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Jing Li

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94 papers
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94

EAAI Journal 2026 Journal Article

An anisotropic low-rank self-attention mechanism for infrared and visible image fusion

  • Xiaowen Liu
  • Hongtao Huo
  • Haozhe Cao
  • Xu Dong
  • Jing Li

Visible-infrared image fusion (VIF) is crucial for multi-modality information integration. Existing VIF methods predominantly rely on intricate network architectures and semantic-driven paradigms to enhance global semantic extraction capabilities, which still face significant challenges. Firstly, the patch-wise operations adopted by Transformer-based VIF methods disrupt pixel-level spatial structures, which restricts applicability in low-level vision tasks. Additionally, semantic-driven methods often suffer from feature heterogeneity between upstream and downstream tasks. To solve these, we propose the Anisotropic Low-Rank Self-Attention (ALRSA) mechanism to bridge the gap of Transformer in image fusion tasks. Meantime, we design ALRTFusion, an anisotropic low-rank Transformer-based fusion method with robust semantic representation capability, which is a pure Transformer-based encoder–decoder model with pixel-level token encoding capability and linear computational complexity. Specifically, the ALRSA mechanism incorporates a prior anisotropic low-rank characteristic, guiding the model to preserve critical visual and semantic structures while efficiently suppressing redundant information. Furthermore, an orthogonal attention embedding strategy is adopted to map and expand the low-rank content across channel and spatial dimensions at the feature level, thereby achieving low-rank approximation and capturing comprehensive contextual information. Extensive experiments on multiple benchmark datasets demonstrate that ALRTFusion significantly outperforms state-of-the-art methods in both pixel-level visual quality and semantic-level performance. The code will be available at https: //github. com/XiaoW-Liu/ALRTFusion.

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AAAI Conference 2026 Conference Paper

ESCA: An Emotional Support Conversation Agent for Enhancing Reasonable Strategy Planning and Effective Expression

  • Jing Li
  • Yanxin Luo
  • Donghong Han
  • Yimeng Zhan
  • Xiaoming Fu
  • Baiyou Qiao
  • Gang Wu

Emotional Support Conversation (ESC) aims to alleviate individuals’ negative emotions through multi-turn dialogues, where effective strategy planning and response generation are essential. However, existing methods often suffer from limitations in both planning reasonable support strategies and effectively expressing them in responses. To the end, we propose a novel LLM-based Emotional Support Conversation Agent (ESCA) with a plug-in strategy planner and a strategy-aligned prompt generator. The strategy planner cooperates with four aspects of the seeker’s state, including emotion intensity, trust degree, dialogue behavior, and stage of change, to enhance the rationality and effectiveness of the strategy prediction. To ensure that predicted strategies are better conveyed, the prompt generator integrates strategy-aligned instructions, knowledge, and context to generate the soft prompt for guiding the LLM to generate supportive responses. In addition to supervised fine-tuning, the prompt generator is further optimized by reinforcement learning. Experimental results demonstrate that ESCA significantly improves both response quality and the success rate of achieving the ESC task goal.

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EAAI Journal 2026 Journal Article

Event-triggered adaptive robust non-singular fast terminal sliding mode fault-tolerant control for intelligent vehicle stability systems under extreme conditions

  • Min Gao
  • Jiaqi Li
  • Jing Li
  • Jin Luo
  • Chenyang Wang

The vehicle stability control system serves as the fundamental guarantee for the active safety of intelligent vehicles. In light of existing challenges such as ensemble uncertainty interference, limited communication resources, and actuator faults. This study presents an event-triggered adaptive robust non-singular fast terminal sliding mode fault-tolerant control method. Adaptive laws are formulated to evaluate the switching gains, thereby circumventing complications associated with insufficient prior knowledge of lumped uncertainties. Subsequently, an event-triggered adaptive robust non-singular fast terminal sliding mode control strategy is introduced to conserve communication resources, mitigate chattering, and prevent singularities. Furthermore, fault factors are incorporated into the vehicle dynamics framework to enhance the torque optimization allocation strategy for fault-tolerant control in the presence of actuator faults. The application of the Lyapunov stability theorem confirms stability over a limited duration, as well as Zeno-free behavior within the vehicle stability control system. Ultimately, CarSim and Matlab/Simulink co-simulation are employed to verify the effectiveness of the proposed method, with numerical simulations conducted across various complex driving conditions. The simulation data indicate that the proposed method reduces the number of event-triggered numbers for the yaw rate controller and the side slip angle controller by 67. 1 %/75. 2 % and 36. 1 %/28. 2 % for Conditions A and B, respectively, when compared to the fixed-time-triggered sliding mode controller and the without-control method.

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AAAI Conference 2026 Conference Paper

FP=XINT: Representing Neural Networks via Low-Bit Series Basis Functions

  • Boyang Zhang
  • Daning Cheng
  • Yunquan Zhang
  • Jiake Tian
  • Jing Li
  • Fangming Liu

Deep neural networks are often over-parameterized, resulting in prohibitive storage and computational costs. A fundamental question is whether a complex network can be re-expressed in terms of a compact set of basis functions without sacrificing accuracy. Motivated by this perspective, we aim to approximate a dense model by decomposing it into a small number of lightweight components that capture the essential functional structure of the network. To this end, we propose a series expansion framework that rewrites a neural network as a linear combination of low-bit basis models. Within the post-training quantization setting, the full-precision model is expanded hierarchically at the tensor, layer, and model levels into a structured set of basis functions. We theoretically prove that this expansion converges exponentially to the original model. Furthermore, we design AbelianAdd and AbelianMul operations between isomorphic basis models, endowing the expansion with an Abelian group structure that naturally supports commutative and parallel computation. Experimental results across diverse architectures show that our series expansion method leverages a set of ultra-low-bit basis functions, not only preserving full-precision performance without the need for calibration data or fine-tuning, but also featuring a parallel-friendly design that enables efficient and scalable deployment.

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AAMAS Conference 2026 Conference Paper

GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion

  • Tongxuan Liu
  • Xingyu Wang
  • Weizhe Huang
  • Wenjiang Xu
  • Yuting Zeng
  • Lei Jiang
  • Hailong Yang
  • Jing Li

In recent years, Large Language Models (LLMs) have demonstrated remarkablecapabilitiesacrossdiverseNLPtasks, includingcomplex logical reasoning, mathematical problem-solving, and multi-step decision-making. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of- Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number of agents and debate rounds. However, the escalation in the number of agents and debate rounds can drastically raise the tokens cost of debates, thereby limiting the scalability of the multi-agent debate technique. To better harness the advantages of multi-agent debates in logical reasoning tasks, this paper proposes a method to significantly reduce token cost in multi-agent debates. This approach involves dividing all agents into multiple debate groups, with agents engaging in debates within their respective groups and sharing interim debate results between groups. Comparative experiments across multiple datasets have demonstrated that this method can reduce the total tokens by up to 46. 9% during debates and while potentially enhancing accuracy by as much as 21. 9%. Our method significantly enhances the performance and efficiency of interactions in the multi-agent debate. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/DSDX8860

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EAAI Journal 2026 Journal Article

Hesitant fuzzy three-way decision-making for large-scale data based on a new distance measure and behavioral theory

  • Jing Li
  • Haidong Zhang
  • Zhuoma Dawa
  • Yanping He

In highly uncertain real-world environments, making robust decisions amid incomplete information and the cognitive biases of decision-makers remain critical challenges in project management and other complex system decision-making-related domains. Although the three-way decision-making (3WD) method based on hesitant fuzzy (HF) environments provides an effective approach for managing uncertainty, the current research still has shortcomings in several key areas. On the one hand, the distance formulas that are commonly used for computing hesitant fuzzy elements (HFEs) generally suffer from insufficient sensitivity to the information captured by the score function and weak discriminative power. On the other hand, the process of determining loss functions is subjective and fails to consider the behavioral psychological factors of decision-makers, making it difficult to reflect the cognitive characteristics of humans during actual decision-making processes. These issues collectively limit the adaptability and practicality of the existing decision-making methods in complex real-world environments. To address the aforementioned issues, this study is aimed at constructing an HF 3WD framework that possesses both cognitive rationality and computational robustness. To this end, the core contributions of this work are as follows. First, a novel HF distance measure is developed, significantly improving the ability to distinguish fuzzy information differences. Second, a novel o -dominance relation is introduced, and the conditional probability is calculated using a data-driven approach, eliminating the reliance on expert scoring and thereby improving the objectivity and accuracy of the conditional probability. Finally, an objective loss function is established, effectively capturing the decision-maker’s nonlinear value perceptions and comparative psychology in gain and loss scenarios. Furthermore, comparative experiments and parameter analyses are conducted in big data scenarios to validate the fact that the proposed method outperforms the existing methods in terms of classification accuracy and decision stability, demonstrating superior effectiveness and robustness. We believe that by simulating human judgments made under uncertainty, this method opens up new avenues for implementing artificial intelligence-based decision-making systems in high-risk scenarios.

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AAAI Conference 2026 Conference Paper

LaF-GRPO: In-Situ Navigation Instruction Generation for the Visually Impaired via GRPO with LLM-as-Follower Reward

  • Yi Zhao
  • Siqi Wang
  • Jing Li

Navigation instruction generation for visually impaired (VI) individuals (NIG-VI) is critical yet relatively underexplored. This study focuses on generating precise, in-situ, step-by-step navigation instructions that are practically usable for VI users. Specifically, we propose LaF-GRPO (LLM-as-Follower GRPO), where an LLM simulates VI user responses to navigation instructions, thereby providing feedback rewards to guide the post-training of a Vision-Language Model (VLM). This enhances instruction accuracy and usability while reducing costly real-world data collection needs. To address the scarcity of dedicated benchmarks in this field, we introduce NIG4VI, a 27k-sample open-source dataset to facilitate training and evaluation. It comprises diverse navigation scenarios with accurate spatial coordinates, supporting detailed and open-ended in-situ instruction generation. Experiments on NIG4VI demonstrate the effectiveness of LaF-GRPO through quantitative metrics (e.g., Zero-(LaF-GRPO) boosts BLEU 14%; SFT+(LaF-GRPO) METEOR 0.542 vs. GPT-4o 0.323), and qualitative analysis further confirms that our method yields more intuitive and safer instructions.

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AAAI Conference 2026 Conference Paper

MARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management

  • Jiayi Chen
  • Jing Li
  • Guiling Wang

Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many models fail to adapt to dynamically changing market conditions. We propose Meta-controlled Agents for a Risk-aware System (MARS), a novel framework addressing this through a multi-agent, risk-aware approach. MARS replaces monolithic models with a Heterogeneous Agent Ensemble, where each agent’s unique risk profile is enforced by a Safety-Critic network to span behaviors from capital preservation to aggressive growth. A high-level Meta-Adaptive Controller (MAC) dynamically orchestrates this ensemble, shifting reliance between conservative and aggressive agents to minimize drawdown during downturns while seizing opportunities in bull markets. This two-tiered structure leverages behavioral diversity rather than explicit feature engineering to ensure a disciplined portfolio robust across market regimes. Experiments on major international indexes confirm that our framework significantly reduces maximum drawdown and volatility while maintaining competitive returns.

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AAAI Conference 2026 Conference Paper

MdaIF: Robust One-Stop Multi-Degradation-Aware Image Fusion with Language-Driven Semantics

  • Jing Li
  • Yifan Wang
  • Jiafeng Yan
  • Renlong Zhang
  • Bin Yang

Infrared and visible image fusion aims to integrate complementary multi-modal information into a single fused result. However, existing methods 1) fail to account for the degradation visible images under adverse weather conditions, thereby compromising fusion performance; and 2) rely on fixed network architectures, limiting their adaptability to diverse degradation scenarios. To address these issues, we propose a one-stop degradation-aware image fusion framework for multi-degradation scenarios driven by a large language model (MdaIF). Given the distinct scattering characteristics of different degradation scenarios (e.g., haze, rain, and snow) in atmospheric transmission, a mixture-of-experts (MoE) system is introduced to tackle image fusion across multiple degradation scenarios. To adaptively extract diverse weather-aware degradation knowledge and scene feature representations, collectively referred to as the semantic prior, we employ a pre-trained vision-language model (VLM) in our framework. Guided by the semantic prior, we propose degradation-aware channel attention module (DCAM), which employ degradation prototype decomposition to facilitate multi-modal feature interaction in channel domain. In addition, to achieve effective expert routing, the semantic prior and channel-domain modulated features are utilized to guide the MoE, enabling robust image fusion in complex degradation scenarios. Extensive experiments validate the effectiveness of our MdaIF, demonstrating superior performance over SOTA methods.

