Ying Li

EAAI Journal 2026 Journal Article

A multivariate long-term time-series prediction model for water quality based on Transformer architecture with spectral reconstruction optimizer

• Dashe Li
• Ying Li
• Lu Liu
• Xiaodong Ji
• Haoran Xing

Predicting key water quality parameters, such as dissolved oxygen, is of considerable importance for water environment monitoring and aquaculture management. It also provides scientific support to achieve ecological protection and sustainable development. However, long-term prediction faces challenges such as difficulty in nonstationary information modeling, insufficient perception of intervariable dependency structures, and limited feature expression. This study proposes a multivariate long-term time-series prediction model based on the Transformer architecture. First, a spectral reconstruction optimizer is designed to explicitly enhance and reconstruct the intermediate frequency energy in the frequency domain to solve the information asymmetry problem caused by the dominance of low frequencies in the frequency space distribution of time series. Second, a graph-structured feature modulation mechanism is constructed to dynamically adjust variable features by integrating dual-pooling compression and graph-structured modeling operations to explore potential cross-variable synergies. Finally, dual-stream hybrid attention is used. This mechanism introduces a learnable fusion of Squared Rectified Linear unit (ReLU 2 ) and softmax-attention based on a dense–sparse dual-branch structure, considering both information retention and key dependency enhancement. This study conducted experiments on six ocean datasets for 168 time steps in the future, indicating that the proposed model outperformed seven baseline models with higher accuracy and stronger generalization ability. For example, on the BaffleCreek dataset, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model were reduced by an average of 17. 50% and 16. 45%, respectively. Similarly, on the Shandong Peninsula dataset, the reductions were 31. 29% and 31. 24% for MAE and RMSE, respectively.

EAAI Journal 2026 Journal Article

Corrigendum to “An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling” [Eng. Appl. Artif. Intell. (137), Part B, November 2024, 109221]

• Xuxue Sun
• Nan Kong
• Weiping Ding
• Ying Li
• Nazmus Sakib
• Hao Zeng
• Hongdao Meng
• Chris Masterson

EAAI Journal 2026 Journal Article

Global relationship awareness 3-dimensional object detection using 4-dimensional radar

• Pianzhang Duan
• Li Wang
• Cheng Fang
• Ziying Song
• Ming Gao
• Mo Zhou
• Ying Li
• Yibo Zhang

4D (4-dimensional) radar sensing technology is essential for high-precision autonomous driving perception systems, as its superior detection capabilities at increased distances, compared to traditional LiDAR (Light Detection and Ranging). However, due to the sparsity of point clouds and the low resolution of millimeter-wave radar, voxel-based methods may fail to detect distant or closely adjacent objects, leading to inadequate detection accuracy. To mitigate the accuracy issues arising from the sparse nature of point clouds in such scenarios, we propose a novel object detection network: GRA-Net (Global Relation-Aware object detection Network). By leveraging a self-attention mechanism, GRA-Net effectively learns critical features from each radar pillar, enhancing the network’s capacity to capture relevant information about nearby objects. Furthermore, we introduce a global perception module that integrates key features within the pillars and global features, mitigating the impact of point cloud sparsity, particularly in distant regions. We conducted a series of experiments to evaluate the performance of GRA-Net. On the Astyx HiRes 2019 dataset, our method achieved 33. 63 mAP (mean Average Precision) and 43. 93 mAP at the moderate level; On the View-of-Delft dataset, our method achieved 47. 74 mAP in the entire annotated area and 69. 25 mAP in the driving corridor area.

AAAI Conference 2026 Conference Paper

Learning Whom to Align With: Progressive Anomaly Combination Detection for Partially View-Aligned Clustering

• Hang Gao
• Zuosong Cai
• Yuze Li
• Cheng Liu
• Gaoyang Li
• Ying Li
• Wei Du
• You Zhou

Partially View-aligned Clustering (PVC) addresses the challenge of partial view alignment in multi-view learning by leveraging complementary and consistent information. While existing PVC methods show promise, most rely on distance-based strategies that are sensitive to view-specific details and noise, limiting their robustness. In this work, we propose a novel view alignment strategy that reformulates the alignment task as an anomaly detection problem. Rather than learning a view-alignment matrix that enforces strict one-to-one correspondences across views, we adopt a progressive approach to identify well-aligned samples. Specifically, we sample subsets of data by generating random view combinations from unaligned samples and propose an anomaly combination detection module to evaluate the alignment consistency of these combinations. In addition, our progressive training framework alternates between updating model parameters and selecting high-confidence view combinations for subsequent optimization. By reformulating view alignment as an anomaly detection task, our approach provides a more robust and effective solution to partial view alignment. Experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in the PVC problem.

AAAI Conference 2026 Conference Paper

ManipDreamer3D: Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory

• Ying Li
• Xiaobao Wei
• Xiaowei Chi
• Yuming Li
• Zhongyu Zhao
• Hao Wang
• Ningning Ma
• Ming Lu

Data scarcity continues to be a critical bottleneck in the field of robotic manipulation, limiting the ability to train robust and generalizable models. While diffusion models provide a promising approach to synthesizing realistic robotic manipulation videos, their effectiveness hinges on the availability of precise and reasonable control instructions. Current methods primarily rely on 2D trajectories as instruction prompts, which inherently face issues with 3D spatial ambiguity. In this work, we present a novel framework named ManipDreamer3Dfor generating plausible 3D-aware robotic manipulation videos from the input image and the text instruction. Our method combines 3D trajectory planning with a reconstructed 3D occupancy map created from a third-person perspective, along with a novel trajectory-to-video diffusion model. Specifically, ManipDreamer3D first reconstructs the 3D occupancy representation from the input image and then computes an optimized 3D end-effector trajectory, minimizing path length, avoiding collisions and retiming. Next, we employ a latent editing technique to create video sequences from the initial image latent, text instruction and the optimized 3D trajectory. This process conditions our specially trained trajectory-to-video diffusion model to produce robotic pick-and-place videos. Our method significantly reduces human intervention requirements by autonomously planing plausible 3D trajectories. Experimental results demonstrate its superior visual quality and precision.

EAAI Journal 2026 Journal Article

Multi-modal semantic interaction fusion with dual-consistency contrastive learning for rotating machinery fault diagnosis

• Ying Li
• Xiaoping Liu
• Xutong Zhang
• Pengfei Liang
• Xuetao Xu
• Xiaoming Yuan
• Lijie Zhang

Intelligent fault diagnosis of rotating machinery relies on the ability to extract discriminative and robust representations from multi-modal sensor data. However, in realistic industrial environments, multi-modal signals are often weakly labeled, and many existing data-driven methods suffer from insufficient semantic interaction across modalities and scales, leading to unstable diagnostic decisions. To address these issues, this paper develops a novel artificial intelligence framework for fault diagnosis based on multiscale semantic interaction and dual-consistency contrastive representation learning. Heterogeneous sensor signals are first transformed into unified multi-channel time-frequency representations through continuous wavelet analysis and tensor fusion. A hierarchical representation learning architecture is then constructed to progressively capture global dependencies, intermediate semantic patterns, and fine-grained local fault features. A feedback-driven interaction mechanism is further introduced to propagate discriminative local information to higher-level representations, thereby enhancing global-local semantic consistency. To overcome the scarcity of labeled data and improve generalization, a dual-consistency contrastive learning strategy is designed, which enforces both intra-channel stability and inter-channel semantic alignment across different sensor modalities. This consistency-driven formulation constrains the representation space such that fault-related features remain separable, and robust under limited supervision and heterogeneous sensing conditions. Comprehensive evaluations using multiple performance metrics on two rotating machinery benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches. Further ablation, sensitivity, and efficiency analyses confirm a favorable balance between diagnostic performance and model complexity. These results indicate that the proposed artificial intelligence-based framework provides an effective solution for intelligent condition monitoring and predictive maintenance in complex industrial systems.

