , layout, keypoints and segmentation maps) to enhance both realism and compositionality of the generated images. An intuitive and novel balancer is proposed to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Extensive experiments show that our RealCompo consistently outperforms state-of-the-art text-to-image models and spatial-aware image diffusion models in multiple-object compositional generation while keeping satisfactory realism and compositionality of the generated images. Notably, our RealCompo can be seamlessly extended with a wide range of spatial-aware image diffusion models and stylized diffusion models. Code is available at: https: //github. com/YangLing0818/RealCompo

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EAAI Journal 2024 Journal Article

Supersonic combustion flow field reconstruction based on multi-view domain adaptation generative network in scramjet combustor

Mingming Guo
Erda Chen
Ye Tian
Linjing Li
Mengqi Xu
Jialing Le
Hua Zhang

The efficient and precise reconstruction of supersonic combustion flow fields enables real-time sensing and control of hypersonic vehicles. However, current flow field reconstruction methodologies often suffer from limited prediction accuracy, poor generalization capabilities, and high model energy consumption. In this research, a robust and efficient multi-source data fusion framework for combustion flow field reconstruction based on a multi-view domain adaptation generative network (MV-DAGN) is developed and evaluated. In order to utilize multivariate flow field data, this study adopts a multi-view learning approach to thoroughly integrate various physical field data. It introduces an MV-DAGN framework for training models on multi-source data from supersonic combustor with a Mach 2. 5 low equivalence ratio derived from ground-based pulse combustion wind tunnels. The concept of transfer learning is incorporated, and the fusion of wall pressure and flame field data is utilized to validate the flow field reconstruction by including a limited set of high equivalence ratio data. Subsequently, to diminish the model's training duration and enhance the prediction speed of the combustion flow field, a lightweight MV-DAGN model is established.

NeurIPS Conference 2024 Conference Paper

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Ye Tian
Baolin Peng
Linfeng Song
Lifeng Jin
Dian Yu
Lei Han
Haitao Mi
Dong Yu

Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Self-correction and self-learning emerge as viable solutions, employing strategies that allow LLMs to refine their outputs and learn from self-assessed rewards. Yet, the efficacy of LLMs in self-refining its response, particularly in complex reasoning and planning task, remains dubious. In this paper, we introduce AlphaLLM for the self-improvements of LLMs, which integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop, thereby enhancing the capabilities of LLMs without additional annotations. Drawing inspiration from the success of AlphaGo, AlphaLLM addresses the unique challenges of combining MCTS with LLM for self-improvement, including data scarcity, the vastness search spaces of language tasks, and the subjective nature of feedback in language tasks. AlphaLLM is comprised of prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Our experimental results in mathematical reasoning tasks demonstrate that AlphaLLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs.

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ICML Conference 2024 Conference Paper

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

Ye Tian
Haolei Weng
Yang Feng 0002

While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated EM algorithms have gained popularity in practice, however, their theoretical foundations are often lacking. In this paper, we first introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models, which supplements the existing federated EM algorithms by considering task heterogeneity and potential adversarial attacks. We present a comprehensive finite-sample theory that holds for general mixture models, then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions. Our theory elucidates when and how FedGrEM outperforms local single-task learning with insights extending to existing federated EM algorithms. This bridges the gap between their practical success and theoretical understanding. Our numerical results validate our theory, and demonstrate FedGrEM’s superiority over existing unsupervised federated learning benchmarks.