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EAAI Journal 2026 Journal Article

Multi-label learning research on joint ensemble strategies for predicting adverse prognosis in patients with coronary heart disease

  • Hong Yang
  • Jing Li
  • Yanyan Dai
  • Leyi Zhang
  • Jing Tian
  • Yanbo Zhang

Major Adverse Cardiac Events (MACE), as composite endpoints in coronary heart disease (CHD) prognosis research, present a conflict between multidimensional prediction needs and the limitations of single-label modeling. While the Ensemble Multi-Label classifier (EMLC) enables simultaneous multi-outcome predictions, medical data complexity introduces dual challenges: multi-label feature selection impacts model performance, and existing methods overlook label correlations. This study proposes two innovations: First, the Dynamic Weighted Multi-Label Ensemble Feature selection (DWML-EFS) algorithm integrates information-theoretic approaches to reduce computational costs while enhancing feature selection efficiency. Second, the Multi-Label Linearly Weighted Stacked Ensemble (MLLWSE) model employs accelerated proximal gradient optimization and regularization to balance structural simplicity with predictive power, while capturing interdependencies among base classifiers. Experiments demonstrated superior performance over state-of-the-art methods on both public datasets and real-world CHD data. By incorporating SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) interpretability techniques, the framework achieves synergistic optimization of precision and explainability in CHD multi-label prognosis prediction. These advancements address the critical complexities of multi-label learning in medical data, delivering an efficient and interpretable solution for MACE prediction. Importantly, this framework can identify patient groups at risk of adverse outcomes and provide prediction tools with practical value for clinical decision-making.

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EAAI Journal 2026 Journal Article

Quantitative assessment of interfacial debonding in large-span concrete-filled steel tube arch bridges via interpretable deep hybrid learning and ultrasonic inspection

  • Minghui Yao
  • Shuhong Guan
  • Jing Li
  • Zheng Chen
  • Junhui Liu
  • Cai Tan
  • Yunchao Tang

Interfacial debonding between the steel tube and concrete core poses a critical threat to the structural integrity and serviceability of concrete-filled steel tube (CFST) arch bridges. However, the occult nature of such internal defects, obscured by the external steel wall, complicates traditional inspection and quantitative assessment. This study proposes an intelligent non-destructive testing (NDT) framework integrating ultrasonic wave propagation with a deep hybrid learning architecture. Specifically, an improve deep hybrid learning model was developed to achieve high-precision quantification of debonding severity. The model leverages convolutional neural networks (CNN) for spatial feature extraction from ultrasonic signals, bidirectional long short-term memory (BiLSTM) for capturing bidirectional temporal dependencies, and an attention mechanism to adaptively weight salient features. Experimental results demonstrate superior predictive accuracy on the test set, achieving an R-squared of 0. 93, with a Root Mean Square Error of 4. 69 and Mean Absolute Error of 3. 72. To address the inherent opacity of the model, a SHapley Additive exPlanation (SHAP) framework was implemented, quantifying the contributory impact of key input parameters on the predictions. Field validation via core sampling on an in-service CFST arch bridge further confirmed the model's robust generalization capability (R-squared of 0. 89). The findings provide a scientific basis for the automated, quantitative health monitoring of large-span CFST structures, ensuring their long-term structural reliability and construction quality.

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TMLR Journal 2026 Journal Article

S$^2$Transformer: Scalable Structured Transformers for Global Station Weather Forecasting

  • Hongyi Chen
  • Xiucheng Li
  • Xinyang Chen
  • Yun Cheng
  • Jing Li
  • Kehai Chen
  • Liqiang Nie

Global Station Weather Forecasting (GSWF) is a key meteorological research area, critical to energy, aviation, and agriculture. Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting. This contradicts the intrinsic nature underlying observations of the global weather system, limiting forecast performance. To address this, we propose a novel Spatial Structured Attention Block in this paper. It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph, and aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention---considering both spatial proximity and global correlation. Building on this block, we develop a multiscale spatiotemporal forecasting model S$^2$Transformer by progressively expanding subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. The experimental results show that it can achieve performance improvements up to 16.8% over time series forecasting baselines at low running costs.

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AAAI Conference 2026 Conference Paper

ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning

  • Zongsheng Cao
  • Anran Liu
  • Yangfan He
  • Jing Li
  • Bo Zhang
  • Zigan Wang

Retrieval-augmented generation (RAG) has greatly improved Large Language Models (LLMs) by adding external knowledge. However, current RAG-based methods face difficulties with long-context video understanding due to two main challenges. First, Current RAG-based methods for long-context video understanding struggle to effectively integrate multimodal and long-range temporal information, resulting in fragmented and context-insensitive knowledge representations. Furthermore, their retrieval mechanisms often rely on static textual matching, failing to dynamically align user queries with the most relevant video segments and leading to suboptimal downstream performance. To overcome these issues, we introduce ViG-RAG, a new framework to enhance long-context video understanding through structured textual knowledge grounding and multi-modal retrieval. Specifically, we segment video transcripts into structured units, extract key entities, form temporal connections, and assign confidence for evidence, enabling coherent long-range reasoning. In this way, it utilizes a knowledge-aware grounding mechanism and a context-aware retrieval process that dynamically builds a probabilistic temporal knowledge graph to organize multi-video content. To improve retrieval accuracy, we propose a hybrid retrieval strategy for semantic and temporal features, with an adaptive distribution modeling the relevance. In this way, it achieves the optimal retrieval distribution for each query, enhancing generation efficiency by reducing unnecessary computations. On top of this, ViG-RAG uses a vision-language model to integrate semantic anchors, expanded contextual fields, and selected video frames, generating an accurate response. We evaluate ViG-RAG on several benchmarks, demonstrating that it significantly surpasses current RAG-based methods.

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JBHI Journal 2025 Journal Article

A Non-Invasive Blood Glucose Detection System Based on Photoplethysmogram With Multiple Near-Infrared Sensors

  • Kai Du
  • Jing Li
  • Zhiyi Huang
  • Houbing Song
  • Yuanting Zhang
  • Yuan Zhang
  • Zhen Mei

Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0. 242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.

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EAAI Journal 2025 Journal Article

Bayesian bidirectional long short-term memory-based kinematics-dynamics fusion for fault-tolerant vehicle state estimation under yaw rate sensor failures

  • Min Gao
  • Jiaqi Li
  • Wei Wang
  • Renguang Wang
  • Jin Luo
  • Jing Li

Accurate estimation of vehicle states is a fundamental component of vehicle stability control systems. To address the issue of inaccurate estimation of vehicle state parameters resulting from yaw rate sensor failures, this study proposes a three-mode collaborative fault-tolerant state estimation method based on Bayesian Bidirectional Long Short-Term Memory (BiLSTM) kinematics-dynamics fusion. First, the kinematics-based method is established using the kinematics model. Second, the dynamics-based method is designed by integrating the Unscented Kalman Filter (UKF) with the dynamics model. Subsequently, a BiLSTM network fusion model based on Bayesian optimization is presented. The model utilizes estimates from kinematic and kinetic methods as a priori inputs and combines the bidirectional information capturing capability of BiLSTM with hyperparameter tuning from Bayesian optimization. The results indicate that when the yaw rate sensor fails, the proposed method achieves an average Root Mean Square Error (RMSE) of 0. 0276 km per hour (km/h) for longitudinal speed, 0. 0008 radian (rad) for side slip angle, and 0. 0072 radian per second (rad/s) for yaw rate across all scenarios. This performance demonstrates a superiority over various maneuvers. This paper combines kinematics, dynamics, and deep learning to provide a reliable solution for fault-tolerant estimation of vehicle states.

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AAAI Conference 2025 Conference Paper

BeyondGender: A Multifaceted Bilingual Dataset for Practical Sexism Detection

  • Xuan Luo
  • Li Yang
  • Han Zhang
  • Geng Tu
  • Qianlong Wang
  • Keyang Ding
  • Chuang Fan
  • Jing Li

Sexism affects both women and men, yet research often overlooks misandry and suffers from overly broad annotations that limit AI applications. To address this, we introduce BeyondGender, a dataset meticulously annotated according to the latest definitions of misogyny and misandry. It features innovative multifaceted labels encompassing aspects of sexism, gender, phrasing, misogyny, and misandry. The dataset includes 6K English and 1.7K Chinese sexism instances, alongside 13K non-sexism examples. Our evaluations of masked language models and large language models reveal that they detect misogyny in English and misandry in Chinese more effectively, with F1-scores of 0.87 and 0.62, respectively. However, they frequently misclassify hostile and mild comments, underscoring the complexity of sexism detection. Parallel corpus experiments suggest promising data augmentation strategies to enhance AI systems for nuanced sexism detection, and our dataset can be leveraged to improve value alignment in large language models.

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NeurIPS Conference 2025 Conference Paper

Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation

  • hanzhuo tan
  • Xiaolong Tian
  • Hanrui Qi
  • Jiaming Liu
  • Siyi Wang
  • GAO Zuchen
  • Qi Luo
  • Jing Li

Recent advances in LLM-based decompilers have been shown effective to convert low-level binaries into human-readable source code. However, there still lacks a comprehensive benchmark that provides large-scale binary-source function pairs, which is critical for advancing the LLM decompilation technology. Creating accurate binary-source mappings incurs severe issues caused by complex compilation settings and widespread function inlining that obscure the correspondence between binaries and their original source code. Previous efforts have either relied on used contest‐style benchmarks, synthetic binary–source mappings that diverge significantly from the mappings in real world, or partially matched binaries with only code lines or variable names, compromising the effectiveness of analyzing the binary functionality. To alleviate these issues, we introduce Decompile-Bench, the first open-source dataset comprising two million binary-source function pairs condensed from 100 million collected function pairs, i. e. , 450GB of binaries compiled from permissively licensed GitHub projects. For the evaluation purposes, we also developed a benchmark Decompile-Bench-Eval including manually crafted binaries from the well-established HumanEval and MBPP, alongside the compiled GitHub repositories released after 2025 to mitigate data leakage issues. We further explore commonly-used evaluation metrics to provide a thorough assessment of the studied LLM decompilers and find that fine-tuning with Decompile-Bench causes a 20% improvement over previous benchmarks in terms of the re-executability rate. Our code and data has been released in HuggingFace and Github. https: //github. com/anonepo/LLM4Decompile

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ICML Conference 2025 Conference Paper

Few-Shot Learner Generalizes Across AI-Generated Image Detection

  • Shiyu Wu
  • Jing Liu
  • Jing Li
  • Yequan Wang

Current fake image detectors trained on large synthetic image datasets perform satisfactorily on limited studied generative models. However, these detectors suffer a notable performance decline over unseen models. Besides, collecting adequate training data from online generative models is often expensive or infeasible. To overcome these issues, we propose Few-Shot Detector (FSD), a novel AI-generated image detector which learns a specialized metric space for effectively distinguishing unseen fake images using very few samples. Experiments show that FSD achieves state-of-the-art performance by $+11. 6%$ average accuracy on the GenImage dataset with only $10$ additional samples. More importantly, our method is better capable of capturing the intra-category commonality in unseen images without further training. Our code is available at https: //github. com/teheperinko541/Few-Shot-AIGI-Detector.

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EAAI Journal 2025 Journal Article

Health- and behavior-aware energy management strategy for fuel cell hybrid electric vehicles based on parallel deep deterministic policy gradient learning

  • Haochen Sun
  • Jing Li
  • Chun Cheng
  • Suzhen Shi
  • Jing Wang
  • Jingjing Lin
  • Yang Liu

To find a more optimal way to solve the energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs), the majority of existing research focuses on external driving conditions, while the driver’s behavior as a more important internal influence factor also needs to be taken into account. In this paper, a health- and behavior-aware two-layer hierarchical energy management framework using an improved adaptive parallel deep deterministic policy gradient (DDPG) learning algorithm is proposed for obtaining the optimal EMS of a multi-source FCHEV. In the upper layer, machine learning approaches are employed to recognize the real-time driver’s behavior, and Pontryagin’s minimum principle is applied to calculate the optimal equivalent factor of each driver’s behavior. In the lower layer, to protect the service life of fuel cell and battery as well as increase the learning efficiency, an adaptive fuzzy filter is used, and a health- and behavior-aware multi-objective adaptive equivalent consumption minimization strategy model is constructed and solved by an improved adaptive parallel DDPG-based algorithm. Simulation results show that, the EMS obtained by the proposed DDPG algorithm can achieve the highest fuel cell (FC) working efficiency (approximate to 56%), apparently reduce the degree of degradation of battery (BAT) from 0. 42% to 0. 28%, and achieve a reduction of 9. 24% in terms of the total cost to use compared with deep Q network (DQN)-based EMS.