AAAI Conference 2026 Conference Paper

Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset

• Songcheng Du
• Yang Zou
• Jiaxin Li
• Mingxuan Liu
• Ying Li
• Changjing Shang
• Qiang Shen

Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative errors and suboptimal performance due to the lack of joint degradation modeling. To address these challenges, we propose a Unified Pansharpening Model with Thin Cloud Removal (Pan-TCR), an end-to-end framework that integrates physical priors. Motivated by theoretical analysis in the frequency domain, we design a frequency-decoupled restoration (FDR) block that disentangles the restoration of multispectral image (MSI) features into amplitude and phase components, each guided by complementary degradation-robust prompts: the near-infrared (NIR) band amplitude for cloud-resilient restoration, and the panchromatic (PAN) phase for high-resolution structural enhancement. To ensure coherence between the two components, we further introduce an interactive inter-frequency consistency (IFC) module, enabling cross-modal refinement that enforces consistency and robustness across frequency cues. Furthermore, we introduce the first real-world thin-cloud contaminated pansharpening dataset (PanTCR-GF2), comprising paired clean and cloudy PAN-MSI images, to enable robust benchmarking under realistic conditions. Extensive experiments on real-world and synthetic datasets demonstrate the superiority and robustness of Pan-TCR, establishing a new benchmark for pansharpening under realistic atmospheric degradations.

AAAI Conference 2026 Conference Paper

UVLM: Benchmarking Video Language Model for Underwater World Understanding

• Xizhe Xue
• Yang Zhou
• Dawei Yan
• Lijie Tao
• Junjie Li
• Ying Li
• Haokui Zhang
• Rong Xiao

Recently, video-language models (VidLMs) have gained widespread attention and adoption. However, existing works primarily focus on terrestrial scenarios, overlooking the highly demanding application needs of underwater observation. To overcome this gap, we introduce UVLM, an under water observation benchmark which is build through a collaborative approach combining human expertise and AI models. To ensure data quality, we have conducted in-depth considerations from multiple perspectives. First, to address the unique challenges of underwater environments, we selected videos that represent typical underwater challenges including light variations, water turbidity, and diverse viewing angles to construct the dataset. Second, to ensure data diversity, the dataset covers a wide range of frame rates, resolutions, 419 classes of marine animals, and various static plants and terrains. Next, for task diversity, we adopted a structured design where observation targets are categorized into two major classes: biological and environmental. Each category includes content observation and change/action observation, totaling 20 subtask types. Finally, we designed several challenging evaluation metrics to enable quantitative comparison and analysis of different methods. Experiments on two representative VidLMs demonstrate that fine-tuning VidLMs on UVLM significantly improves underwater world understanding while also showing potential for slight improvements on existing in-air VidLM benchmarks.

AAAI Conference 2025 Conference Paper

Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs

• Wei Du
• Hongmin Sun
• Hang Gao
• Gaoyang Li
• Ying Li

In recent years, methods based on heterogeneous graph neural networks (HGNNs) have been widely used for embedding heterogeneous graphs (HGs) due to their ability to effectively encode the rich information from HGs into low-dimensional node embeddings. Existing HGNNs focus on neighbor aggregation and semantic fusion while neglecting the HG structure and learning paradigms. However, the original HG data might lack node features, which existing models may not effectively account for. Additionally, exclusively relying on a single supervised learning approach may only partially leverage the invariant information in graph data. To address these challenges, we introduce the Contrastive Auxiliary Learning Model for Heterogeneous Graphs (CALHG). This model combines edge perturbation and graph diffusion to enhance graph data, allowing it to capture the inherent structural information within heterogeneous graphs fully. Additionally, we employ a category-guided multi-view contrastive learning approach, which does not rely on positive and negative samples for model training, enabling us to capture the intrinsic invariances in heterogeneous graph data. Extensive experiments and analyses on five benchmark datasets without node features and three benchmark datasets with node features demonstrate the effectiveness and efficiency of our novel method compared with several state-of-the-art methods.

YNIMG Journal 2025 Journal Article

Corrigendum to “Development of diffusion analysis along the perivascular space (DTI-ALPS) index during childhood and adolescence: Evidence from two longitudinal cohorts” [NeuroImage 317 (2025) 121331]

• Shiwei Lin
• Qunjun Liang
• Ying Li
• Caixue Cheng
• Tingting Gong
• Yingwei Qiu

YNIMG Journal 2025 Journal Article

Development of diffusion analysis along the perivascular space (DTI-ALPS) index during childhood and adolescence: Evidence from two longitudinal cohorts

• Shiwei Lin
• Qunjun Liang
• Ying Li
• Caixue Cheng
• Tingting Gong
• Yingwei Qiu

Brain glymphatic activity, as indicated by diffusion analysis along the perivascular space (DTI-ALPS) index, has been discovered to participate in the pathogenesis of atypical development. However, little is known about the physiological developmental trajectory of the DTI-ALPS index during childhood and adolescence. Here, we evaluated DTI-ALPS index developmental characteristics and investigated its potential association with sex in 627 participants from two longitudinal cohorts. The global DTI-ALPS (gDTI-ALPS) index showed a positive cross-sectional relationship with age in both cohorts even after adjusting for sex. The annual net increase in the gDTI-ALPS index in both cohorts ranged from 0.003 to 0.005. Increases in the gDTI-ALPS index with age were also observed in the longitudinal analysis. Moreover, regional DTI-ALPS index analysis revealed the increment of DTI-ALPS mainly involved in the anterior brain regions, with the peak value of the regional DTI-ALPS index moving forward with age. Sex had no significant moderating effect on DTI-ALPS index. Our results suggest DTI-ALPS index development is a dynamic process during childhood and adolescence, particularly within the anterior brain regions, which may represent glymphatic system maturation during this critical period. The global and regional DTI-ALPS index could serve as a sensitive biomarker for monitoring this process.

AAAI Conference 2025 Conference Paper

Dynamic Syntactic Feature Filtering and Injecting Networks for Cross-lingual Dependency Parsing

• Jianjian Liu
• Zhengtao Yu
• Ying Li
• Yuxin Huang
• Shengxiang Gao

Pre-trained language models enhanced parsers have achieved outstanding performance in rich-resource languages. Cross-lingual dependency parsing aims to learn useful knowledge from high-resource languages to alleviate data scarcity in low-resource languages. However, effectively reducing the syntactic structure distributional bias and excavating the commonalities among languages is the key challenge for cross-lingual dependency parsing. To address this issue, we propose novel dynamic syntactic feature filtering and injecting networks based on the typical shared-private model that employs one shared and two private encoders to separate source and target language features. Concretely, a Language-Specific Filtering Network (LSFN) on private encoders emphasizes helpful information and ignores the irrelevant or harmful parts of it from the source language. Meanwhile, a Language-Invariant Injecting Network (LIIN) on the shared encoder integrates the advantages of BiLSTM and improved Transformer encoders to transcend language boundaries, thus amplifying syntactic commonalities across languages. Experiments on seven benchmark datasets show that our model achieves an average absolute gain of 1.84 UAS and 3.43 LAS compared with the shared-private model. Comparative experiments validate that both LSFN and LIIN components are complementary in transferring beneficial knowledge from source to target languages. Detailed analyses highlight that our model can effectively capture linguistic commonalities and mitigate the effect of distributional bias, showcasing its robustness and efficacy.