NeurIPS Conference 2024 Conference Paper

VideoTetris: Towards Compositional Text-to-Video Generation

Ye Tian
Ling Yang
Haotian Yang
Yuan Gao
Yufan Deng
Jingmin Chen
Xintao Wang
Zhaochen Yu

Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in object numbers. To address these limitations, we propose VideoTetris, a novel framework that enables compositional T2V generation. Specifically, we propose spatio-temporal compositional diffusion to precisely follow complex textual semantics by manipulating and composing the attention maps of denoising networks spatially and temporally. Moreover, we propose a new dynamic-aware data processing pipeline and a consistency regularization method to enhance the consistency of auto-regressive video generation. Extensive experiments demonstrate that our VideoTetris achieves impressive qualitative and quantitative results in compositional T2V generation. Code is available at: https: //github. com/YangLing0818/VideoTetris

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ICLR Conference 2024 Conference Paper

VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs

Ling Yang 0006
Ye Tian
Minkai Xu
Zhongyi Liu 0001
Shenda Hong
Wei Qu
Wentao Zhang 0001
Bin Cui 0001

GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output representations of teacher GNN. Existing methods mainly make the MLP to mimic the GNN predictions over a few class labels. However, the class space may not be expressive enough for covering numerous diverse local graph structures, thus limiting the performance of knowledge transfer from GNN to MLP. To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation. Specifically, we propose a variant of VQ-VAE to learn a structure-aware tokenizer on graph data that can encode each node's local substructure as a discrete code. The discrete codes constitute a codebook as a new graph representation space that is able to identify different local graph structures of nodes with the corresponding code indices. Then, based on the learned codebook, we propose a new distillation target, namely soft code assignments, to directly transfer the structural knowledge of each node from GNN to MLP. The resulting framework VQGraph achieves new state-of-the-art performance on GNN-to-MLP distillation in both transductive and inductive settings across seven graph datasets. We show that VQGraph with better performance infers faster than GNNs by 828×, and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. Our code is available at https://github.com/YangLing0818/VQGraph

NeurIPS Conference 2023 Conference Paper

Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing

Shangshang Yang
Xiaoshan Yu
Ye Tian
Xueming Yan
Haiping Ma
Xingyi Zhang

Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises. Despite the excellent performance of existing Transformer-based KT approaches, they are criticized for the manually selected input features for fusion and the defect of single global context modelling to directly capture students' forgetting behavior in KT, when the related records are distant from the current record in terms of time. To address the issues, this paper first considers adding convolution operations to the Transformer to enhance its local context modelling ability used for students' forgetting behavior, then proposes an evolutionary neural architecture search approach to automate the input feature selection and automatically determine where to apply which operation for achieving the balancing of the local/global context modelling. In the search space, the original global path containing the attention module in Transformer is replaced with the sum of a global path and a local path that could contain different convolutions, and the selection of input features is also considered. To search the best architecture, we employ an effective evolutionary algorithm to explore the search space and also suggest a search space reduction strategy to accelerate the convergence of the algorithm. Experimental results on the two largest and most challenging education datasets demonstrate the effectiveness of the architecture found by the proposed approach.

YNIMG Journal 2023 Journal Article

Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes

Léonie Borne
Ye Tian
Michelle K. Lupton
Johan N. van der Meer
Jayson Jeganathan
Bryan Paton
Nikitas Koussis
Christine C. Guo

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

ICLR Conference 2023 Conference Paper

Quality-Similar Diversity via Population Based Reinforcement Learning

Shuang Wu
Jian Yao 0008
Haobo Fu
Ye Tian
Chao Qian 0001
Yaodong Yang 0001
Qiang Fu 0016
Wei Yang 0032

Diversity is a growing research topic in Reinforcement Learning (RL). Previous research on diversity has mainly focused on promoting diversity to encourage exploration and thereby improve quality (the cumulative reward), maximizing diversity subject to quality constraints, or jointly maximizing quality and diversity, known as the quality-diversity problem. In this work, we present the quality-similar diversity problem that features diversity among policies of similar qualities. In contrast to task-agnostic diversity, we focus on task-specific diversity defined by a set of user-specified Behavior Descriptors (BDs). A BD is a scalar function of a trajectory (e.g., the fire action rate for an Atari game), which delivers the type of diversity the user prefers. To derive the gradient of the user-specified diversity with respect to a policy, which is not trivially available, we introduce a set of BD estimators and connect it with the classical policy gradient theorem. Based on the diversity gradient, we develop a population-based RL algorithm to adaptively and efficiently optimize the population diversity at multiple quality levels throughout training. Extensive results on MuJoCo and Atari demonstrate that our algorithm significantly outperforms previous methods in terms of generating user-specified diverse policies across different quality levels.

