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Yifan Chen

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23 papers

2 author rows

TMLR Journal 2026 Journal Article

DiffKGW: Stealthy and Robust Diffusion Model Watermarking

Tianxin Wei
Ruizhong Qiu
Yifan Chen
Yunzhe Qi
Jiacheng Lin
Wenxuan Bao
Wenju Xu
Sreyashi Nag

Diffusion models are known for their supreme capability to generate realistic images. However, ethical concerns, such as copyright protection and the generation of inappropriate content, pose significant challenges for the practical deployment of diffusion models. Recent work has proposed a flurry of watermarking techniques that inject artificial patterns into initial latent representations of diffusion models, offering a promising solution to these issues. However, enforcing a specific pattern on selected elements can disrupt the Gaussian distribution of the initial latent representation. Inspired by watermarks for large language models (LLMs), we generalize the LLM KGW watermark to image diffusion models and propose a stealthy probability adjustment approach DiffKGW that preserves the Gaussian distribution of initial latent representation. In addition, we dissect the design principles of state-of-the-art watermarking techniques and introduce a unified framework. We identify a set of dimensions that explain the manipulation enforced by watermarking methods, including the distribution of individual elements, the specification of watermark shapes within each channel, and the choice of channels for watermark embedding. Through the empirical studies on regular text-to-image applications and the first systematic attempt at watermarking image-to-image diffusion models, we thoroughly verify the effectiveness of our proposed framework through comprehensive evaluations. On all the diffusion models, including Stable Diffusion, our approach induced from the proposed framework not only preserves image quality but also outperforms existing methods in robustness against a wide range of attacks.

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EAAI Journal 2026 Journal Article

Prediction of rate of penetration in oil and gas well drilling: A hybrid data-driven approach with multi-stage data preprocessing

Yifan Chen
Pengju Chen
Xianwei Dai
Jing Gong
Kunbin Lu

Rate of Penetration (ROP) is an important measure of oil and gas well drilling efficiency. An accurate prediction on ROP is essential for optimizing drilling operations, achieving drilling automations and reducing drilling costs. However, real-time drilling data often suffers from outliers, high-frequency noise, and irregular sampling, which pose significant challenges to reliable ROP prediction modeling. To address these issues, this study proposes a Hybrid Data-Driven method for ROP prediction that integrates multi-stage data preprocessing framework with ensemble learning and deep neural networks. The data preprocessing framework combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering for outlier removal, Hermite interpolation for reconstructing time series continuity, and wavelet transform with adaptive thresholding to effectively denoise the signal and enhance the signal-to-noise ratio (SNR). The refined data is first fed into a stacked ensemble model, which integrates Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) as base learners, with a Multi-Layer Perceptron (MLP) regressor serving as the meta-learner to aggregate their predictions. The ensemble outputs are then passed to a 1D Convolutional Neural Network (1D-CNN) for local pattern extraction, followed by a Bidirectional Long Short-Term Memory (BiLSTM) network to capture long-term temporal dependencies. Experimental evaluations on real-time drilling datasets demonstrate that the proposed method achieves high prediction accuracy with an R2 of 0. 987. Comparative experiments against single-model baselines demonstrate that the proposed hybrid model achieves the R2 score improvement of approximately 9. 32 %, reflecting its superior predictive performance and ability to capture complex relationships in drilling data.

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AAAI Conference 2026 Conference Paper

When Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF

Yifan Xu
Xichen Ye
Yifan Chen
Qiaosheng Zhang

Quality of datasets plays an important role in large language model (LLM) alignment. In collecting human feedback, however, preference flipping is ubiquitous and causes corruption in data annotation; the issue necessitates the alignment algorithms with improved robustness against potential flipped pairs. To this end, this paper introduces a Flipping-Aware Direct Preference Optimization (FA-DPO) algorithm tailored to preference flipping from a reinforcement learning with human feedback (RLHF) perspective. We dissect the inherent human intention model and the preference flipping mechanism introduced by external factors as two distinct stages; in the latter, we introduce an instance-dependent flipping probability on the basis of the Bradley-Terry (BT) model. Further, by leveraging features relevant to preference annotation, we capture uncertainty in judgments and model preference flipping patterns. In practice, we design a simple yet efficient iterative optimization algorithm compatible with the original RLHF and Direct Preference Optimization (DPO) algorithms. In our experiments, we investigate the instance-dependent preference flipping model under multiple circumstances for evaluation of our proposed method, as well as other baseline methods.