NeurIPS Conference 2025 Conference Paper

FreqExit: Enabling Early-Exit Inference for Visual Autoregressive Models via Frequency-Aware Guidance

• Ying Li
• Chengfei Lyu
• Huan Wang

Visual AutoRegressive (VAR) modeling employs a next-scale decoding paradigm that progresses from coarse structures to fine details. While enhancing fidelity and scalability, this approach challenges two fundamental assumptions of conventional dynamic inference: semantic stability (intermediate outputs approximating final results) and monotonic locality (smooth representation evolution across layers), which renders existing dynamic inference methods ineffective for VAR models. To address this challenge, we propose FreqExit, a unified training framework that enables dynamic inference in VAR without altering its architecture or compromising output quality. FreqExit is based on a key insight: high-frequency details are crucial for perceptual quality and tend to emerge only in later decoding stages. Leveraging this insight, we design targeted mechanisms that guide the model to learn more effectively through frequency-aware supervision. The proposed framework consists of three components: (1) a curriculum-based supervision strategy with progressive layer dropout and early exit loss; (2) a wavelet-domain high-frequency consistency loss that aligns spectral content across different generation steps; and (3) a lightweight self-supervised frequency-gated module that guides adaptive learning of both structural and detailed spectral components. On ImageNet 256×256, FreqExit achieves up to 2× speedup with only minor degradation, and delivers 1. 3× acceleration without perceptible quality loss. This enables runtime-adaptive acceleration within a unified model, offering a favorable trade-off between efficiency and fidelity for for practical and flexible deployment.

IJCAI Conference 2025 Conference Paper

FreqMoE: Dynamic Frequency Enhancement for Neural PDE Solvers

• Tianyu Chen
• Haoyi Zhou
• Ying Li
• Hao Wang
• Zhenzhe Zhang
• Tianchen Zhu
• Shanghang Zhang
• Jianxin Li

Fourier Neural Operators (FNO) have emerged as promising solutions for efficiently solving partial differential equations (PDEs) by learning infinite-dimensional function mappings through frequency domain transformations. However, the sparsity of high-frequency signals limits computational efficiency for high-dimensional inputs, and fixed-pattern truncation often causes high-frequency signal loss, reducing performance in scenarios such as high-resolution inputs or long-term predictions. To address these challenges, we propose FreqMoE, an efficient and progressive training framework that exploits the dependency of high-frequency signals on low-frequency components. The model first learns low-frequency weights and then applies a sparse upward-cycling strategy to construct a mixture of experts (MoE) in the frequency domain, effectively extending the learned weights to high-frequency regions. Experiments on both regular and irregular grid PDEs demonstrate that FreqMoE achieves up to 16. 6 percent accuracy improvement while using merely 2. 1 percent parameters (47. 32x reduction) compared to dense FNO. Furthermore, the approach demonstrates remarkable stability in long-term predictions and generalizes seamlessly to various FNO variants and grid structures, establishing a new Low frequency Pretraining, High frequency Fine-tuning'' paradigm for solving PDEs.

EAAI Journal 2025 Journal Article

Interpretable and robust fault diagnosis of rotating machinery in noisy environments via improved high-order spatial interactions network

• Bin Wang
• Pengfei Liang
• Ying Li
• Junhui Hu
• Lijie Zhang

According to the problems of the existing fault diagnosis (FD) model being affected by noise and lacking interpretability, this paper proposed an innovative FD model for rotating machinery, named noise critical layer adaptation (NCLA). By designing the module of noise robustness criticality (NRC), the model effectively focuses on layers most affected by noise, significantly improving classification accuracy and interpretability in noisy environments. Furthermore, this study designed an improved feature extraction framework based on the high-order spatial interactions with recursive gated wavelet convolution (WTConv) network, which enables the model to decompose and process signal components at different frequencies, enhancing its robustness and capability to capture fine-grained features. Unlike traditional models that rely on datasets with identical feature distributions, the proposed model was pre-trained on noise-free data and tested on noisy datasets, which aligns more closely with actual engineering applications. Experimental results of the two cases demonstrated that the model exhibits superior generalization and robustness across various noise conditions, outperforming conventional approaches. Additionally, by visualizing the impact of noise on critical layers, the proposed model addresses the limitations of the black box in deep learning methods.

JBHI Journal 2025 Journal Article

Localized Intra- and Inter-Tumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer

• Yinhao Liang
• Wenjie Tang
• Qingcong Kong
• Ting Wang
• Jianjun Zhang
• Wing W. Y. Ng
• Siyi Chen
• Ying Li

This study proposes a novel method for extracting breast cancer tumor heterogeneity descriptors to non-invasively predict whether pathological complete response (pCR) can be achieved after neoadjuvant chemotherapy (NAC). These localized descriptors extract corresponding heterogeneity features for different radiomic features and are able to capture tumor characteristics at various localization levels. These descriptors also capture tumor heterogeneity both at the individual tumor level and across the whole dataset, providing decision-making models with features that are both more effective and interpretable. We validated the effectiveness of the proposed features with the Kolmogorov-Arnold network (KAN) across multiple centers, yielding an AUC of 0. 92 when combined with pathological features and demonstrating good performance in external datasets (AUCs of 0. 84 and 0. 81). Additionally, we transform the best model into a symbolic formula to intuitively explain the machine learning model's prediction process, showing how factors such as age, HER2, Ki-67 and heterogeneity influence the prediction. The symbolized model is consistent with the experience of clinical experts, which enhances users' confidence in deep models. The experimental results show that our proposed features and method outperform classical heterogeneity features and end-to-end neural networks with a small additional computational cost.

ICLR Conference 2025 Conference Paper

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

• Can Jin
• Ying Li
• Mingyu Zhao
• Shiyu Zhao 0001
• Zhenting Wang
• Xiaoxiao He
• Ligong Han
• Tong Che

Visual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning. However, existing visual prompting techniques often pad the prompt parameters around the image, limiting the interaction between the visual prompts and the original image to a small set of patches while neglecting the inductive bias present in shared information across different patches. In this study, we conduct a thorough preliminary investigation to identify and address these limitations. We propose a novel visual prompt design, introducing **Lo**w-**R**ank matrix multiplication for **V**isual **P**rompting (LoR-VP), which enables shared and patch-specific information across rows and columns of image pixels. Extensive experiments across seven network architectures and four datasets demonstrate significant improvements in both performance and efficiency compared to state-of-the-art visual prompting methods, achieving up to $6\times$ faster training times, utilizing $18\times$ fewer visual prompt parameters, and delivering a 3.1% improvement in performance.