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AAAI Conference 2025 Conference Paper

Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering

  • Yifan Lu
  • Yigeng Zhou
  • Jing Li
  • Yequan Wang
  • Xuebo Liu
  • Daojing He
  • Fangming Liu
  • Min Zhang

Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without negatively impacting other unrelated knowledge, offers a potential solution for addressing MHQA challenges with LLMs. However, current solutions struggle to effectively resolve issues of knowledge conflicts. Most parameter-preserving editing methods are hindered by inaccurate retrieval and overlook secondary editing issues, which can introduce noise into the reasoning process of LLMs. In this paper, we introduce KEDKG, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers. KEDKG involves two primary steps: dynamic knowledge graph construction and knowledge graph augmented generation. Initially, KEDKG autonomously constructs a dynamic knowledge graph to store revised information while resolving potential knowledge conflicts. Subsequently, it employs a fine-grained retrieval strategy coupled with an entity and relation detector to enhance the accuracy of graph retrieval for LLM generation. Experimental results on benchmarks show that KEDKG surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.

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NeurIPS Conference 2025 Conference Paper

Learning to Watermark: A Selective Watermarking Framework for Large Language Models via Multi-Objective Optimization

  • Chenrui Wang
  • Junyi Shu
  • Billy Chiu
  • Yu Li
  • Saleh Alharbi
  • Min Zhang
  • Jing Li

The rapid development of LLMs has raised concerns about their potential misuse, leading to various watermarking schemes that typically offer high detectability. However, existing watermarking techniques often face trade-off between watermark detectability and generated text quality. In this paper, we introduce Learning to Watermark (LTW), a novel selective watermarking framework that leverages multi-objective optimization to effectively balance these competing goals. LTW features a lightweight network that adaptively decides when to apply the watermark by analyzing sentence embeddings, token entropy, and current watermarking ratio. Training of the network involves two specifically constructed loss functions that guide the model toward Pareto-optimal solutions, thereby harmonizing watermark detectability and text quality. By integrating LTW with two baseline watermarking methods, our experimental evaluations demonstrate that LTW significantly enhances text quality without compromising detectability. Our selective watermarking approach offers a new perspective for designing watermarks for LLMs and a way to preserve high text quality for watermarks. The code is publicly available at: https: //github. com/fattyray/learning-to-watermark

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NeurIPS Conference 2025 Conference Paper

Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning

  • Jian Liu
  • Jing Xu
  • Song Guo
  • Jing Li
  • jingfeng Guo
  • Jiaao Yu
  • Haohan Weng
  • Biwen Lei

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present $\textbf{Mesh-RFT}$, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24. 6\% and improves Topology Score (TS) by 3. 8\% over pre-trained models, while outperforming global DPO methods with a 17. 4\% HD reduction and 4. 9\% TS gain. These results demonstrate Mesh-RFT’s ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation.

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NeurIPS Conference 2025 Conference Paper

NeurIPT: Foundation Model for Neural Interfaces

  • Zitao Fang
  • Chenxuan Li
  • Hongting Zhou
  • Shuyang Yu
  • Guodong DU
  • Ashwaq Qasem
  • Yang Lu
  • Jing Li

Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there has been growing interest in establishing foundation models (FMs) to scale up and generalize neural decoding. Despite showing early potential, applying FMs to EEG remains challenging due to substantial inter-subject, inter-task, and inter-condition variability, as well as diverse electrode configurations across recording setups. To tackle these open challenges, we propose NeurIPT, a foundation model tailored for diverse EEG-based Neur al I nterfaces with a P re-trained T ransformer by capturing both homogeneous and heterogeneous spatio-temporal characteristics inherent in EEG signals. Temporally, we introduce Amplitude-Aware Masked Pretraining (AAMP), masking based on signal amplitude rather than random intervals, to learn robust representations across varying signal intensities beyond local interpolation. Moreover, this temporal representation is enhanced by a progressive Mixture-of-Experts (MoE) architecture, where specialized expert subnetworks are progressively introduced at deeper layers, adapting effectively to the diverse temporal characteristics of EEG signals. Spatially, NeurIPT leverages the 3D physical coordinates of electrodes, enabling effective transfer across varying EEG settings, and develops Intra-Inter Lobe Pooling (IILP) during fine-tuning to efficiently exploit regional brain features. Empirical evaluations across nine downstream BCI datasets, via fine-tuning and training from scratch, demonstrated NeurIPT consistently achieved state-of-the-art performance, highlighting its broad applicability and robust generalization. Our work pushes forward the state of FMs in EEG and offers insights into scalable and generalizable neural information processing systems.

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EAAI Journal 2025 Journal Article

NFIoT-GATE-DTL IDS: Genetic algorithm-tuned ensemble of deep transfer learning for NetFlow-based intrusion detection system for internet of things

  • Jing Li
  • Hewan Chen
  • Mohd Shahizan Othman
  • Naomie Salim
  • Lizawati Mi Yusuf
  • Shamini Raja Kumaran

Industry 5. 0 requires robust Internet of Things (IoT) networks, which are constantly vulnerable to cyber threats. Despite contributions in intrusion detection systems (IDS), creating generalized attack classification models remains a challenge. Conventional machine learning or deep learning-driven IDSs struggle to retain learned knowledge and to keep up with rapidly increasing IoT threats, whereas transfer learning-based models may lose the resilience required for model generalization. Thus, this study proposes a Genetic Algorithm-Tuned Ensemble of Deep Transfer Learning for NetFlow-Based Intrusion Detection System for Internet of Things (NFIoT-GATE-DTL IDS) to fill the gap. Two public NetFlow IoT datasets are preprocessed and transformed into three-dimensional images for convolutional neural networks (CNNs). Six pre-trained CNNs, including Xception, Inception, MobileNet, MobileNetV2, DenseNet121, and EfficientNetB0, undergo hyperparameter optimization using a Genetic Algorithm (GA). The top-five models are then combined using a soft voting ensemble to boost detection robustness across diverse attack types. Validation methods are employed, including assessing the impact of GA optimization, comparing it to optimizers like the covariance matrix adaptation evolution strategy and the coyote optimization algorithm, and comparing it to cutting-edge studies. The proposed framework consistently achieves 100% accuracy across 15 attack classes, including three highly minority threats like backdoors, ransomware, and theft in IoT networks. Furthermore, the NFIoT-GATE-DTL IDS outperforms recent methodologies, achieving a 5–7% multi-classification higher accuracy on average. This research significantly contributes to a robust IDS with a high detection rate for NetFlow-based IoT networks.

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NeurIPS Conference 2025 Conference Paper

Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning

  • Zhe Hu
  • Jing Li
  • Zhongzhu Pu
  • Hou Pong (Ken) Chan
  • Yu Yin

Vision Language Models exhibit impressive performance for various tasks, yet they often lack the sophisticated situational reasoning required for complex decision-making. This paper shows that VLMs can achieve surprisingly strong decision-making performance when visual scenes are replaced by textual descriptions, suggesting foundational reasoning can be effectively learned from language. Motivated by this insight, we propose Praxis-VLM, a reasoning VLM for vision-grounded decision-making. Praxis-VLM employs the GRPO algorithm on textual scenarios to instill robust reasoning capabilities, where models learn to evaluate actions and their consequences. These reasoning skills, acquired purely from text, successfully transfer to multimodal inference with visual inputs, significantly reducing reliance on scarce paired image-text training data. Experiments across diverse decision-making benchmarks demonstrate that Praxis-VLM substantially outperforms standard supervised fine-tuning, exhibiting superior performance and generalizability. Further analysis confirms that our models engage in explicit and effective reasoning, underpinning their enhanced performance and adaptability.

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NeurIPS Conference 2025 Conference Paper

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning

  • Yihong Tang
  • Kehai Chen
  • Muyun Yang
  • Zheng-Yu Niu
  • Jing Li
  • Tiejun Zhao
  • Min Zhang

The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent RPAs are often built on explicit dialogue data, lacking deep, human-like internal thought processes, resulting in superficial knowledge and style expression. While Large Reasoning Models (LRMs) can be employed to simulate character thought, their direct application is hindered by attention diversion (i. e. , RPAs forget their role) and style drift (i. e. , overly formal and rigid reasoning rather than character-consistent reasoning). To address these challenges, this paper introduces a novel Role-Aware Reasoning (RAR) method, which consists of two important stages: Role Identity Activation (RIA) and Reasoning Style Optimization (RSO). RIA explicitly guides the model with character profiles during reasoning to counteract attention diversion, and then RSO aligns reasoning style with the character and scene via LRM distillation to mitigate style drift. Extensive experiments demonstrate that the proposed RAR significantly enhances the performance of RPAs by effectively addressing attention diversion and style drift.

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EAAI Journal 2025 Journal Article

TSD-DETR: A lightweight real-time detection transformer of traffic sign detection for long-range perception of autonomous driving

  • Lili Zhang
  • Kang Yang
  • Yucheng Han
  • Jing Li
  • Wei Wei
  • Hongxin Tan
  • Pei Yu
  • Ke Zhang

The key to accurate perception and efficient decision making of autonomous driving is the long-range detection of traffic signs. Long-range detection of traffic signs has the problems of small traffic sign size and complex background. In order to solve these problems, this paper proposes a lightweight model for traffic sign detection based on real-time detection transformer (TSD-DETR). Firstly, the feature extraction module is constructed using multiple types of convolutional modules. The model extracts multi-scale features of different levels to enhance feature extraction ability. Then, small object detection module and detection head are designed to extract and detect shallow features. It can improve the detection of small traffic signs. Finally, Efficient Multi-Scale Attention is introduced to adjust the channel weights. It aggregates the output features of three parallel branches interactively. TSD-DETR achieves a mean average precision (mAp) of 96. 8% on Tsinghua-Tencent 100K dataset. It is improved by 2. 5% compared with real-time detection transformer. In small object detection, mAp improved by 9%. TSD-DETR achieves 99. 4% mAp on the Changsha University of Science and Technology Chinese Traffic Sign Detection Benchmark dataset, with an improvement of 0. 6%. The experimental results show that TSD-DETR reduces the number of parameters by 9. 06M by optimizing the model structure. On the premise of ensuring the real-time performance of the model, the detection accuracy of the model is improved greatly. The results of ablation experiments show that the feature extraction module and small object detection module proposed in this paper are conducive to improving the detection accuracy.

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NeurIPS Conference 2025 Conference Paper

UMoE: Unifying Attention and FFN with Shared Experts

  • Yuanhang Yang
  • Chaozheng Wang
  • Jing Li

Sparse Mixture of Experts (MoE) architectures have emerged as a promising approach for scaling Transformer models. While initial works primarily incorporated MoE into feed-forward network (FFN) layers, recent studies have explored extending the MoE paradigm to attention layers to enhance model performance. However, existing attention-based MoE layers require specialized implementations and demonstrate suboptimal performance compared to their FFN-based counterparts. In this paper, we aim to unify MoE designs in attention and FFN layers by introducing a novel reformulation of the attention mechanism, that reveals an underlying FFN-like structure within attention modules. Our proposed architecture, UMoE, achieves superior performance through attention-based MoE layers while enabling efficient parameter sharing between FFN and attention components.