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ICML Conference 2025 Conference Paper

A Recipe for Causal Graph Regression: Confounding Effects Revisited

Yujia Yin
Tianyi Qu
Zihao Wang
Yifan Chen

Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the empirical successes of CGL techniques are mostly exemplified in classification settings, while regression tasks, a more challenging setting in graph learning, are overlooked. We thus devote this work to tackling causal graph regression (CGR); to this end we reshape the processing of confounding effects in existing CGL studies, which mainly deal with classification. Specifically, we reflect on the predictive power of confounders in graph-level regression, and generalize classification-specific causal intervention techniques to regression through a lens of contrastive learning. Extensive experiments on graph OOD benchmarks validate the efficacy of our proposals for CGR. The model implementation and the code are provided on https: //github. com/causal-graph/CGR.

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JBHI Journal 2025 Journal Article

DrugKANs: A Paradigm to Enhance Drug-Target Interaction Prediction With KANs

Xiangzheng Fu
Zhenya Du
Yifan Chen
Haiting Chen
Linlin Zhuo
Aiping Lu
Dongsheng Cao
Xiaojun Yao

Identifyingpotential drug-target interactions (DTIs) is crucial for understanding drug mechanisms, and recent computational methods have yielded promising results in this area. However, these methods face several challenges, including limited model generalization due to heavy reliance on multiple similarity datasets and complex feature extraction, as well as a lack of interpretability by ignoring intrinsic information about drugs and targets. To address these challenges, we propose DrugKANs, a novel DTI prediction model that enhances both the quality and interpretability of DTI representations by integrating a dual-tower architecture with Kolmogorov-Arnold Network (KAN) technology. Our model involves utilizing a pre-trained model to derive initial representations of drugs and targets, and employing a lightweight attention mechanism to capture key features, thereby improving representation quality. We leverage the dual-tower architecture and a lightweight feature interaction mechanism to extract high-level representations separately for drugs and targets, aiming to reduce complex feature interactions and mitigate overfitting. Additionally, we incorporate a contrastive learning strategy within the drug-target bipartite graph to address sparse neighborhood effects and enhance topological information. The inclusion of KAN technology further improves the interpretability of the DTI prediction model. Experimental results on public datasets demonstrate that our model predicts DTIs effectively, underscoring its potential as a valuable tool in drug discovery. This comprehensive methodology presents a balanced approach to overcoming the identified challenges in DTI prediction. Our data and code are available at: https://github.com/Excelsior511/DrugKANs.

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AAAI Conference 2025 Conference Paper

Optimized Gradient Clipping for Noisy Label Learning

Xichen Ye
Yifan Wu
Weizhong Zhang
Xiaoqiang Li
Yifan Chen
Cheng Jin

Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To address this issue, we propose a simple yet effective approach called Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping, estimated by modeling the distributions of clean and noisy samples. This approach allows us to modify the clipping threshold at each training step, effectively controlling the influence of noise gradients. Additionally, we provide statistical analysis to certify the noise-tolerance ability of OGC. Our extensive experiments across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise, demonstrate the effectiveness of our approach.

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NeurIPS Conference 2025 Conference Paper

Split Gibbs Discrete Diffusion Posterior Sampling

Wenda Chu
Zihui Wu
Yifan Chen
Yang Song
Yisong Yue

We study the problem of posterior sampling in discrete-state spaces using discrete diffusion models. While posterior sampling methods for continuous diffusion models have achieved remarkable progress, analogous methods for discrete diffusion models remain challenging. In this work, we introduce a principled plug-and-play discrete diffusion posterior sampling algorithm based on split Gibbs sampling, which we call SGDD. Our algorithm enables reward-guided generation and solving inverse problems in discrete-state spaces. We demonstrate the convergence of SGDD to the target posterior distribution and verify this through controlled experiments on synthetic benchmarks. Our method enjoys state-of-the-art posterior sampling performance on a range of benchmarks for discrete data, including DNA sequence design, discrete image inverse problems, and music infilling, achieving more than 30% improved performance compared to existing baselines.