EAAI Journal 2025 Journal Article

Multi-objective optimization of additive manufacturing process parameters of nitinol alloys for elastocaloric cooling

• Shuyao Wang
• Yongjun Shi
• Wenjia Cheng
• Kaijun Fan
• Qin Wang
• Ying Li
• Quan Li
• Huayang Sun

Nitinol shape memory alloys are recognized as green and environmentally friendly solid-state refrigerants, and their preparation through additive manufacturing technology demonstrates significant application potential. However, minimizing energy consumption while enhancing material utilization and manufacturing efficiency remains a primary goal and challenge for researchers. This study proposes the application of intelligent optimization algorithms to optimize process parameters, thereby minimizing energy consumption, improving material utilization, and reducing dilution rates. The objective is to achieve full utilization of energy and materials, thus enhancing additive manufacturing efficiency. Compared to traditional experimental methods, this approach eliminates limitations related to test conditions and parameters, allowing for more effective optimization of additive manufacturing process parameters. Designing and conducting experiments on key process parameters—such as laser powers, scanning speeds, powder feeding rates, and shielding gas flow rates—over a broader range can provide accurate data for intelligent optimization algorithms. The results indicate that the established genetic algorithm-optimized back propagation neural network prediction model exhibits high prediction accuracy for energy consumption, powder utilization, and dilution rates. The optimal process parameters were determined using a Pareto front for a multi-objective optimization problem. This multi-objective optimization resulted in reduction of energy consumption and dilution rates by 34. 1 % and 27. 2 %, respectively, while increasing the metal powder utilization rate by 139. 8 % compared to the average of the experimental results. This study contributes to the reduction of energy and material consumption in the additive manufacturing process of Nitinol shape memory alloys and promotes the development of green cooling technology utilizing these alloys as solid-state refrigerants.

NeurIPS Conference 2025 Conference Paper

Multi-View Oriented GPLVM: Expressiveness and Efficiency

• Zi Yang
• Ying Li
• Zhidi Lin
• Michael Minyi Zhang
• Pablo Olmos

The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and the kernel function. By modeling the spectral density with a bivariate Gaussian mixture, we then derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs. To address the inherent computational inefficiency of the NG-SM kernel, we propose a random Fourier feature approximation. Combined with a tailored reparameterization trick, this approximation enables scalable variational inference for both the model and the unified latent representations. Numerical evaluations across a diverse range of multi-view datasets demonstrate that our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations.

EAAI Journal 2025 Journal Article

Process planning of parameter intelligent adjustment for batch machining based on historical data segmented modeling

• Juan Lu
• Shiying Tu
• Ying Li
• Liang Zhang
• Xiaoping Liao

Improving machining benefit while maintaining stable quality and performance is a prerequisite for improving intelligent manufacturing and enterprise competitiveness. This paper attempts to explore the method of segmented optimization of process parameters to improve benefit (such as machining time and tool life) while maintaining quality and performance within a given range during the tool life cycle in batch machining. An intelligent parameter adjustment framework is proposed, which utilizes the historical machining data to construct the process parameter adjustment time series (i. e. , process planning) in the tool life cycle, and by online adding data from new tool machining processes, the time series can be modified. The key technology of the framework is to determine the adjustment point and the corresponding process parameters. A segmented prediction model based on historical data is presented to predict the adjustment point during the new tool machining process of, and a double depth Q-network (DDQN) is utilized to obtain the optimized machining parameters at the adjustment point. Based on a milling experiment of TC18, the effectiveness of the proposed method is validated by the prediction and optimization of the first adjustment point. The adjustment point is predicted to be the 58th machining of the new tool, after adjusting the process parameters obtained through DDQN at the adjustment point, the quality and cutting force remain within the set threshold, cutting time decreases by 0. 2% and tool life increases by 14. 42%, which shows that the proposed methods achieve quality assurance and benefit enhancement during the machining process.

AAAI Conference 2025 Conference Paper

Safe Online Convex Optimization with Heavy-Tailed Observation Noises

• Yunhao Yang
• Bo Xue
• Yunzhi Hao
• Ying Li
• Yuanyu Wan

We investigate safe online convex optimization (SOCO), where each decision must satisfy a set of unknown linear constraints. Assuming that the unknown constraints can be observed with a sub-Gaussian noise for each chosen decision, previous studies have established a high-probability regret bound of O(T^{2/3}). However, this assumption may not hold in many practical scenarios. To address this limitation, in this paper, we relax the assumption to allow any noise that admits finite (1+ε)-th moments for some ε∈(0,1], and propose two algorithms that enjoy an O(T^{c_ε}) regret bound with high probability, where T is the time horizon and c_ε=(1+ε)/(1+2ε). The key idea of our two algorithms is to respectively utilize the median-of-means and truncation techniques to achieve accurate estimation under heavy-tailed noises. To the best of our knowledge, these are the first algorithms designed to handle SOCO with heavy-tailed observation noises.

IS Journal 2025 Journal Article

SNNL: A Programming Language for SNN Development

• Qinghui Xing
• Zirun Li
• Ying Li
• Schahram Dustdar
• Xin Du
• Gang Pan
• Shuiguang Deng

Spiking Neural Networks (SNNs) are gaining attention for biological plausibility and energy efficiency. Advances in neuromorphic systems—integrating hardware and software tools—accelerate SNN implementation. Yet, deploying SNNs on such platforms remains challenging due to model complexity and system heterogeneity, requiring flexible frameworks. Existing tools (e. g. , PyNN, Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This paper proposes SNNL, a flexible domain-specific language for SNN development and deployment on neuromorphic hardware. SNNL decouples neuronal dynamics modeling from network topology specification: equation-based representations handle diverse neuron/synapse models, while hierarchical constructs define complex connectivity patterns. We present a Darwin3-targeted compiler with efficient code generation. Evaluations confirm SNNL achieves precise neuronal dynamic descriptions and flexible network configurations. This work bridges algorithm-hardware gaps in neuromorphic computing by enhancing programmability. Experimental results have demonstrated the feasibility of SNNL in developing SNNs for neuromorphic systems.

YNIMG Journal 2025 Journal Article

Tracking neural activity patterns during rapid high-altitude transitions

• Ji-Yu Xie
• Yi Zhang
• Wei Shen
• Liying Wu
• Quanhao Yu
• Zhen Lyu
• Liangyuan Song
• Rui Yang

Rapid adaptation to dynamic changes in the environment is critical for human survival. Extensive studies have observed human behavior and brain activity in a stable environment, but there is still a lack of understanding of how our brain's functional activity drives behavioral changes when the natural environment changes. Here, we used a virtual environment platform named the hypobaric hypoxia chamber to investigate how human neural oscillations and related behaviors are affected by changes in barometric pressure and oxygen levels at different altitudes. We found that physiological compensations occurred in the hypobaric hypoxic environment followed by an increase in altitude, resulting in faster response times in working memory tasks. High-density EEG analysis revealed a significant decrease in the alpha band at high altitudes, while delta band activity gradually increased with altitude. Moreover, a predictive model based on differences in brain regions across frequency bands identified the left supramarginal gyrus and left lingual gyrus as two hub regions strongly associated with hypoxia-related behavioral changes, and activations in the pallidum and amygdala could effectively decode the specific altitude at which humans are located. Our study underscores the potential of hypobaric hypoxia chambers as a powerful tool for dynamic high-altitude research and provides novel insights into how altitude-related changes shape human cognition and brain activity.