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EAAI Journal 2024 Journal Article

A multi-type semantic interaction and enhancement method for tax question understanding

  • Jing Li
  • Dezheng Zhang
  • Yonghong Xie
  • Aziguli Wulamu

Problem classification serves as a fundamental process in a tax intelligence consulting system, enabling the categorization of user-posed questions according to their semantic attributes. This categorization is pivotal in ensuring accurate question comprehension. Nevertheless, the inclusion of intricate professional terminology and the frequent alterations in linguistic structures associated with tax-related matters may culminate in suboptimal classification outcomes and hinder the precise comprehension of user demands. To address these issues, we propose a multitype semantic interaction and enhancement method (MtSIEM) to classify tax related issues that integrates entity and nonentity semantics to represent the semantic features of tax-related domain issues. Specifically, a pretraining language model and multigram mechanism are adopted to enhance the feature extraction ability. A soft attention module is also simplified to allocate interaction information weights, thereby adaptively determining the importance of the feature elements. These three components are used to perform precise learning on the tax question data. Subsequently, a dynamic routing architecture is employed to capture the relationships between the different problem features, resulting in predictive vectors. A series of comparative experiments on tax question data demonstrated that the proposed model achieved a classification accuracy of approximately 94. 46%, an improvement of 2. 99% compared with the baseline. Therefore, the proposed model can be utilized to predict the semantic category of tax-related issues, assisting intelligent tax advisory systems in matching questions with the most relevant knowledge and professional domains, thereby enabling faster retrieval of pertinent information and enhancing the timeliness of responses.

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JBHI Journal 2024 Journal Article

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals

  • Zhian Liu
  • Lichengxi Si
  • Shaoxian Shi
  • Jing Li
  • Jing Zhu
  • Won Hee Lee
  • Sio-Long Lo
  • Xiangguo Yan

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO 2 ) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69. 27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

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NeurIPS Conference 2024 Conference Paper

Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions

  • Zhe Hu
  • Tuo Liang
  • Jing Li
  • Yiren Lu
  • Yunlai Zhou
  • Yiran Qiao
  • Jing Ma
  • Yu Yin

Recent advancements in large vision language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction. We introduce the YesBut benchmark, which comprises tasks of varying difficulty aimed at assessing AI's capabilities in recognizing and interpreting these comics, ranging from literal content comprehension to deep narrative reasoning. Through extensive experimentation and analysis of recent commercial or open-sourced large vision language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics. Our results show that even the state-of-the-art models still struggle with this task. Our findings offer insights into the current limitations and potential improvements for AI in understanding human creative expressions.

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ICML Conference 2024 Conference Paper

Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

  • Mo Yu
  • Qiujing Wang
  • Shunchi Zhang
  • Yisi Sang
  • Kangsheng Pu
  • Zekai Wei
  • Han Wang
  • Liyan Xu

When reading a story, humans can quickly understand new fictional characters with a few observations, mainly by drawing analogies to fictional and real people they already know. This reflects the few-shot and meta-learning essence of humans’ inference of characters’ mental states, i. e. , theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP dataset in a realistic narrative understanding scenario, ToM-in-AMC. Our dataset consists of $\sim$1, 000 parsed movie scripts, each corresponding to a few-shot character understanding task that requires models to mimic humans’ ability of fast digesting characters with a few starting scenes in a new movie. We further propose a novel ToM prompting approach designed to explicitly assess the influence of multiple ToM dimensions. It surpasses existing baseline models, underscoring the significance of modeling multiple ToM dimensions for our task. Our extensive human study verifies that humans are capable of solving our problem by inferring characters’ mental states based on their previously seen movies. In comparison, all the AI systems lag $>20%$ behind humans, highlighting a notable limitation in existing approaches’ ToM capabilities. Code and data are available at https: //github. com/ShunchiZhang/ToM-in-AMC

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IJCAI Conference 2024 Conference Paper

Fuel-Saving Route Planning with Data-Driven and Learning-Based Approaches – A Systematic Solution for Harbor Tugs

  • Shengming Wang
  • Xiaocai Zhang
  • Jing Li
  • Xiaoyang Wei
  • Hoong Chuin Lau
  • Bing Tian Dai
  • Binbin Huang
  • Zhe Xiao

In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based on Deep Deterministic Policy Gradient (DDPG) framework is developed considering the characteristics and obstructions of waterway in Singapore as well as the environmental factors to learn the optimal transit strategy that minimizes fuel consumption. We also demonstrate the efficacy of the solution to generate routes from origin to destination terminals, exhibiting significantly reduced fuel consumption in comparison to real-world transit scenarios.

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AAAI Conference 2024 Conference Paper

Fully Data-Driven Pseudo Label Estimation for Pointly-Supervised Panoptic Segmentation

  • Jing Li
  • Junsong Fan
  • Yuran Yang
  • Shuqi Mei
  • Jun Xiao
  • Zhaoxiang Zhang

The core of pointly-supervised panoptic segmentation is estimating accurate dense pseudo labels from sparse point labels to train the panoptic head. Previous works generate pseudo labels mainly based on hand-crafted rules, such as connecting multiple points into polygon masks, or assigning the label information of labeled pixels to unlabeled pixels based on the artificially defined traversing distance. The accuracy of pseudo labels is limited by the quality of the hand-crafted rules (polygon masks are rough at object contour regions, and the traversing distance error will result in wrong pseudo labels). To overcome the limitation of hand-crafted rules, we estimate pseudo labels with a fully data-driven pseudo label branch, which is optimized by point labels end-to-end and predicts more accurate pseudo labels than previous methods. We also train an auxiliary semantic branch with point labels, it assists the training of the pseudo label branch by transferring semantic segmentation knowledge through shared parameters. Experiments on Pascal VOC and MS COCO demonstrate that our approach is effective and shows state-of-the-art performance compared with related works. Codes are available at https://github.com/BraveGroup/FDD.

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JMLR Journal 2024 Journal Article

Generative Adversarial Ranking Nets

  • Yinghua Yao
  • Yuangang Pan
  • Jing Li
  • Ivor W. Tsang
  • Xin Yao

We propose a new adversarial training framework -- generative adversarial ranking networks (GARNet) to learn from user preferences among a list of samples so as to generate data meeting user-specific criteria. Verbosely, GARNet consists of two modules: a ranker and a generator. The generator fools the ranker to raise generated samples to the top; while the ranker learns to rank generated samples at the bottom. Meanwhile, the ranker learns to rank samples regarding the interested property by training with preferences collected on real samples. The adversarial ranking game between the ranker and the generator enables an alignment between the generated data distribution and the user-preferred data distribution with theoretical guarantees and empirical verification. Specifically, we first prove that when training with full preferences on a discrete property, the learned distribution of GARNet rigorously coincides with the distribution specified by the given score vector based on user preferences. The theoretical results are then extended to partial preferences on a discrete property and further generalized to preferences on a continuous property. Meanwhile, numerous experiments show that GARNet can retrieve the distribution of user-desired data based on full/partial preferences in terms of various interested properties (i.e., discrete/continuous property, single/multiple properties). Code is available at https://github.com/EvaFlower/GARNet. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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IJCAI Conference 2024 Conference Paper

LocMoE: A Low-overhead MoE for Large Language Model Training

  • Jing Li
  • Zhijie Sun
  • Xuan He
  • Li Zeng
  • Yi Lin
  • Entong Li
  • Binfan Zheng
  • Rongqian Zhao

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Σ model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12. 68% to 22. 24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy.

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NeurIPS Conference 2024 Conference Paper

Parameter Competition Balancing for Model Merging

  • Guodong DU
  • Junlin Lee
  • Jing Li
  • Runhua Jiang
  • Yifei Guo
  • Shuyang Yu
  • Hanting Liu
  • Sim K. Goh

While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named PCB-Merging (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models, outperforming existing model merging methods.

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EAAI Journal 2024 Journal Article

Power load combination forecasting system based on longitudinal data selection

  • Yan Xu
  • Jing Li
  • Yan Dong
  • Pei Du

In the current situation of energy supply shortage and surging demand, effective and stable load forecasting is essential to ensure reliable power supply and the security of the power system. However, due to some factors such as periodicity and seasonality, the power load sequence shows complex nonlinear characteristics. Meanwhile, the current load forecasting lacks the ability to explore the data deeply, and it is difficult to accurately predict the short-term trend and fluctuation range. To remedy these limitations, this study proposes a hybrid point-interval prediction system (HPILS). The system integrates data preprocessing, optimal model selection, multi-objective optimization combination and interval prediction modules. To verify the performance of the proposed system, four load data sets in Australia are used as examples to conduct experiments. The experimental results demonstrate that HPILS can effectively provide the predicting power load trend changes and fluctuation ranges. Specifically, compared with the benchmark model, HPILS has a 13. 47% ∼ 67. 89% improvement in point prediction and a 1. 67% ∼ 72. 08% improvement in interval prediction. In addition, a series of discussion tests are performed to verify the superiority of the proposed system and further confirm the validity of our proposed system.

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YNIMG Journal 2024 Journal Article

Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis

  • Jing Li
  • Fan Yang
  • Kathleen Zhang
  • Shiqiang Wu
  • James Niemeyer
  • Mingrui Zhao
  • Peijuan Luo
  • Nan Li

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

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EAAI Journal 2024 Journal Article

Robust semi-supervised learning with reciprocal weighted mixing distribution alignment

  • Ziyu Cheng
  • Xianmin Wang
  • Jing Li
  • Feng Liu
  • Yutong Xie
  • Haiyan Liang

Recent semi-supervised learning(SSL) methods have achieved great success owing to the impressive performances brought by the combination of pseudo-labeling and consistency regularization. These methods often use pre-defined constant thresholds or dynamical thresholds to select unlabeled samples that contribute to training. However, many correct/incorrect pseudo-labels may be ignored/selected. Especially in distribution mismatched scenario, threshold-adjusted strategy is often complex and ineffective. To alleviate this issue, we develop a simple yet powerful framework whose idea is to abandon this strategy and utilize distribution alignment to adjust the predictions generated from a biased model softly. Specifically, first, we create two classifiers to predict pseudo-label(i. e. , the sample belongs to a specific category) and complementary pseudo-label(i. e. , the sample does not belong to a specific category), respectively. Second, by maintaining the distributions of pseudo-labels, complementary pseudo-labels and their reverse versions from past iterations, we enforce a reciprocal weighted mixing according to the predicted category weights. Third, a reciprocal distribution alignment is applied to the mixed distributions to adjust the predicted distributions. Finally, we propose Implication Alignment Loss, which keeps consistency between the predictions of the same implications but from different versions. We empirically demonstrate the effectiveness of our proposed method in comparison with state-of-the-art benchmarks. Especially, our method achieves a 1. 18% error rate reduction over the latest state-of-the-art method MutexMatch on CIFAR-10 with 2 labels per class and exhibits robustness in the scenario of mismatched distribution.

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AAAI Conference 2024 Conference Paper

Tensorized Label Learning on Anchor Graph

  • Jing Li
  • Quanxue Gao
  • Qianqian Wang
  • Wei Xia

Graph-based multimedia data clustering has attracted much attention due to the impressive clustering performance for arbitrarily shaped multimedia data. However, existing graph-based clustering methods need post-processing to get labels for multimedia data with high computational complexity. Moreover, it is sub-optimal for label learning due to the fact that they exploit the complementary information embedded in data with different types pixel by pixel. To handle these problems, we present a novel label learning model with good interpretability for clustering. To be specific, our model decomposes anchor graph into the products of two matrices with orthogonal non-negative constraint to directly get soft label without any post-processing, which remarkably reduces the computational complexity. To well exploit the complementary information embedded in multimedia data, we introduce tensor Schatten p-norm regularization on the label tensor which is composed of soft labels of multimedia data. The solution can be obtained by iteratively optimizing four decoupled sub-problems, which can be solved more efficiently with good convergence. Experimental results on various datasets demonstrate the efficiency of our model.

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JBHI Journal 2024 Journal Article

TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification

  • Aixi Qu
  • Qiang Wu
  • Jing Wang
  • Luyue Yu
  • Jing Li
  • Ju Liu

In clinical settings, the implementation of deep neural networks is impeded by the prevalent problems of label scarcity and class imbalance in medical images. To mitigate the need for labeled data, semi-supervised learning (SSL) has gained traction. However, existing SSL schemes exhibit certain limitations. 1) They commonly fail to address the class imbalance problem. Training with imbalanced data makes the model's prediction biased towards majority classes, consequently introducing prediction bias. 2) They usually suffer from training bias arising from unreasonable training strategies, such as strong coupling between the generation and utilization of pseudo labels. To address these problems, we propose a novel SSL framework called Tri-Net with Cross-Balanced pseudo supervision (TNCB). Specifically, two student networks focusing on different learning tasks and a teacher network equipped with an adaptive balancer are designed. This design enables the teacher model to pay more focus on minority classes, thereby reducing prediction bias. Additionally, we propose a virtual optimization strategy to further enhance the teacher model's resistance to class imbalance. Finally, to fully exploit valuable knowledge from unlabeled images, we employ cross-balanced pseudo supervision, where an adaptive cross loss function is introduced to reduce training bias. Extensive evaluation on four datasets with different diseases, image modalities, and imbalance ratios consistently demonstrate the superior performance of TNCB over state-of-the-art SSL methods. These results indicate the effectiveness and robustness of TNCB in addressing imbalanced medical image classification challenges.