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NeurIPS Conference 2025 Conference Paper

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Xeron Du
Yifan Yao
Kaijing Ma
Bingli Wang
Tianyu Zheng
Minghao Liu
Yiming Liang
Xiaolong Jin

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e. g. , the reasoning-focused model Gemini-2. 5-Pro achieved the highest accuracy of 63. 56% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

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JBHI Journal 2024 Journal Article

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation

Lei Liu
Huiqi Lu
Maxine Whelan
Yifan Chen
Xiaorong Ding

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20–90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3. 77 mmHg and 2. 52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

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EAAI Journal 2024 Journal Article

Exploiting global and instance-level perceived feature relationship matrices for 3D face reconstruction and dense alignment

Lei Li
Fuqiang Liu
Junyuan Wang
Yanni Wang
Yifan Chen
Xinyu Hu

3D face reconstruction and dense alignment play a key role in digital humans. Learning-based approaches have been developed by employing cascaded convolutional neural networks for feature extraction and spatial-semantic relationship construction. However, these networks often struggle with accurately capturing the complex relationships among various facial components and attributes, particularly in unconstrained environments. Furthermore, the mappings modeled by convolutional weights are at low levels, which are usually implicit and local, and lack global discrimination. In this paper, we propose a global and instance-level perceived relationship matrices-based network (PRMNet) to recover high-fidelity 3D faces and perform accurate dense alignment in unconstrained environments. Specifically, a Key Information Extraction Module (KIEM) extracts crucial features from global and local feature banks. This reduces reasoning costs while maintaining high perceptual quality. Learnable global and instance-level perceived feature relationship matrices are then integrated into the Feature Relationship Reasoning Module (FRRM). This calibration combines key information from both the global macroscopic and sample-specific microscopic views, allowing accurate construction of spatial-semantic relationships to harvest both global discrimination and local relevance. Finally, we introduce a Spatial Reasoning and Guidance Module (SRGM), designed to recalibrate the joint weight responses of feature extraction and its enhancement paths using various attention mechanisms, thereby further enhancing global discrimination ability. Extensive quantitative and qualitative experiments on the benchmark datasets show that our PRMNet outperforms the state-of-the-art. Codes and all resources will be publicly available at https: //github. com/Ray-tju/PRMNet.

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NeurIPS Conference 2024 Conference Paper

Gliding over the Pareto Front with Uniform Designs

Xiaoyuan Zhang
Genghui Li
Xi Lin
Yichi Zhang
Yifan Chen
Qingfu Zhang

Multiobjective optimization (MOO) plays a critical role in various real-world domains. A major challenge therein is generating $K$ uniform Pareto-optimal solutions to represent the entire Pareto front. To address this issue, this paper firstly introduces \emph{fill distance} to evaluate the $K$ design points, which provides a quantitative metric for the representativeness of the design. However, directly specifying the optimal design that minimizes the fill distance is nearly intractable due to the nested $\min-\max-\min$ optimization problem. To address this, we propose a surrogate ``max-packing'' design for the fill distance design, which is easier to optimize and leads to a rate-optimal design with a fill distance at most $4\times$ the minimum value. Extensive experiments on synthetic and real-world benchmarks demonstrate that our proposed paradigm efficiently produces high-quality, representative solutions and outperforms baseline methods.

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NeurIPS Conference 2024 Conference Paper

LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch

Xiaoyuan Zhang
Liang Zhao
Yingying Yu
Xi Lin
Yifan Chen
Han Zhao
Qingfu Zhang

Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective functions into a scalar objective, MOPs aim to optimize for the so-called Pareto optimality or Pareto set learning, which involves optimizing more than one objective function simultaneously, over models with thousands to millions of parameters. Existing benchmark libraries for MOPs mainly focus on evolutionary algorithms, most of which are zeroth-order or meta-heuristic methods that do not effectively utilize higher-order information from objectives and cannot scale to large-scale models with millions of parameters. In light of the above challenges, this paper introduces \algoname, the first multiobjective optimization library that supports state-of-the-art gradient-based methods, provides a fair and comprehensive benchmark, and is open-sourced for the community.