EAAI Journal 2025 Journal Article

Tube-LaneNet: Predict each three-dimensional lane as a completed structure via geometric priors

• Genghua Kou
• Shihao Wang
• Ying Li

Monocular three-dimensional lane detection is a critical task for intelligent vehicles. However, most current methods, which mainly extend the two-dimensional paradigms, regard lanes as separated points set and constrain loss through the orthogonal projection on the two-dimensional plane. In this work, a novel deep learning framework is proposed to detect each lane as a continuous completed three-dimensional spatial structure. Concretely, three-dimensional lane anchors are implemented to extract proposal features through geometric priors to guarantee the continuous linear spatial structure. To enhance the feature of proposals, a relation-aware mechanism is further introduced to extract the spatial relationship between three-dimensional lanes. In particular, a novel tube-like intersection over union (TubeIOU) is proposed, which extends each three-dimensional lane to the tube-like structure as a completed unified entity in the three-dimensional space. Experiments on different datasets demonstrate the state-of-art performance of the proposed framework, especially achieves the fastest efficiency with 69 frames per second. The code will be made publicly available.

EAAI Journal 2025 Journal Article

Two-layer knowledge graph transformer network-based question and answer explainable recommendation

• Ying Li
• Ming Li
• Jin Ding
• Yixue Bai

The question and answer (Q&A) recommendation in community question answering (CQA) helps users quickly and accurately find the desired Q&A. However, existing studies face the problems of sparse interaction data, cold starts, and a lack of explanations. This paper proposes a novel Q&A explainable recommendation approach based on a two-layer knowledge graph transformer network. It alleviates the sparse data and cold start problem by the novel two-layer knowledge graph. First, a two-layer knowledge graph in CQA is constructed. The interaction layer helps to enrich the associations between users and questions and answers (Q&As). The semantic layer provides semantic associations and reflects contextual domain knowledge. Second, a critical meta-path recognition module is constructed to learn the critical meta-paths between users and documents from the interaction layer. Then, a user and Q&A embedding method based on a two-layer knowledge graph is proposed to enhance the user and Q&A representations. Finally, a recommendation and explanation layer is established to obtain personalized Q&A recommendation results and corresponding explanations. Compared with the baselines, the proposed method shows superior performance. It achieves average improvements of 21. 28%, 28. 41% and 27. 18% in precision, recall and F1-measure, respectively, in the top- K Q&A recommendation separately. It improves the area under the curve and F1-measure of the click-through rate prediction recommendation by 11. 32% and 23. 06%, respectively.

NeurIPS Conference 2025 Conference Paper

URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model

• Zhe Li
• Xiang Bai
• Jieyu Zhang
• Zhuangzhe Wu
• Che Xu
• Ying Li
• Chengkai Hou
• Shanghang Zhang

Constructing accurate digital twins of articulated objects is essential for robotic simulation training and embodied AI world model building, yet historically requires painstaking manual modeling or multi-stage pipelines. In this work, we propose \textbf{URDF-Anything}, an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM). URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction. It implements a specialized [SEG] token mechanism that interacts directly with point cloud features, enabling fine-grained part-level segmentation while maintaining consistency with the kinematic parameter predictions. Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches regarding geometric segmentation (mIoU 17\% improvement), kinematic parameter prediction (average error reduction of 29\%), and physical executability (surpassing baselines by 50\%). Notably, our method exhibits excellent generalization ability, performing well even on objects outside the training set. This work provides an efficient solution for constructing digital twins for robotic simulation, significantly enhancing the sim-to-real transfer capability.

EAAI Journal 2024 Journal Article

A multidimensional probabilistic model based evolutionary algorithm for the energy-efficient distributed flexible job-shop scheduling problem

• Zi-Qi Zhang
• Ying Li
• Bin Qian
• Rong Hu
• Jian-Bo Yang

With escalating environmental effects, the spotlight on low-carbon manufacturing has garnered significant attention. The rise of distributed production has emerged as a prominent trend in response to the imperatives of economic globalization. This article focuses on addressing the energy-efficient distributed flexible job-shop scheduling problem (EE_DFJSP), with the aim of minimizing both makespan and total energy consumption (TEC) simultaneously. The production process contains four pivotal phases: 1) job assignment in distributed factories; 2) machine selection within factories; 3) operation allocation on flexible machines; and 4) machine speed adjustment for processing. Given the problem's multi-phase and strong coupling characteristics, it is imperative to develop a promising evolutionary algorithm (EA) for EE_DFJSP. To tackle this challenge, we propose a multidimensional probabilistic model-based EA (MPMEA) paradigm. First, problem-specific encoding and decoding schemes are developed based on the solution features of EE_DFJSP. Second, a hybrid initialization strategy incorporating four heuristic rules is devised to yield an initial population with diversity. Third, an effective union probabilistic model (UPM) is formulated to learn promising patterns from superior solutions, and an efficient sampling strategy is designed to produce high-quality offspring individuals. To achieve a balance between global exploration and local exploitation, problem-specific multiple neighborhood operators are proposed to perform an in-depth local search. Furthermore, a two-stage energy-saving speed adjustment strategy is designed for the superior solutions obtained through local search. Finally, computational comparisons and simulation studies are conducted to validate the effectiveness and superiority of the MPMEA in effectively addressing EE_DFJSP.

EAAI Journal 2024 Journal Article

A new efficient algorithm for short path planning of the vertical take-off and landing air-ground integrated vehicle

• Jing Zhao
• Weida Wang
• Chao Yang
• Ying Li
• Liuquan Yang
• Jiankang Cheng

With excellent air-ground multi-mode movements, the vertical take-off and landing (VTOL) air-ground integrated vehicle can easily traverse complex terrains and maintain high energy efficiency. During movement, path planning plays an important role in achieving the autonomous operation of the vehicle, which faces the following difficulty. A short air-ground multi-mode path requires efficient planning, with proper judgment of the timing and position for mode switching. To address this difficulty, we propose a new path planning algorithm, named Dynamically Directed Graph Algorithm (DDGA). It can realize short path search in limited search nodes via dynamically extracting key search nodes in maps and forming a dynamically directed graph. To be specific, adjacent nodes of the first obstacle traversed by the connection line from the current search node to the destination are defined as key search nodes. As the current node changes, key search nodes are dynamically updated. The above key search nodes and the directed paths between them form a dynamically directed graph. Considering the air-ground movement capability, obstacle areas below maximum flight altitudes in maps are defined as pending flight areas. The directed paths traversing these areas are considered in the above graph. Besides the two-dimensional distance cost, flight altitude cost is added to the cost values of different directed paths. This cost contributes to judging the proper switching timing and position. Compared to other algorithms, DDGA can find short paths with fewer search nodes in multiple obstacle maps. It efficiently plans a short air-ground multi-mode path for the VTOL air-ground integrated vehicle.