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NeurIPS Conference 2024 Conference Paper

Towards Harmless Rawlsian Fairness Regardless of Demographic Prior

  • Xuanqian Wang
  • Jing Li
  • Ivor W. Tsang
  • Yew-Soon Ong

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https: //github. com/wxqpxw/VFair.

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YNIMG Journal 2024 Journal Article

Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia

  • Guanqun Yao
  • Jing Luo
  • Ting Zou
  • Jing Li
  • Shuang Hu
  • Langxiong Yang
  • Xinrong Li
  • Yu Tian

Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.

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EAAI Journal 2023 Journal Article

An adaptive sparse general regression neural network-based force observer for teleoperation system

  • Mingzhang Pan
  • Jing Li
  • Qiye Yang
  • Yupeng Wang
  • Yu Tang
  • Lei Pan
  • Xianbao Jiang
  • Yizhong Lin

Restricted by factors such as small robot size and harsh operating environment, the inability to obtain the interaction force between the robot arm and the environment through force sensors has become problematic in promoting the application of teleoperation systems in minimally invasive surgery, nuclear waste cleanup, and other fields. To accurately obtain the interaction force without the force sensors, a force observer based on an adaptive sparse general regression neural network (ASGRNN) is proposed in this paper. The proposed force observer uses a machine learning-based approach to obtain estimated force, thus eliminating the need for the dynamic parameters of the robot arm. Also, an innovative feature selection method incorporating the wrapper method and sparse regularization is proposed to select the input features of the force observer. Secondly, two new criteria are defined to eliminate the useless support vectors in the model. In addition, an improved antlion optimization algorithm (IALO) is proposed to optimize the bandwidth parameters of the model. To verify the performance of the proposed force observer, a 6-degree-of-freedom teleoperation robot experimental platform is built and compared with three existing force estimation models. The results show that the proposed force observer outperforms the existing model in terms of estimation accuracy, and the mean square error (MSE) is at least 35. 79% lower than the existing model. In conclusion, this paper provides a feasible and effective force observer for a teleoperation system where force sensors are not applicable and dynamic parameters are non-knowable.

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IJCAI Conference 2023 Conference Paper

Cross-Domain Facial Expression Recognition via Disentangling Identity Representation

  • Tong Liu
  • Jing Li
  • Jia Wu
  • Lefei Zhang
  • Shanshan Zhao
  • Jun Chang
  • Jun Wan

Most existing cross-domain facial expression recognition (FER) works require target domain data to assist the model in analyzing distribution shifts to overcome negative effects. However, it is often hard to obtain expression images of the target domain in practical applications. Moreover, existing methods suffer from the interference of identity information, thus limiting the discriminative ability of the expression features. We exploit the idea of domain generalization (DG) and propose a representation disentanglement model to address the above problems. Specifically, we learn three independent potential subspaces corresponding to the domain, expression, and identity information from facial images. Meanwhile, the extracted expression and identity features are recovered as Fourier phase information reconstructed images, thereby ensuring that the high-level semantics of images remain unchanged after disentangling the domain information. Our proposed method can disentangle expression features from expression-irrelevant ones (i. e. , identity and domain features). Therefore, the learned expression features exhibit sufficient domain invariance and discriminative ability. We conduct experiments with different settings on multiple benchmark datasets, and the results show that our method achieves superior performance compared with state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

GRLSTM: Trajectory Similarity Computation with Graph-Based Residual LSTM

  • Silin Zhou
  • Jing Li
  • Hao Wang
  • Shuo Shang
  • Peng Han

The computation of trajectory similarity is a crucial task in many spatial data analysis applications. However, existing methods have been designed primarily for trajectories in Euclidean space, which overlooks the fact that real-world trajectories are often generated on road networks. This paper addresses this gap by proposing a novel framework, called GRLSTM (Graph-based Residual LSTM). To jointly capture the properties of trajectories and road networks, the proposed framework incorporates knowledge graph embedding (KGE), graph neural network (GNN), and the residual network into the multi-layer LSTM (Residual-LSTM). Specifically, the framework constructs a point knowledge graph to study the multi-relation of points, as points may belong to both the trajectory and the road network. KGE is introduced to learn point embeddings and relation embeddings to build the point fusion graph, while GNN is used to capture the topology structure information of the point fusion graph. Finally, Residual-LSTM is used to learn the trajectory embeddings.To further enhance the accuracy and robustness of the final trajectory embeddings, we introduce two new neighbor-based point loss functions, namely, graph-based point loss function and trajectory-based point loss function. The GRLSTM is evaluated using two real-world trajectory datasets, and the experimental results demonstrate that GRLSTM outperforms all the state-of-the-art methods significantly.

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NeurIPS Conference 2023 Conference Paper

Orthogonal Non-negative Tensor Factorization based Multi-view Clustering

  • Jing Li
  • Quanxue Gao
  • Qianqian Wang
  • Ming Yang
  • Wei Xia

Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have attracted much attention due to their advantages in clustering interpretability. However, existing NMF-based multi-view clustering methods perform NMF on each view respectively and ignore the impact of between-view. Thus, they can't well exploit the within-view spatial structure and between-view complementary information. To resolve this issue, we present orthogonal non-negative tensor factorization (Orth-NTF) and develop a novel multi-view clustering based on Orth-NTF with one-side orthogonal constraint. Our model directly performs Orth-NTF on the 3rd-order tensor which is composed of anchor graphs of views. Thus, our model directly considers the between-view relationship. Moreover, we use the tensor Schatten $p$-norm regularization as a rank approximation of the 3rd-order tensor which characterizes the cluster structure of multi-view data and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point. Extensive experiments on various benchmark datasets indicate that our proposed method is able to achieve satisfactory clustering performance.

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AAAI Conference 2023 Conference Paper

SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

  • Wenlu Du
  • Junyi Ye
  • Jingyi Gu
  • Jing Li
  • Hua Wei
  • Guiling Wang

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.

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IJCAI Conference 2022 Conference Paper

A Survey of Machine Narrative Reading Comprehension Assessments

  • Yisi Sang
  • Xiangyang Mou
  • Jing Li
  • Jeffrey Stanton
  • Mo Yu

As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.

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EAAI Journal 2022 Journal Article

Fault diagnosis of modular multilevel converter based on adaptive chirp mode decomposition and temporal convolutional network

  • Qun Guo
  • Xinhao Zhang
  • Jing Li
  • Gang Li

The reliability of the insulated gate bipolar transistors (IGBTs) is essential to the stable operation of the modular multilevel converter (MMC) system. However, there are a large number of IGBTs in the MMC system and the open-circuit faults of IGBTs are usually so hidden that it is difficult to find. Therefore, this article proposes a fault diagnosis framework based on temporal convolutional network (TCN) integrating adaptive chirp mode decomposition (ACMD) and silhouette coefficient (SC). First, ACMD is used to extract and reconstruct signal components from the original signal. Then, in order to avoid artificial selection of signal components, silhouette coefficient is introduced to characterize the importance of each component. Finally, the TCN model automatically extracts the features of the signal components and outputs the classification results. The main contributions are as follows: (1) A complete fault diagnosis framework that can adaptively extract features and perform fault classification is proposed in the paper. (2) For the MMC using the carrier-phase-shifted pulsewidth modulation strategy, the fault can be located to the IGBT by the output current. (3) Under certain noise conditions, the fault diagnosis proposed in the paper method still has good robustness. (4) The signal visualization of different residual blocks and channels explains the working mechanism of the AMCD-SC-TCN framework.

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IJCAI Conference 2022 Conference Paper

FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

  • Xuan Rao
  • Hao Wang
  • Liang Zhang
  • Jing Li
  • Shuo Shang
  • Peng Han

Traffic flow forecasting plays a vital role in the transportation domain. Existing studies usually manually construct correlation graphs and design sophisticated models for learning spatial and temporal features to predict future traffic states. However, manually constructed correlation graphs cannot accurately extract the complex patterns hidden in the traffic data. In addition, it is challenging for the prediction model to fit traffic data due to its irregularly-shaped distribution. To solve the above-mentioned problems, in this paper, we propose a novel learning-based method to learn a spatial-temporal correlation graph, which could make good use of the traffic flow data. Moreover, we propose First-Order Gradient Supervision (FOGS), a novel method for traffic flow forecasting. FOGS utilizes first-order gradients, rather than specific flows, to train prediction model, which effectively avoids the problem of fitting irregularly-shaped distributions. Comprehensive numerical evaluations on four real-world datasets reveal that the proposed methods achieve state-of-the-art performance and significantly outperform the benchmarks.

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IJCAI Conference 2022 Conference Paper

Interactive Information Extraction by Semantic Information Graph

  • Siqi Fan
  • Yequan Wang
  • Jing Li
  • Zheng Zhang
  • Shuo Shang
  • Peng Han

Information extraction (IE) mainly focuses on three highly correlated subtasks, i. e. , entity extraction, relation extraction and event extraction. Recently, there are studies using Abstract Meaning Representation (AMR) to utilize the intrinsic correlations among these three subtasks. AMR based models are capable of building the relationship of arguments. However, they are hard to deal with relations. In addition, the noises of AMR (i. e. , tags unrelated to IE tasks, nodes with unconcerned conception, and edge types with complicated hierarchical structures) disturb the decoding processing of IE. As a result, the decoding processing limited by the AMR cannot be worked effectively. To overcome the shortages, we propose an Interactive Information Extraction (InterIE) model based on a novel Semantic Information Graph (SIG). SIG can guide our InterIE model to tackle the three subtasks jointly. Furthermore, the well-designed SIG without noise is capable of enriching entity and event trigger representation, and capturing the edge connection between the information types. Experimental results show that our InterIE achieves state-of-the-art performance on all IE subtasks on the benchmark dataset (i. e. , ACE05-E+ and ACE05-E). More importantly, the proposed model is not sensitive to the decoding order, which goes beyond the limitations of AMR based methods.

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EAAI Journal 2022 Journal Article

Multi-camera joint spatial self-organization for intelligent interconnection surveillance

  • Congcong Li
  • Jing Li
  • Yuguang Xie
  • Jiayang Nie
  • Tao Yang
  • Zhaoyang Lu

The construction of smart city makes information interconnection play an increasingly important role in intelligent surveillance systems. Especially the interconnection among massive cameras is the key to realizing the evolution from current fragmented monitoring to interconnection surveillance. However, it remains a challenging problem in practical systems due to large sensor quantity, various camera types, and complex spatial layout. Aimed at this problem, this paper proposes a novel multi-camera joint spatial self-organization approach, which realizes interconnection surveillance by unifying cameras into one imaging space. Differing from existing back-end data association strategy, our method takes front-end data calibration as a breakthrough to relate surveillance data. Specifically, this paper first initials camera spatial parameter by sequence complementary feature integration. Through integrating complementarity and redundancy among sequence features, our method has robustness under scene dynamic changes and noise. Then, we propose a multi-camera joint optimization method based on common monitoring coverage correlation analysis to estimate a more accurate relative relationship. By leveraging the two strategies, the spatial relationship and visual data association across monitoring cameras are returned finally. Our system organizes all cameras into a unified imaging space by itself. Extensive experimental evaluations on an actual campus environment demonstrate our method achieves remarkable performance.