ICML Conference 2022 Conference Paper

Greedy when Sure and Conservative when Uncertain about the Opponents

Haobo Fu
Ye Tian
Hongxiang Yu
Weiming Liu 0004
Shuang Wu
Jiechao Xiong
Ying Wen 0001
Kai Li 0022

We develop a new approach, named Greedy when Sure and Conservative when Uncertain (GSCU), to competing online against unknown and nonstationary opponents. GSCU improves in four aspects: 1) introduces a novel way of learning opponent policy embeddings offline; 2) trains offline a single best response (conditional additionally on our opponent policy embedding) instead of a finite set of separate best responses against any opponent; 3) computes online a posterior of the current opponent policy embedding, without making the discrete and ineffective decision which type the current opponent belongs to; and 4) selects online between a real-time greedy policy and a fixed conservative policy via an adversarial bandit algorithm, gaining a theoretically better regret than adhering to either. Experimental studies on popular benchmarks demonstrate GSCU’s superiority over the state-of-the-art methods. The code is available online at \url{https: //github. com/YeTianJHU/GSCU}.

YNIMG Journal 2021 Journal Article

High-resolution connectomic fingerprints: Mapping neural identity and behavior

Sina Mansour L
Ye Tian
B.T. Thomas Yeo
Vanessa Cropley
Andrew Zalesky

Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.

YNIMG Journal 2021 Journal Article

Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?

Ye Tian
Andrew Zalesky

Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.

JMLR Journal 2021 Journal Article

RaSE: Random Subspace Ensemble Classification

Ye Tian
Yang Feng

We propose a flexible ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where each weak learner is a base classifier trained in a subspace optimally selected from a collection of random subspaces. To conduct subspace selection, we propose a new criterion, ratio information criterion (RIC), based on weighted Kullback-Leibler divergence. The theoretical analysis includes the risk and Monte-Carlo variance of the RaSE classifier, establishing the screening consistency and weak consistency of RIC, and providing an upper bound for the misclassification rate of the RaSE classifier. In addition, we show that in a high-dimensional framework, the number of random subspaces needs to be very large to guarantee that a subspace covering signals is selected. Therefore, we propose an iterative version of the RaSE algorithm and prove that under some specific conditions, a smaller number of generated random subspaces are needed to find a desirable subspace through iteration. An array of simulations under various models and real-data applications demonstrate the effectiveness and robustness of the RaSE classifier and its iterative version in terms of low misclassification rate and accurate feature ranking. The RaSE algorithm is implemented in the R package RaSEn on CRAN. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

EAAI Journal 2021 Journal Article

Z E -numbers: A new extended Z-numbers and its application on multiple attribute group decision making

Ye Tian
Xiangjun Mi
Yunpeng Ji
Bingyi Kang

As the core mechanism of intelligent systems, decision-making has received widespread attention in recent years. As decision-making environments become more complex, large amounts of data are fuzzy and partially reliable. Zadeh proposed the concept of the Z-numbers, this more anthropomorphic fuzzy set representation framework describes the simultaneous existence of probability measures and probability measures of random variables, and it is regarded as a very powerful tool for modeling uncertain information. However, the representation of Z-numbers still has limitations. Therefore, we propose a new extended Z-numbers, Z E = ( ( A, B ), E ), E is the credibility. As the objective reliability of ( A, B ), it restricts the original Z-numbers. At the same time, the conversion function between them is also defined. Based on this, we proposed a multi-attribute group decision-making (MAGDM) method considering the attitudes of decision-makers. Application examples show the rationality and effectiveness of the proposed methodology, and the superiority of this method is further illustrated through comparison and discussion with other methods.