EAAI Journal 2024 Journal Article

An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling

• Xuxue Sun
• Nan Kong
• Weiping Ding
• Ying Li
• Nazmus Sakib
• Hao Zeng
• Hongdao Meng
• Chris Masterson

Demand-based nursing home resource planning is of great importance to ensure adequate resources (e. g. , beds and staffs) available to provide care services with desired quality, yet challenging. The challenge mainly lies in modeling heterogeneous demand of nursing home residents, reflected by various individual characteristics, diverse dwelling duration with multiple competing discharge dispositions, and diverse daily service need. Existing studies often assumed a homogeneous population of patients and neglected the complexity of demand heterogeneity and uncertainty, leading to biased demand estimation and misguided decisions. The objective of this work is to improve nursing home resource planning decisions in response to the complex demand heterogeneity and uncertainty. To address the challenges, we propose a novel knowledge-guided and data-driven decision support framework. This is the first work of integrating domain knowledge with predictive and decision analytics to enhance modeling fidelity and decision performance for nursing home resource planning. Specifically, to effectively capture different aspects of heterogeneous demand, we develop a novel knowledge-guided demand modeling module with predictive models, including a length-of-stay model with competing risk for duration analysis, a tree-based system for learning daily service need variations, and a demand simulator for capturing uncertainty of fluctuating demand. Moreover, to determine optimal capacity and staffing decisions under demand heterogeneity and uncertainty, we develop a demand-based decision-making module with effective optimization models and solution algorithms, ensuring satisfactory quality of care at reduced costs. Furthermore, to demonstrate the improved prediction and decision performances of the proposed framework, we provide a proof-of-the-concept case study using real data from our industrial collaborator and investigate how demand heterogeneity and uncertainty will impact resource planning decisions. The proposed framework also demonstrates its appealing adaptability under changing resident census compositions.

AAAI Conference 2024 Conference Paper

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

• Qingping Zheng
• Yuanfan Guo
• Jiankang Deng
• Jianhua Han
• Ying Li
• Songcen Xu
• Hang Xu

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed HD images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2X compared to the traditional tiled algorithm. The source code is available at https://github.com/ProAirVerse/Any-Size-Diffusion.

IJCAI Conference 2024 Conference Paper

FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning

• Yi Xu
• Ying Li
• Haoyu Luo
• Xiaoliang Fan
• Xiao Liu

In real-world situations, federated learning often needs to process non-IID (non-independent and identically distributed) data with multiple skews, causing inadequate model performance. Existing federated learning methods mainly focus on addressing the problem with a single skew of non-IID, and hence the performance of global models can be degraded when faced with dual skewed non-IID data caused by heterogeneous label distributions and sample sizes among clients. To address the problem with dual skewed non-IID data, in this paper, we propose a federated learning algorithm based on local graph, named FBLG. Specifically, to address the label distribution skew, we firstly construct a local graph based on clients' local losses and Jensen-Shannon (JS) divergence, so that similar clients can be selected for aggregation to ensure a highly consistent global model. Afterwards, to address the sample size skew, we design the objective function to favor clients with more samples as models trained with more samples tend to carry more useful information. Experiments on four datasets with dual skewed non-IID data demonstrate FBLG outperforms nine baseline methods and achieves up to 9% improvement in accuracy. Simultaneously, both theoretical analysis and experiments show FBLG can converge quickly.

EAAI Journal 2024 Journal Article

Smooth fusion of multi-spectral images via total variation minimization for traffic scene semantic segmentation

• Ying Li
• Aiqing Fang
• Yangming Guo
• Wei Sun
• Xiaobao Yang
• Xiaodong Wang

Achieving precise semantic segmentation for traffic scenes relies on adopting multi-spectral image fusion techniques to attain high-quality images. Many existing fusion solutions often aim to enhance the similarity between the input and fusion results at the pixel intensity and texture details stage. However, this can result in smoothness issues that limit semantic segmentation performance. To address these issues, we present a smooth representation learning optimization mechanism (SFLM) that conducts image fusion on two dimensions: inter- and intra-image levels. The former overcomes over- or under-smoothing problems via the mutual information maximization between the fusion result and image samples (i. e. , negative and positive). The latter balances under and over-smoothing for fusion results by minimizing the total variation in pixel space and maximizing the total variation in gradient space based on contrast learning. In this way, the proposed method effectively overcomes the fusion quality issues, providing better feature representations for semantic segmentation in autonomous vehicles. Experimental results on four public datasets validate our method’s effectiveness, robustness, and overall superiority.

YNIMG Journal 2024 Journal Article

The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective

• Chen Jin
• Ying Li
• Yin Yin
• Tenda Ma
• Wei Hong
• Yan Liu
• Nan Li
• Xinyue Zhang

The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.

AAAI Conference 2023 Conference Paper

Semi-attention Partition for Occluded Person Re-identification

• Mengxi Jia
• Yifan Sun
• Yunpeng Zhai
• Xinhua Cheng
• Yi Yang
• Ying Li

This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person re-identification (re-ID). Currently, the mainstream methods employ either external semantic partition or attention-based partition, and the latter manner is usually better than the former one. Under this background, this paper explores a potential that the weak semantic partition can be a good teacher for the strong attention-based partition. In other words, the attention-based student can substantially surpass its noisy semantic-based teacher, contradicting the common sense that the student usually achieves inferior (or comparable) accuracy. A key to this effect is: the proposed SAP encourages the attention-based partition of the (transformer) student to be partially consistent with the semantic-based teacher partition through knowledge distillation, yielding the so-called semi-attention. Such partial consistency allows the student to have both consistency and reasonable conflict with the noisy teacher. More specifically, on the one hand, the attention is guided by the semantic partition from the teacher. On the other hand, the attention mechanism itself still has some degree of freedom to comply with the inherent similarity between different patches, thus gaining resistance against noisy supervision. Moreover, we integrate a battery of well-engineered designs into SAP to reinforce their cooperation (e.g., multiple forms of teacher-student consistency), as well as to promote reasonable conflict (e.g., mutual absorbing partition refinement and a supervision signal dropout strategy). Experimental results confirm that the transformer student achieves substantial improvement after this semi-attention learning scheme, and produces new state-of-the-art accuracy on several standard re-ID benchmarks.

YNIMG Journal 2022 Journal Article

Increased or decreased? Interpersonal neural synchronization in group creation

• Zheng Liang
• Songqing Li
• Siyuan Zhou
• Shi Chen
• Ying Li
• Yanran Chen
• Qingbai Zhao
• Furong Huang

Group creation is the process by which group members collaborate to produce novel and useful ideas or products, including ideas generation and evaluation. However, the interpersonal neural mechanism of group creation during natural communication remains unclear. In this study, two groups of same-sex dyads with similar individual creativity collaborated to complete the Product Improvement Task (creative condition) and the Item Purchase Plan Task (control condition), respectively. Functional near-infrared spectroscopy (fNIRS) was used to record both members' neural activity in the left prefrontal (lPFC) and right temporal-parietal junction (rTPJ) regions during the task. Considering that the role asymmetry of group members may have an impact on interpersonal neural patterns, we identified leaders and followers in the dyads based on participant performance. The results showed that leaders and followers in the creative condition had significantly lower interpersonal neural synchronization (INS) in the right superior temporal gyrus-left superior frontal gyrus, right supramarginal gyrus-left superior frontal gyrus, and right supramarginal gyrus-left middle frontal gyrus than in the control condition. Partial multivariate Granger causality analyses revealed the influence between dyads was bidirectional but was significantly stronger from the leaders to the followers than the other direction. In addition, in the creative task, the INS was significantly associated with novelty, appropriateness, and conflict of views. All these findings suggest that the ideas generation and ideas evaluation process in group creation have poor interpersonal neural activity coupling due to factors such as the difficulty of understanding novel ideas. However, performances may be improved when groups can better integrate views and reach collective understanding, intentions, and goals. Furthermore, we found that there are differences in the dynamics of INS in different brain regions. The INS related to the novelty of the group creation decreased in the early stages, while the INS related to the appropriateness decreased in the middle stages. Our findings reveal a unique interpersonal neural pattern of group creation processes in the context of natural communication.