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JBHI Journal 2021 Journal Article

2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study

  • Lingwei Meng
  • Di Dong
  • Xin Chen
  • Mengjie Fang
  • Rongpin Wang
  • Jing Li
  • Zaiyi Liu
  • Jie Tian

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features’ representation and discrimination capacity regarding GC, via three tasks ( ${\boldsymbol{T}^{\boldsymbol{LNM}}}$, lymph node metastasis’ prediction; ${\boldsymbol{T}^{\boldsymbol{LVI}}}$, lymphovascular invasion's prediction; ${\boldsymbol{T}^{\boldsymbol{pT}}}$, pT4 or other pT stages’ classification). Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models ( $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LNM}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LNM}}$; $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LVI}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LVI}}$; $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{pT}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{pT}}$ ) were derived and evaluated to reflect modalities’ performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities’ performances when resampling spacing different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LNM}}$ 's 0. 712 (95% confidence interval, 0. 613–0. 811), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LNM}}$ 's 0. 680 (0. 584–0. 775); $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LVI}}$ 's 0. 677 (0. 595–0. 761), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LVI}}$ 's 0. 615 (0. 528-0. 703); $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{pT}}$ 's 0. 840 (0. 779–0. 901), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{pT}}$ 's 0. 813 (0. 747–0. 879). Moreover, the auxiliary experiment indicated that $\boldsymbol{Model}{\boldsymbol{s}_{2\boldsymbol{D}}}$ are statistically advantageous than $\boldsymbol{Model}{\boldsymbol{s}_{3\boldsymbol{D}}}$ with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.

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JBHI Journal 2021 Journal Article

Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis

  • Yamin Li
  • Wen Shi
  • Zhian Liu
  • Jing Li
  • Qiang Wang
  • Xiangguo Yan
  • Zehong Cao
  • Gang Wang

Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

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TCS Journal 2021 Journal Article

Hybrid fault-tolerant prescribed hyper-hamiltonian laceability of hypercubes

  • Yuxing Yang
  • Jing Li

The n-dimensional hypercube Q n is one of the most attractive interconnection networks for multiprocessor systems and it is a bipartite graph. Let F v be a set of the end-nodes of k independent edges in Q n and F e be a set of f edges in Q n − F v. Given a linear forest L of Q n − F v − F e, in this paper, we prove that (i) Q n − F v − F e admits a hamiltonian cycle passing through L if | E ( L ) | + k + f ≤ n − 2; and ( i i ) for any two nodes x and y of the opposite partite sets in Q n − F v − F e such that none of the paths in L has x or y as internal node or both of them as end-nodes, Q n − F v − F e admits a hamiltonian path between x and y passing through L if | E ( L ) | + k + f ≤ n − 3; and ( i i i ) for any two distinct nodes u and v of the partite set not containing w in Q n − F v − F e − w such that none of the paths in L has u or v as internal node or both of them as end-nodes, Q n − F v − F e − w admits a hamiltonian path between u and v passing through L if | E ( L ) | + k + f ≤ n − 3, where w is an arbitrary node in Q n − F v − F e.

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YNIMG Journal 2021 Journal Article

Reliability of EEG microstate analysis at different electrode densities during propofol-induced transitions of brain states

  • Kexu Zhang
  • Wen Shi
  • Chang Wang
  • Yamin Li
  • Zhian Liu
  • Tun Liu
  • Jing Li
  • Xiangguo Yan

Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes. In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation. First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition. The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes. The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.

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AAAI Conference 2020 Conference Paper

A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

  • Zhaohui Che
  • Ali Borji
  • Guangtao Zhai
  • Suiyi Ling
  • Jing Li
  • Patrick Le Callet

Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of pre-trained source models can transfer to other new target models, thus pose a security threat to blackbox applications (when the attackers have no access to the target models). Despite adopting diverse architectures and parameters, source and target models often share similar decision boundaries. Therefore, if an adversary is capable of fooling several source models concurrently, it can potentially capture intrinsic transferable adversarial information that may allow it to fool a broad class of other black-box target models. Current ensemble attacks, however, only consider a limited number of source models to craft an adversary, and obtain poor transferability. In this paper, we propose a novel black-box attack, dubbed Serial-Mini-Batch- Ensemble-Attack (SMBEA). SMBEA divides a large number of pre-trained source models into several mini-batches. For each single batch, we design 3 new ensemble strategies to improve the intra-batch transferability. Besides, we propose a new algorithm that recursively accumulates the “long-term” gradient memories of the previous batch to the following batch. This way, the learned adversarial information can be preserved and the inter-batch transferability can be improved. Experiments indicate that our method outperforms state-ofthe-art ensemble attacks over multiple pixel-to-pixel vision tasks including image translation and salient region prediction. Our method successfully fools two online black-box saliency prediction systems including DeepGaze-II (Kummerer 2017) and SALICON (Huang et al. 2017). Finally, we also contribute a new repository to promote the research on adversarial attack and defense over pixel-to-pixel tasks: https: //github. com/CZHQuality/AAA-Pix2pix.

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YNICL Journal 2020 Journal Article

Aberrant inter-hemispheric coordination characterizes the progression of minimal hepatic encephalopathy in patients with HBV-related cirrhosis

  • Min Ye
  • Zheng Guo
  • Zhipeng Li
  • Xiaoshan Lin
  • Jing Li
  • Guihua Jiang
  • Yun Teng
  • Yingwei Qiu

Patients with hepatitis B virus (HBV)-related cirrhosis (HBV-RC) and minimal hepatic encephalopathy (MHE) exhibit alterations in homotopic inter-hemispheric functional connectivity (FC) and corpus callosum (CC) degeneration. However, the progression of inter-hemispheric dysconnectivity in cirrhotic patients from no MHE (NMHE) to MHE and its association with the progression of diseased-related cognitive impairment remain uncharacterized. We hypothesized that inter-hemispheric dysconnectivity exists in NMHE patients and further deteriorates at the MHE stage, which is associated with performance measured by psychometric hepatic encephalopathy scores (PHES) that can characterize cirrhotic patients with NMHE and MHE. Using inter-hemispheric homotopic FC and CC (and its subfields) volumetric measurements in 31 patients with HBV-RC (17 with NMHE and 14 with MHE) and 37 healthy controls, we verified that MHE patients had significant attenuated inter-hemispheric homotopic FC in the bilateral cuneus, post-central gyrus, inferior parietal lobule, and superior temporal gyms, as well as CC degeneration in total CC, CC2, CC3, and CC4 (each comparison had a corrected P < 0.05). In contrast, NMHE patients had relatively less severe inter-hemispheric homotopic FC and no CC degeneration. In addition, the degeneration of the CC and inter-hemispheric homotopic functional disconnections correlated with poor PHES performances in all cirrhotic patients (NMHE and MHE). Furthermore, impairment of inter-hemispheric homotopic FC partially mediated the association between CC degeneration and worse PHES performance. Notably, a combination of inter-hemispheric homotopic FC and CC volumes had higher discriminative values according to the area under the curve (AUC) score (AUC = 0.908, P < 0.001) to classify patients into MHE or NMHE groups when compared with either alone. Our findings shed light on the progression of inter-hemispheric dysconnectivity in relation to the progression of disease-related cognitive impairment in patients with HBV-RC.

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IJCAI Conference 2020 Conference Paper

Contextualized Point-of-Interest Recommendation

  • Peng Han
  • Zhongxiao Li
  • Yong Liu
  • Peilin Zhao
  • Jing Li
  • Hao Wang
  • Shuo Shang

Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i. e. , global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning

  • Xiang Ni
  • Jing Li
  • Mo Yu
  • Wang Zhou
  • Kun-Lung Wu

This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize system throughput, the resource allocation strategy that partitions the computation tasks of a stream processing graph onto computing devices must simultaneously balance workload distribution and minimize communication. Since this problem of graph partitioning is known to be NP-complete yet crucial to practical streaming systems, many heuristic-based algorithms have been developed to find reasonably good solutions. In this paper, we present a graph-aware encoderdecoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data. We, for the first time, propose to leverage graph embedding to learn the structural information of the stream processing graphs. Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTMbased encoder-decoder model, in about 70% of the test cases.

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ICLR Conference 2020 Conference Paper

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

  • Yang You 0001
  • Jing Li
  • Sashank J. Reddi
  • Jonathan Hseu
  • Sanjiv Kumar
  • Srinadh Bhojanapalli
  • Xiaodan Song
  • James Demmel

Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes.

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IJCAI Conference 2020 Conference Paper

Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network

  • Huiyuan Chen
  • Jing Li

Precise medicine recommendations provide more effective treatments and cause fewer drug side effects. A key step is to understand the mechanistic relationships among drugs, targets, and diseases. Tensor-based models have the ability to explore relationships of drug-target-disease based on large amount of labeled data. However, existing tensor models fail to capture complex nonlinear dependencies among tensor data. In addition, rich medical knowledge are far less studied, which may lead to unsatisfied results. Here we propose a Neural Tensor Network (NeurTN) to assist personalized medicine treatments. NeurTN seamlessly combines tensor algebra and deep neural networks, which offers a more powerful way to capture the nonlinear relationships among drugs, targets, and diseases. To leverage medical knowledge, we augment NeurTN with geometric neural networks to capture the structural information of both drugs’ chemical structures and targets’ sequences. Extensive experiments on real-world datasets demonstrate the effectiveness of the NeurTN model.

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IJCAI Conference 2020 Conference Paper

Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems

  • Huiyuan Chen
  • Jing Li

Recommender systems often involve multi-aspect factors. For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. However, existing models assume multilinear structures in the tensor data, thus failing to capture nonlinear feature interactions. To fill this gap, we propose a novel nonlinear tensor machine, which combines deep neural networks and tensor algebra to capture nonlinear interactions among multi-aspect factors. We further consider adversarial learning to assist the training of our model. Extensive experiments demonstrate the effectiveness of the proposed model.

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AAAI Conference 2020 Conference Paper

Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

  • Lisi Chen
  • Shuo Shang
  • Bin Yao
  • Jing Li

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers’ trips in intelligent transportation platforms (e. g. , DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between n travelers’ trips and the potential travel routes (each trip has k potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efficient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching ef- ficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.

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AAAI Conference 2020 Conference Paper

Pose-Assisted Multi-Camera Collaboration for Active Object Tracking

  • Jing Li
  • Jing Xu
  • Fangwei Zhong
  • Xiangyu Kong
  • Yu Qiao
  • Yizhou Wang

Active Object Tracking (AOT) is crucial to many visionbased applications, e. g. , mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e. g. , target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose- Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for active object tracking. In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images. The pose-based controller moves the camera in accordance to the poses of the other cameras. At each step, the switcher decides which action to take from the two controllers according to the visibility of the target. The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. The code and demo videos are available on our website https: //sites. google. com/view/pose-assistedcollaboration.

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IJCAI Conference 2019 Conference Paper

Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks

  • Jing Li
  • Deheng Ye
  • Shuo Shang

In this paper, we focus on named entity boundary detection, which aims to detect the start and end boundaries of an entity mention in text, without predicting its type. A more accurate and robust detection approach is desired to alleviate error propagation in downstream applications, such as entity linking and fine-grained typing systems. Here, we first develop a novel entity boundary labeling approach with pointer networks, where the output dictionary size depends on the input, which is variable. Furthermore, we propose AT-Bdry, which incorporates adversarial transfer learning into an end-to-end sequence labeling model to encourage domain-invariant representations. More importantly, AT-Bdry can reduce domain difference in data distributions between the source and target domains, via an unsupervised transfer learning approach (i. e. , no annotated target-domain data is necessary). We conduct Formal Text to Formal Text, Formal Text to Informal Text and ablation evaluations on five benchmark datasets. Experimental results show that AT-Bdry achieves state-of-the-art transferring performance against recent baselines.

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TCS Journal 2019 Journal Article

Embedding fault-free hamiltonian paths with prescribed linear forests into faulty ternary n-cubes

  • Yuxing Yang
  • Jing Li
  • Shiying Wang

The k-ary n-cube is an important underlying topology for large-scale multiprocessor systems. A linear forest in a graph is a subgraph each component of which is a path. In this paper, we investigate the problem of embedding hamiltonian paths passing through a prescribed linear forest in ternary n-cubes with faulty edges. Given a faulty edge set F with at most 2 n − 3 edges and a linear forest L with at most 2 n − 3 − | F | edges, for two distinct vertices in the ternary n-cube, we show that the ternary n-cube admits a fault-free hamiltonian path between u and v passing through L if and only if none of the paths in L has u or v as internal vertices or both of them as end-vertices.