EAAI Journal 2019 Journal Article

Derive knowledge of Z-number from the perspective of Dempster–Shafer evidence theory

Qing Liu
Ye Tian
Bingyi Kang

Z-number, combined with constraint and reliability of the information, is an effective frame to simulate the thinking of humans. How to derive knowledge of Z-numbers, especially from the objective data may become a fascinating and open issue. In this paper, a method of deriving knowledge of Z-numbers from the perspective of Dempster–Shafer theory is proposed. The proposed method considers the Z-number generating from objective and subjective data using Dempster–Shafer theory. Some numerical examples and experimental simulations are used to illustrate the effectiveness of the proposed methodology.

YNIMG Journal 2018 Journal Article

Characterizing the functional connectivity diversity of the insula cortex: Subregions, diversity curves and behavior

Ye Tian
Andrew Zalesky

The connectivity of the insula cortex is diverse. We present new models to characterize the resting-state connectional diversity of the human insula cortex and perform model selection using high-quality fMRI data from the Human Connectome Project. We first attempt to parcellate the insula into distinct subregions using traditional clustering methods, but find that the resulting subregions are not homogeneous and that the optimal number of subregions is substantially influenced by data smoothness. We then introduce the concept of a diversity curve, which we use to continuously parameterize the insula's Laplacian eigenmap with respect to streamlines propagated through the eigenmap's gradient field. To perform model selection, we compare the insula's diversity curve to benchmark diversity curves for: i) two distinct regions; ii) a continuum of gradual change; and, iii) an absence of any connectional diversity (i. e. homogenous region). Of the three benchmarks tested, we find that the insula's connectional diversity is most parsimoniously modeled as continuum of gradual change, from dorsal-posterior to ventral-anterior. We find that individuals who score high on measures of positive affect, self-efficacy, emotion recognition, motor dexterity and gustation show greater diversity within the anterior insula. Our findings are replicated using data from a second fMRI session. We conclude that the functional connectivity diversity of the insula can be characterized parsimoniously as a continuum, avoiding the vexed task of determining an optimal number of insula subregions, and that inter-individual variation in this continuum can explain significant variation in behavior.

ICRA Conference 2017 Conference Paper

Estimation of EMG signal for shoulder joint based on EEG signals for the control of upper-limb power assistance devices

Hongbo Liang
Chi Zhu 0001
Masataka Yoshioka
Naoya Ueda
Ye Tian
Yu Iwata
Haoyong Yu
Feng Duan 0006

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to realize the power assistance. The positions of the electrodes where the motion information of shoulder joint is effectively and exactly extracted are discussed, and a linear model that correlates the EMG to the EEG signal is constructed utilizing motion-related features extracted from multi-location EEG measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG using Principal Component Analysis (PCA) method. The proposed approach is experimentally verified, and an average correlation coefficients are as high as about 0. 90 for different subjects are obtained between the estimated and the actually measured EMG signal. Our results suggest that the estimation of EMG based on EEG is feasible. This demonstrates the potential of using EEG signals to support human activities via brain-machine interface.

AIIM Journal 2017 Journal Article

Knowledge graph for TCM health preservation: Design, construction, and applications

Tong Yu
Jinghua Li
Qi Yu
Ye Tian
Xiaofeng Shun
Lili Xu
Ling Zhu
Hongjie Gao

Traditional Chinese Medicine (TCM) is one of the important non-material cultural heritages of the Chinese nation. It is an important development strategy of Chinese medicine to collect, analyzes, and manages the knowledge assets of TCM health care. As a novel and massive knowledge management technology, knowledge graph provides an ideal technical means to solve the problem of “Knowledge Island” in the field of traditional Chinese medicine. In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources. This knowledge graph can facilitate various knowledge services such as knowledge visualization, knowledge retrieval, and knowledge recommendation, and helps the sharing, interpretation, and utilization of TCM health care knowledge.