TCS Journal 2022 Journal Article

The non-inclusive g-good-neighbor diagnosability of interconnection networks

• Jun Yuan
• Ying Li
• Aixia Liu
• Huijuan Qiao

Motivated by the definitions of g-good-neighbor diagnosability and non-inclusive diagnosability, we propose a new diagnosability—the non-inclusive g-good-neighbor diagnosability t N g ( G ) of a multiprocessor system G, which requires every pair of g-good-neighbor faulty sets is non-inclusive. The R g -conditional diagnosability t R g ( G ) of a system G is a generalization of conditional diagnosability, which requires at least g fault-free neighbors for each node. In this paper, we explore the relationships between the non-inclusive g-good-neighbor diagnosability and the R g -conditional diagnosability of G under the PMC and MM* models. We first show t N g ( G ) ≤ t R g ( G ) for g ≥ 1, and also give some conditions for equality. Next, we discuss the non-inclusive g-good-neighbor diagnosability of hypercubes, ( n, k ) -star graphs and ( n, k ) -bubble-sort graphs. We show that the non-inclusive g-good-neighbor diagnosability of n-dimensional hypercubes is less that its R g -conditional diagnosability for 2 ≤ g ≤ n − 2 2, and determine the non-inclusive g-good-neighbor diagnosability of ( n, k ) -star graphs and ( n, k ) -bubble-sort graphs. Finally, we plot and compare the non-inclusive g-good-neighbor diagnosability and the g-good-neighbor diagnosability of ( n, k ) -star graphs and ( n, k ) -bubble-sort graphs under the PMC and MM* models, respectively. It can be seen that their non-inclusive g-good-neighbor diagnosability is significantly larger than their g-good-neighbor diagnosability.

IS Journal 2022 Journal Article

Xsickness in Intelligent Mobile Spaces and Metaverses

• Ruichen Tan
• Ruiyang Gao
• Wenbo Li
• Kai Cao
• Ying Li
• Chen Lv
• Fei-Yue Wang
• Dongpu Cao

Motion sickness is known to be a common problem that influences the comfort and work efficiency of human beings during their daily lives. With the proliferation of increasingly intelligent systems, the detection and mitigation of motion sickness will face more opportunities along with bigger challenges. On the one hand, the technology for integrated sensors in the intelligent system will provide more accurate and efficient methods for motion sickness detection. However, on the other hand, since cyber-physical systems have been gaining increasing concerns in the past two decades, the cyber-physical-social systems introduce and augment the social characteristics of such systems. The interactions between physical space and cyber space increase the chance of sensory conflicts when people use intelligent systems, such as traveling in intelligent cockpits or using metaverse-related virtual reality devices. The multimodal interaction methods and larger screens will cause more sensory conflicts. The symptoms will be more severe compared to traditional motion sickness. In this article, the classifications are first introduced based on the causes of motion sickness. A new type of multifactorial motion sickness (Xsickness) is discussed, which is foreseeable to be common with intelligent development. Then, the current state-of-the-art detection methods for motion sickness and cybersickness are summarized and theoretical methods for Xsickness detection are discussed. Finally, the mitigation methods based on motion reduction and four means of human perception are discussed and the innovative mitigation methods based on the intelligent system are also introduced.

JBHI Journal 2021 Journal Article

GCSBA-Net: Gabor-Based and Cascade Squeeze Bi-Attention Network for Gland Segmentation

• Zhijie Wen
• Ru Feng
• Jingxin Liu
• Ying Li
• Shihui Ying

Colorectal cancer is the second and the third most common cancer in women and men, respectively. Pathological diagnosis is the “gold standard” for tumor diagnosis. Accurate segmentation of glands from tissue images is a crucial step in assisting pathologists in their diagnosis. The typical methods for gland segmentation form a dense image representation, ignoring its texture and multi-scale attention information. Therefore, we utilize a Gabor-based module to extract texture information at different scales and directions in histopathology images. This paper also designs a Cascade Squeeze Bi-Attention (CSBA) module. Specifically, we add Atrous Cascade Spatial Pyramid (ACSP), Squeeze Position Attention (SPA) module and Squeeze Channel Attention module (SCA) to model semantic correlation and maintain the multi-level aggregation on the spatial pyramid with different dilations. Besides, to solve the imbalance of data distribution and boundary blur, we propose a hybrid loss function to response the object boudary better. The experimental results show that the proposed method achieves state-of-the-art performance on the GlaS challenge dataset and CRAG colorectal adenocarcinoma dataset, respectively.

IJCAI Conference 2021 Conference Paper

Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network

• Sixing Wu
• Minghui Wang
• Dawei Zhang
• Yang Zhou
• Ying Li
• Zhonghai Wu

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.

IJCAI Conference 2020 Conference Paper

TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact

• Sixing Wu
• Ying Li
• Dawei Zhang
• Yang Zhou
• Zhonghai Wu

Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.

IJCAI Conference 2019 Conference Paper

Self-attentive Biaffine Dependency Parsing

• Ying Li
• Zhenghua Li
• Min Zhang
• Rui Wang
• Sheng Li
• Luo Si

The current state-of-the-art dependency parsing approaches employ BiLSTMs to encode input sentences. Motivated by the success of the transformer-based machine translation, this work for the first time applies the self-attention mechanism to dependency parsing as the replacement of the BiLSTM-based encoders, leading to competitive performance on both English and Chinese benchmark data. Based on the detailed error analysis, we then combine the power of both BiLSTM and self-attention via model ensembles, demonstrating their complementary capability of capturing contextual information. Finally, we explore the recently proposed contextualized word representations as extra input features, and further improve the parsing performance.

AAAI Conference 2018 Conference Paper

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach

• Bin Liu
• Ying Li
• Zhaonan Sun
• Soumya Ghosh
• Kenney Ng

Type 2 diabetes mellitus (T2DM) is a chronic disease that usually results in multiple complications. Early identification of individuals at risk for complications after being diagnosed with T2DM is of significant clinical value. In this paper, we present a new data-driven predictive approach to predict when a patient will develop complications after the initial T2DM diagnosis. We propose a novel survival analysis method to model the time-to-event of T2DM complications designed to simultaneously achieve two important metrics: 1) accurate prediction of event times, and 2) good ranking of the relative risks of two patients. Moreover, to better capture the correlations of time-to-events of the multiple complications, we further develop a multi-task version of the survival model. To assess the performance of these approaches, we perform extensive experiments on patient level data extracted from a large electronic health record claims database. The results show that our new proposed survival analysis approach consistently outperforms traditional survival models and demonstrate the effectiveness of the multi-task framework over modeling each complication independently.