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IJCAI Conference 2019 Conference Paper

Neural Discourse Segmentation

  • Jing Li

Identifying discourse structures and coherence relations in a piece of text is a fundamental task in natural language processing. The first step of this process is segmenting sentences into clause-like units called elementary discourse units (EDUs). Traditional solutions to discourse segmentation heavily rely on carefully designed features. In this demonstration, we present SegBot, a system to split a given piece of text into sequence of EDUs by using an end-to-end neural segmentation model. Our model does not require hand-crafted features or external knowledge except word embeddings, yet it outperforms state-of-the-art solutions to discourse segmentation.

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AAAI Conference 2019 Conference Paper

Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

  • Dongliang Liao
  • Jin Xu
  • Gongfu Li
  • Weijie Huang
  • Weiqing Liu
  • Jing Li

Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, which are unpredictable and hard to capture. (2) Content and meta-data features, largely determining the online content popularity, are usually multi-modal and nontrivial to model. (3) Besides, it also needs to figure out how to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles. In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them. For modeling the temporal popularity process, we adopt the recurrent neural network and convolutional neural network. For multi-modal content features, we exploit the hierarchical attention network and embedding technique. Finally, a temporal attention fusion is employed for dynamically integrating all these parts. Using datasets collected from WeChat, we show that the proposed model significantly outperforms stateof-the-art approaches on popularity prediction.

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AIIM Journal 2019 Journal Article

Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network

  • Yi-Peng Liu
  • Zhanqing Li
  • Cong Xu
  • Jing Li
  • Ronghua Liang

Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automated image assessment system, which can benefit from the development of deep learning techniques. Therefore, the effective classification performance is significant in favor of the referable DR identification task. In this paper, we propose a new strategy, which applies multiple weighted paths into convolutional neural network, called the WP-CNN, motivated by the ensemble learning. In WP-CNN, multiple path weight coefficients are optimized by back propagation, and the output features are averaged for redundancy reduction and fast convergence. The experiment results show that with the efficient training convergence rate WP-CNN achieves an accuracy of 94. 23% with sensitivity of 90. 94%, specificity of 95. 74%, an area under the receiver operating curve of 0. 9823 and F1-score of 0. 9087. By taking full advantage of the multipath mechanism, the proposed WP-CNN is shown to be accurate and effective for referable DR identification compared to the state-of-art algorithms.

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AAAI Conference 2019 Conference Paper

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation

  • Pengjie Ren
  • Zhumin Chen
  • Jing Li
  • Zhaochun Ren
  • Jun Ma
  • Maarten de Rijke

Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e. g. , e-commerce, music, and TV program recommendations), where the same item is re-consumed repeatedly over time. However, no previous studies have emphasized repeat consumption with neural networks. An effective neural approach is needed to decide when to perform repeat recommendation. In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user’s history and recommends them at the right time. We report on extensive experiments on three benchmark datasets. RepeatNet outperforms state-of-the-art baselines on all three datasets in terms of MRR and Recall. Furthermore, as the dataset size and the repeat ratio increase, the improvements of RepeatNet over the baselines also increase, which demonstrates its advantage in handling repeat recommendation scenarios.

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JBHI Journal 2018 Journal Article

Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting

  • Jing Li
  • Yi Wang
  • Baiying Lei
  • Jie-Zhi Cheng
  • Jing Qin
  • Tianfu Wang
  • Shengli Li
  • Dong Ni

Head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, the manual measurement of this biometric by doctors often requires substantial experience. We developed a learning-based framework that used prior knowledge and employed a fast ellipse fitting method (ElliFit) to measure HC automatically. We first integrated the prior knowledge about the gestational age and ultrasound scanning depth into a random forest classifier to localize the fetal head. We further used phase symmetry to detect the center line of the fetal skull and employed ElliFit to fit the HC ellipse for measurement. The experimental results from 145 HC images showed that our method had an average measurement error of 1. 7 mm and outperformed traditional methods. The experimental results demonstrated that our method shows great promise for applications in clinical practice.

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NeurIPS Conference 2018 Conference Paper

Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation

  • Jing Li
  • Rafal Mantiuk
  • Junle Wang
  • Suiyi Ling
  • Patrick Le Callet

In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labeling. Our method employs Bayesian optimization framework and Bradley-Terry model to construct the utility function, then to obtain the Expected Information Gain (EIG) of each pair. For computational efficiency, Gaussian-Hermite quadrature is used for estimation of EIG. In this work, a hybrid active sampling strategy is proposed, either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST) sampling in each trial, which is determined by the test budget. The proposed method has been validated on both simulated and real-world datasets, where it shows higher preference aggregation ability than the state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Joint Learning Embeddings for Chinese Words and their Components via Ladder Structured Networks

  • Yan Song
  • Shuming Shi
  • Jing Li

The components, such as characters and radicals, of a Chinese word are important sources to help in capturing semantic information of the word. In this paper, we propose a novel framework, namely, ladder structured networks (LSN), which contains three layers representing word, character and radical and learns their embeddings synchronously. LSN captures not only the relations among words, but also the relations among their component characters and radicals, as well as the relations across layers. Each layer in LSN is pluggable so that any particular type of unit (word, character, radical) can be removed and the LSN is thus adjusted for particular types of inputs. In evaluating our framework, we use word similarity as the intrinsic evaluation and part-of-speech tagging and document classification as extrinsic evaluations. Experimental results confirm the validity of our approach and show superiority of our approach over previous work.

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YNICL Journal 2018 Journal Article

Longitudinal brain structural alterations in patients with nasopharyngeal carcinoma early after radiotherapy

  • Zheng Guo
  • Lujun Han
  • Yadi Yang
  • Haoqiang He
  • Jing Li
  • Hong Chen
  • Ting Song
  • Yingwei Qiu

Background and purpose: To investigate effects of radiotherapy on normal brain tissue using in vivo neuroimaging in patients with nasopharyngeal carcinoma (NPC). Methods and materials: < 0.05, which was corrected for multiple comparisons. Results: Volumes of the gray matter, and bilateral temporal lobes decreased in a time-dependent manner, whereas ventricle volume showed a time-dependent increase after radiotherapy. No volume changes were detected in NPC patients before radiotherapy when compared normal controls. No volume changes were detected in the subcohort of patients after completion of induction chemotherapy but prior to initiation of radiotherapy. Changes of bilateral temporal lobe volume correlated with irradiation dose in this region. Expansion of the ventricles correlated with a reduction in cognition assessment. Conclusions: We detected significant and progressive radiotherapy-associated structural changes in the brains of patients with NPC who were treated with standard radiotherapy, especially in the bilateral temporal lobe in which the effect was dose-dependent. Expansion of the ventricles can serve as an imaging marker for treatment-related reduction in cognitive function. Future studies with longer follow-ups are needed to evaluate morphometric changes long-term after radiotherapy.

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IJCAI Conference 2018 Conference Paper

Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network

  • Dongliang Liao
  • Weiqing Liu
  • Yuan Zhong
  • Jing Li
  • Guowei Wang

Predicting users’ activity and location preferences is of great significance in location based services. Considering that users’ activity and location preferences interplay with each other, many scholars tried to figure out the relation between users’ activities and locations for improving prediction performance. However, most previous works enforce a rigid human-defined modeling strategy to capture these two factors, either activity purpose controlling location preference or spatial region determining activity preference. Unlike existing methods, we introduce spatial-activity topics as the latent factor capturing both users’ activity and location preferences. We propose Multi-task Context Aware Recurrent Neural Network to leverage the spatial activity topic for activity and location prediction. More specifically, a novel Context Aware Recurrent Unit is designed to integrate the sequential dependency and temporal regularity of spatial activity topics. Extensive experimental results based on real-world public datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.

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IJCAI Conference 2018 Conference Paper

SegBot: A Generic Neural Text Segmentation Model with Pointer Network

  • Jing Li
  • Aixin Sun
  • Shafiq Joty

Text segmentation is a fundamental task in natural language processing that comes in two levels of granularity: (i) segmenting a document into a sequence of topical segments (topic segmentation), and (ii) segmenting a sentence into a sequence of elementary discourse units (EDU segmentation). Traditional solutions to the two tasks heavily rely on carefully designed features. The recently proposed neural models do not need manual feature engineering, but they either suffer from sparse boundary tags or they cannot well handle the issue of variable size output vocabulary. We propose a generic end-to-end segmentation model called SegBot. SegBot uses a bidirectional recurrent neural network to encode input text sequence. The model then uses another recurrent neural network together with a pointer network to select text boundaries in the input sequence. In this way, SegBot does not require hand-crafted features. More importantly, our model inherently handles the issue of variable size output vocabulary and the issue of sparse boundary tags. In our experiments, SegBot outperforms state-of-the-art models on both topic and EDU segmentation tasks.

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YNICL Journal 2017 Journal Article

Anatomical brain difference of subthreshold depression in young and middle-aged individuals

  • Jing Li
  • Zengjian Wang
  • JiWon Hwang
  • Bingcong Zhao
  • Xinjing Yang
  • Suicheng Xin
  • Yu Wang
  • Huili Jiang

BACKGROUND: Subthreshold depression (StD) is associated with substantial functional impairments due to depressive symptoms that do not fully meet the diagnosis of major depressive disorder (MDD). Its high incidence in the general population and debilitating symptoms has recently put it at the forefront of mood disorder research. AIM: In this study we investigated common volumetric brain changes in both young and middle-aged StD patients. METHODS: = 76) underwent voxel-based morphometry (VBM). RESULTS: VBM analysis found that: 1) compared with healthy controls, StD patients showed decreased gray matter volume (GMV) in the bilateral globus pallidus and precentral gyrus, as well as increased GMV in the left thalamus and right rostral anterior cingulate cortex/medial prefrontal cortex; 2) there is a significant association between Center for Epidemiological Studies Depression Scale scores and the bilateral globus pallidus (negative) and left thalamus (positive); 3) there is no interaction between age (young vs. middle-age) and group (StD vs. controls). CONCLUSIONS: Our findings indicate significant VBM brain changes in both young and middle-aged individuals with StD. Individuals with StD, regardless of age, may share common neural characteristics.

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AIIM Journal 2017 Journal Article

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

  • Changjian Sun
  • Shuxu Guo
  • Huimao Zhang
  • Jing Li
  • Meimei Chen
  • Shuzhi Ma
  • Lanyi Jin
  • Xiaoming Liu

This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD. In the case of 3Dircadb, using the FCN, the mean ratios of the volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSSD) were 15. 6±4. 3%, 5. 8±3. 5%, 2. 0±0. 9%, 2. 9±1. 5mm, 7. 1±6. 2mm, respectively. For JDRD, using the MC-FCN, the mean ratios of VOE, RVD, ASD, RMSD, and MSSD were 8. 1±4. 5%, 1. 7±1. 0%, 1. 5±0. 7%, 2. 0±1. 2mm, 5. 2±6. 4mm, respectively. The test results demonstrate that the MC-FCN model provides greater accuracy and robustness than previous methods.

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TCS Journal 2017 Journal Article

On g-extra conditional diagnosability of hypercubes and folded hypercubes

  • Aixia Liu
  • Shiying Wang
  • Jun Yuan
  • Jing Li

Diagnosability of a multiprocessor system is one important study topic, which plays an important role in measuring of the reliability of multiprocessor systems. In the work of Zhang et al. in 2016, they proposed a new measure for fault diagnosis of systems, namely, g-extra conditional diagnosability. It is defined as the diagnosability of a multiprocessor system under the assumption that every fault-free component contains more than g vertices, which can measure the reliability of interconnection networks in heterogeneous environments more accurately than traditional diagnosability. As two kind of favorable topology structures of interconnection networks, the n-dimensional hypercubes Q n and folded hypercubes F Q n have many good properties. In this paper, we investigate their g-extra conditional diagnosability and show that (a) the g-extra conditional diagnosability of Q n is ( g + 1 ) n − g − C g 2 for n ≥ 5 and 1 ≤ g ≤ n − 1 4 under the MM* model; (b) the g-extra conditional diagnosability of F Q n is ( g + 1 ) n − C g 2 + 1 for n ≥ 9 and 1 ≤ g ≤ n 4 under the MM* model.

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TCS Journal 2017 Journal Article

Paired 2-disjoint path covers of multi-dimensional torus networks with 2n− 3 faulty edges

  • Jing Li
  • Guoren Wang
  • Lichao Chen

The n-dimensional torus T ( k 1, k 2, …, k n ) (including the k-ary n-cube Q n k ) is one of the most popular interconnection networks. A paired k-disjoint path cover (paired k-DPC for short) of a graph is a set of k disjoint paths joining k distinct source-sink pairs that cover all vertices of the graph. In this paper, we consider the paired 2-DPC problem of n-dimensional torus. Assuming k i ≥ 3 for i = 1, 2, …, n, with at most one k i being even, then T ( k 1, k 2, …, k n ) with at most 2 n − 3 faulty edges always has a paired 2-DPC. And the upper bound 2 n − 3 of edge faults tolerated is optimal. The result is a supplement of the results of Chen [3] and [4].

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IJCAI Conference 2017 Conference Paper

Self-weighted Multiview Clustering with Multiple Graphs

  • Feiping Nie
  • Jing Li
  • Xuelong Li

In multiview learning, it is essential to assign a reasonable weight to each view according to its importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of it, we further propose a new multiview clustering method which is totally self-weighted. Furthermore, once the target graph is obtained in our models, we can directly assign the cluster label to each data point and do not need any postprocessing such as $K$-means in standard spectral clustering. Evaluations on two synthetic datasets prove the effectiveness of our methods. Compared with several representative graph-based multiview clustering approaches on four real-world datasets, experimental results demonstrate that the proposed methods achieve the better performances and our new clustering method is more practical to use.

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TCS Journal 2016 Journal Article

g-Good-neighbor conditional diagnosability measures for 3-ary n-cube networks

  • Jun Yuan
  • Aixia Liu
  • Xiao Qin
  • Jifu Zhang
  • Jing Li

The diagnosability of a parallel system is defined as the maximum number of faulty processors or nodes that the system can guarantee to identify. In this study, we investigate the g-good-neighbor conditional diagnosability, which indicates that every fault-free node in a system contains at least g fault-free neighbors. Compared with the conventional diagnosability, g-good-neighbor conditional diagnosability improves accuracy in measuring the reliability of interconnection networks in heterogeneous environments. We apply the PMC and MM* models to study the g-good-neighbor conditional diagnosability of 3-ary n-cube networks, which represent a family of popular parallel systems such as IBM's Blue Gene and Cray T3D. The findings made in this study facilitate accurate reliability measurements in modern parallel systems powered by 3-ary n-cube networks. Specifically, our results show that the g-good-neighbor conditional diagnosability of 3-ary n-cube is g 2 ( 2 n − g + 1 ) − 1 and g − 1 2 ( 4 n − 2 g + 1 ) − 1 when the g value is even and odd, respectively.

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IJCAI Conference 2016 Conference Paper

Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification

  • Feiping Nie
  • Jing Li
  • Xuelong Li

Graph-based approaches have been successful in unsupervised and semi-supervised learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. In this paper, we propose a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semi-supervised tasks. Unlike other methods in the literature, the proposed method can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. Furthermore, our objective under semi-supervised learning is convex and the global optimal result will be obtained. Extensive empirical results on different real-world data sets demonstrate that the proposed method achieves comparable performance with the state-of-the-art approaches and can be used more practically.

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YNIMG Journal 2015 Journal Article

Imaging with referenceless distortion correction and flexible regions of interest using single-shot biaxial spatiotemporally encoded MRI

  • Jing Li
  • Lin Chen
  • Shuhui Cai
  • Congbo Cai
  • Jianhui Zhong
  • Zhong Chen

Owing to its intrinsic characteristics, spatiotemporally encoded (SPEN) imaging is less sensitive to adverse effects due to field inhomogeneity in comparison with echo planar imaging, a feature highly desired for functional, diffusion, and real-time MRI. However, the quality of images obtained with SPEN MRI is still degraded by geometric distortions when field inhomogeneity exists. In this study, a single-shot biaxial SPEN (bi-SPEN) pulse sequence is implemented, utilizing a 90° and a 180° chirp pulse incorporated with two orthogonal gradients. A referenceless geometric-distortion correction based on the single-shot bi-SPEN sequence is then proposed. The distorted image acquired with the single-shot bi-SPEN sequence is corrected by iterative super-resolved reconstruction involving the field gradients estimated from a field map, which in turn is obtained from its own super-resolved data after a phase-unwrapping procedure without additional scans. In addition, the distortion correction method is applied to improve the quality of the multiple region-of-interest images obtained with single-shot bi-SPEN sequence.

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ICRA Conference 2014 Conference Paper

Comparative study of robot kinematic calibration algorithms using a unified geometric framework

  • Yuanqing Wu 0001
  • Cheng Li 0002
  • Jing Li
  • Zexiang Li 0001

In this paper, we conduct a comparative study of three well known robot kinematic calibration algorithms, namely the Denavit-Hartenberg (DH) parameter algorithm, the product of exponentials (POE) algorithm, and the local POE (LPOE) algorithm. To cope with distinct formulations associated to different algorithms, we propose a unified geometric framework which is based on POE kinematics and a novel Adjoint error model. The Adjoint error model offers us an extremely efficient way to benchmark the aforesaid calibration algorithms, and also compare them to a novel calibration algorithm based on the Adjoint error model.

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IS Journal 2014 Journal Article

Healthcare Intelligence: Turning Data into Knowledge

  • Hui Yang
  • Erhun Kundakcioglu
  • Jing Li
  • Teresa Wu
  • J. Ross Mitchell
  • Amy K. Hara
  • William Pavlicek
  • Leland S. Hu

Exceptional opportunities exist for researchers and practitioners to invest in conducting innovative and transformative research in data mining and health informatics. This IEEE Intelligent Systems "Trends and Controversies" (T&C) department hopes to raise awareness and highlight recent research to move toward such goals. The introduction, "Healthcare Intelligence: Turning Data into Knowledge, " is written by Hui Yang and Erhun Kundakcioglu. Next, "Empowering Excellence of Care by Radiology Informatics" is written by Jing Li, Teresa Wu, J. Ross Mitchell, Amy K. Hara, William Pavlicek, Leland S. Hu, Alvin C. Silva, and Christine M. Zwart. Third, "Opportunities for Operations Research in Medical Decision Making" is written by Sait Tunc, Oguzhan Alagoz, and Elizabeth Burnside. Fourth, "Diagnostic Network Modeling of Neural Connectivity Using Functional Magnetic Resonance Imaging" is written by W. Art Chaovalitwongse, Georgiy Presnyakov, Yulian Cao, Sirirat Sujitnapitsatham, Daehan Won, Tara Madhyastha, Kurt E. Weaver, Paul R. Borghesani, and Thomas J. Grabowski. The final article, "Spatial Clustering in Public Health: Advances and Challenges, " is written by Lianjie Shu, Man Ho Ling, Shui-Yee Wong, and Kwok-Leung Tsui.

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I&C Journal 2013 Journal Article

Fault-tolerant embedding of cycles of various lengths in k-ary n-cubes

  • Shiying Wang
  • Jing Li
  • Shangwei Lin
  • Ruixia Wang

The k-ary n-cube has been one of the most popular interconnection networks for distributed-memory parallel systems. In this paper, we study the problem of embedding cycles of various lengths into faulty k-ary n-cubes. It is proved that a faulty k-ary n-cube with f v faulty vertices and f e faulty edges admits a fault-free cycle of every even length from 4 to k n − 2 f v if k ⩾ 4 is even and f v + f e ⩽ 2 n − 2. Furthermore, we show that every healthy edge of the faulty k-ary n-cube lies on a fault-free cycle of every even length from 4 to k n − 2 f v if k ⩾ 4 is even and f v + f e ⩽ 2 n − 3. The results are optimal with respect to the number of vertex and edge faults tolerated.

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NeurIPS Conference 2011 Conference Paper

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

  • Shuai Huang
  • Jing Li
  • Jieping Ye
  • Teresa Wu
  • Kewei Chen
  • Adam Fleisher
  • Eric Reiman

Diagnosis of Alzheimer's disease (AD) at the early stage of the disease development is of great clinical importance. Current clinical assessment that relies primarily on cognitive measures proves low sensitivity and specificity. The fast growing neuroimaging techniques hold great promise. Research so far has focused on single neuroimaging modalities. However, as different modalities provide complementary measures for the same disease pathology, fusion of multi-modality data may increase the statistical power in identification of disease-related brain regions. This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone. We propose a sparse composite linear discriminant analysis model (SCLDA) for identification of disease-related brain regions of early AD from multi-modality data. SCLDA uses a novel formulation that decomposes each LDA parameter into a product of a common parameter shared by all the modalities and a parameter specific to each modality, which enables joint analysis of all the modalities and borrowing strength from one another. We prove that this formulation is equivalent to a penalized likelihood with non-convex regularization, which can be solved by the DC ((difference of convex functions) programming. We show that in using the DC programming, the property of the non-convex regularization in terms of preserving weak-effect features can be nicely revealed. We perform extensive simulations to show that SCLDA outperforms existing competing algorithms on feature selection, especially on the ability for identifying weak-effect features. We apply SCLDA to the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images of 49 AD patients and 67 normal controls (NC). Our study identifies disease-related brain regions consistent with findings in the AD literature.

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YNIMG Journal 2010 Journal Article

Gender consistency and difference in healthy adults revealed by cortical thickness

  • Bin Lv
  • Jing Li
  • Huiguang He
  • Meng Li
  • Mingchang Zhao
  • Likun Ai
  • Fei Yan
  • Junfang Xian

Many previous studies have shown that there exists the gender effect on the structural and functional organization in the human brain. Although the reported functional differences are generally consistent, the structural differences are controversial among the various studies. In this study, we particularly focused on the gender-related effect in the gray matter (GM). We performed a structural magnetic resonance imaging (MRI) study in 184 healthy adults (90 males and 94 females) with ages ranging from 18 to 70years. Cortical thickness was measured using an automated surface-based method. Based on this surface morphological feature of GM, we first compared their regional differences between males and females. We then constructed the morphometry-based anatomical networks derived from cortical thickness measurement, while the anatomical connection between two cortical areas depended upon the statistical dependence of their cortical thickness across subjects. Subsequently, we applied graph theoretical approaches to investigate the properties of the resultant anatomical networks. The results showed that the significant gender-related differences of cortical thickness appeared extensively in the frontal, parietal and occipital lobes. And there were also some between-group differences in the interregional correlation. Additional graph theoretical analysis on the morphological networks revealed both networks exhibited the small-world efficiency and their patterns of topological vulnerability had no statistical differences. The findings on the large sample may provide the evidences to study the gender consistency and difference in the human brain structures.

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YNIMG Journal 2010 Journal Article

Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation

  • Shuai Huang
  • Jing Li
  • Liang Sun
  • Jieping Ye
  • Adam Fleisher
  • Teresa Wu
  • Kewei Chen
  • Eric Reiman

Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer's disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i. e. , the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data. Our method is able to identify both the connectivity network structure and strength for a large number of brain regions with small sample sizes. We apply the proposed method to the PET data of AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Compared with NC, AD shows decrease in the amount of inter-region functional connectivity within the temporal lobe especially between the area around hippocampus and other regions and increase in the amount of connectivity within the frontal lobe as well as between the parietal and occipital lobes. Also, AD shows weaker between-lobe connectivity than within-lobe connectivity and weaker between-hemisphere connectivity, compared with NC. In addition to being a method for knowledge discovery about AD, the proposed SICE method can also be used for classifying new subjects, which makes it a suitable approach for novel connectivity-based AD biomarker identification. Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively.

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NeurIPS Conference 2009 Conference Paper

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

  • Shuai Huang
  • Jing Li
  • Liang Sun
  • Jun Liu
  • Teresa Wu
  • Kewei Chen
  • Adam Fleisher
  • Eric Reiman

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia. Previous studies have shown that AD is closely related to alternation in the functional brain network, i. e. , the functional connectivity among different brain regions. In this paper, we consider the problem of learning functional brain connectivity from neuroimaging, which holds great promise for identifying image-based markers used to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. More specifically, we study sparse inverse covariance estimation (SICE), also known as exploratory Gaussian graphical models, for brain connectivity modeling. In particular, we apply SICE to learn and analyze functional brain connectivity patterns from different subject groups, based on a key property of SICE, called the “monotone property” we established in this paper. Our experimental results on neuroimaging PET data of 42 AD, 116 MCI, and 67 NC subjects reveal several interesting connectivity patterns consistent with literature findings, and also some new patterns that can help the knowledge discovery of AD.

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