YNICL Journal 2017 Journal Article

Acupuncture modulates the abnormal brainstem activity in migraine without aura patients

• Zhengjie Li
• Fang Zeng
• Tao Yin
• Lei Lan
• Nikos Makris
• Kristen Jorgenson
• Taipin Guo
• Feng Wu

Migraine is a common neurological disease with a high prevalence and unsatisfactory treatment options. The specific pathophysiological mechanisms of migraine remain unclear, which restricts the development of effective treatments for this prevalent disorder. The aims of this study were to 1) compare the spontaneous brain activity differences between Migraine without Aura (MwoA) patients and healthy controls (HCs), using amplitude of low-frequency fluctuations (ALFF) calculation method, and 2) explore how an effective treatment (verum acupuncture) could modulate the ALFF of MwoA patients. One hundred MwoA patients and forty-six matched HCs were recruited. Patients were randomized to four weeks' verum acupuncture, sham acupuncture, and waiting list groups. Patients had resting state BOLD-fMRI scan before and after treatment, while HCs only had resting state BOLD-fMRI scan at baseline. Headache intensity, headache frequency, self-rating anxiety and self-rating depression were used for clinical efficacy evaluation. Compared with HCs, MwoA patients showed increased ALFF in posterior insula and putamen/caudate, and reduced ALFF in rostral ventromedial medulla (RVM)/trigeminocervical complex (TCC). After longitudinal verum acupuncture treatment, the decreased ALFF of the RVM/TCC was normalized in migraine patients. Verum acupuncture and sham acupuncture have different modulation effects on ALFF of RVM/TCC in migraine patients. Our results suggest that impairment of the homeostasis of the trigeminovascular nociceptive pathway is involved in the neural pathophysiology of migraines. Effective treatments, such as verum acupuncture, could help to restore this imbalance.

EAAI Journal 2015 Journal Article

Efficient web service QoS prediction using local neighborhood matrix factorization

• Wei Lo
• Jianwei Yin
• Ying Li
• Zhaohui Wu

In the era of Big Data, companies worldwide are actively deploying web services in both intranet and internet environments. Quality-of-Service (QoS), the fundamental aspect of web service has thus attracted numerous attention in industry and academia. The study on sufficient QoS data keeps advancing the state in Service-Oriented Computing (SOC) area. To collect a large amount of resource in practice, QoS prediction applications are designed and built. Nevertheless, how to generate accurate results in high productivity is still a main challenge to existing frameworks. In this paper, we propose LoNMF, a Local Neighborhood Matrix Factorization application that incorporates domain knowledge in modern Artificial Intelligence (AI) technique to tackle this challenge. LoNMF first proposes a two-level selection mechanism that can identify a set of highly relevant local neighbors for target user. And then, it integrates the geographical information to build up an extended Matrix Factorization (MF) approach for personalized QoS prediction. Finally, it iteratively generates results by utilizing hints from previous round computations, a gradient boosting strategy that directly accelerates solving process. Experimental evidence on large-scale real-world QoS data shows that LoNMF is scalable, and consistently outperforming other state-of-the-art applications in prediction accuracy and efficiency.

TCS Journal 2013 Journal Article

Optimal fault-tolerant routing algorithm and fault-tolerant diameter in directed double-loop networks

• Yebin Chen
• Ying Li
• Tao Chen

This paper addresses the reliability of directed double-loop networks G ( N; r, s ), and studies the problems about optimal fault-tolerant routing, fault-tolerant diameter, etc. , in G ( N; r, s ). Firstly, we study the shapes of the L-shaped tiles which are the minimum distance diagram of directed double-loop networks; we divide them into four types. There are different parameters for each type. According to the solutions to the congruence equation, then we study the distributions of the optimal equivalent nodes for different destination nodes, and present an optimal fault-tolerant routing algorithm and a formula computing the fault-tolerant diameter. Finally, we prove that there is a lower bound for fault-tolerant diameter, and show that there may be lots of double optimal directed double-loop networks in some infinite clusters of G ( N; r, s ). According to the proposed fault-tolerant routing algorithm, the reliability and transmission performance will be optimal when some faults occur in G ( N; r, s ).

YNIMG Journal 2012 Journal Article

A quantitative analytic pipeline for evaluating neuronal activities by high‐throughput synaptic vesicle imaging

• Jing Fan
• Xiaofeng Xia
• Ying Li
• Jennifer G. Dy
• Stephen T.C. Wong

Synaptic vesicle dynamics play an important role in the study of neuronal and synaptic activities of neurodegradation diseases ranging from the epidemic Alzheimer's disease to the rare Rett syndrome. A high-throughput assay with a large population of neurons would be useful and efficient to characterize neuronal activity based on the dynamics of synaptic vesicles for the study of mechanisms or to discover drug candidates for neurodegenerative and neurodevelopmental disorders. However, the massive amounts of image data generated via high‐throughput screening require enormous manual processing time and effort, restricting the practical use of such an assay. This paper presents an automated analytic system to process and interpret the huge data set generated by such assays. Our system enables the automated detection, segmentation, quantification, and measurement of neuron activities based on the synaptic vesicle assay. To overcome challenges such as noisy background, inhomogeneity, and tiny object size, we first employ MSVST (Multi-Scale Variance Stabilizing Transform) to obtain a denoised and enhanced map of the original image data. Then, we propose an adaptive thresholding strategy to solve the inhomogeneity issue, based on the local information, and to accurately segment synaptic vesicles. We design algorithms to address the issue of tiny objects of interest overlapping. Several post processing criteria are defined to filter false positives. A total of 152 features are extracted for each detected vesicle. A score is defined for each synaptic vesicle image to quantify the neuron activity. We also compare the unsupervised strategy with the supervised method. Our experiments on hippocampal neuron assays showed that the proposed system can automatically detect vesicles and quantify their dynamics for evaluating neuron activities. The availability of such an automated system will open opportunities for investigation of synaptic neuropathology and identification of candidate therapeutics for neurodegeneration.

ICRA Conference 2002 Conference Paper

An Analytical Grasp Planning on Given Object with Multifingered Hand

• Ying Li
• Yong Yu 0003
• Showzow Tsujio

In this paper, an analytical approach is proposed for planning finger positions of grasping an object with a multifingered hand. First, a method is given to obtain which combination of the object edges is possible to be used for grasping. Then, a graspable finger position region (GFPR) on a combination of edges is defined where the object can be held successfully. It is shown that the region is bounded by several boundary hyperplanes. By combining these boundary hyperplanes, two propositions for analytically and exactly obtaining the GFPR are proposed. An algorithm is proposed to find a stable GFPR that contains the biggest inscribed hypersphere of GFPR and has the largest volume. Finally, a numerical example is performed to show the effectiveness of the proposed grasp planning approach.

IROS Conference 2001 Conference Paper

A novel analytical method for finger position regions on grasped object

• Yong Yu 0003
• Ying Li
• Showzow Tsujio

An analytical approach is proposed for obtaining finger position regions of an object with a multi-fingered hand. First, a method to obtain which combination of the object edges is possible to be used for grasping, is given. Then, a graspable finger position region on a combination of edges is defined where the object can be held successfully. It is shown that the region is bounded by plural boundary hyperplanes. By combining these boundary hyperplanes, two propositions for exactly obtaining the graspable finger position region by using an analytical method, are proposed. Finally, numerical examples are performed to show the effectiveness of the proposed approach.