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NeurIPS Conference 2024 Conference Paper

Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Zihui Wu
Yu Sun
Yifan Chen
Bingliang Zhang
Yisong Yue
Katherine L. Bouman

Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on approximations in the generative process to be generic to different inverse problems, leading to inaccurate sample distributions that deviate from the target posterior defined within the Bayesian framework. To harness the generative power of DMs while avoiding such approximations, we propose a Markov chain Monte Carlo algorithm that performs posterior sampling for general inverse problems by reducing it to sampling the posterior of a Gaussian denoising problem. Crucially, we leverage a general DM formulation as a unified interface that allows for rigorously solving the denoising problem with a range of state-of-the-art DMs. We demonstrate the effectiveness of the proposed method on six inverse problems (three linear and three nonlinear), including a real-world black hole imaging problem. Experimental results indicate that our proposed method offers more accurate reconstructions and posterior estimation compared to existing DM-based imaging inverse methods.

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ICML Conference 2024 Conference Paper

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes

Yifan Chen
Mark Goldstein
Mengjian Hua
Michael Samuel Albergo
Nicholas Matthew Boffi
Eric Vanden-Eijnden

We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the framework of stochastic interpolants, which facilitates the construction of a generative model between an arbitrary base distribution and the target. We design a fictitious, non-physical stochastic dynamics that takes as initial condition the current system state and produces as output a sample from the target conditional distribution in finite time and without bias. This process therefore maps a point mass centered at the current state onto a probabilistic ensemble of forecasts. We prove that the drift coefficient entering the stochastic differential equation (SDE) achieving this task is non-singular, and that it can be learned efficiently by square loss regression over the time-series data. We show that the drift and the diffusion coefficients of this SDE can be adjusted after training, and that a specific choice that minimizes the impact of the estimation error gives a Föllmer process. We highlight the utility of our approach on several complex, high-dimensional forecasting problems, including stochastically forced Navier-Stokes and video prediction on the KTH and CLEVRER datasets. The code is available at https: //github. com/interpolants/forecasting.

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EAAI Journal 2024 Journal Article

Reliability-centered availability collaborative optimization allocation approach for machine tools

Yifan Chen
Zongyi Mu
Hongwei Wang
Genbao Zhang

The availability of computer numerical control (CNC) machine tools is a comprehensive reflection of reliability and maintainability. To ensure the availability requirements of products, it is necessary to allocate the availability indicators of the entire machine to all units. Traditional methods based on reliability allocation or maintainability allocation ignore the coupling relationships among them, which cannot comprehensively improve the availability level of machine tools. Therefore, in this paper, a availability collaborative optimization allocation model for CNC machine tools is proposed to maximize the availability under the optimal combination of failure rate and repair rate. Firstly, a two-stage reliability allocation model based on different allocation mechanisms is established by taking the Function-Motion-Action (FMA) decomposition tree as the allocation path. In the first stage, the allocation weights are determined based on global sensitivity analysis. In the second stage, the reliability optimization allocation model based on Copula function is established. Then, with reliability as the center, an availability collaborative optimization allocation model constrained by maintainability is constructed, and the non-dominated sorting genetic algorithm II (NSGA-II) algorithm is used to solve the model. Finally, a CNC gear milling machine is taken as an example to verify the rationality and effectiveness of the method on product availability requirements under collaborative allocation.

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TMLR Journal 2023 Journal Article

Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks

Yifan Chen
Tianning Xu
Dilek Hakkani-Tur
Di Jin
Yun Yang
Ruoqing Zhu

Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer. This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement. To address these issues, we accordingly propose two remedies: a new principle for constructing sampling probabilities and an efficient debiasing algorithm. The improvements are demonstrated by extensive analyses of estimation variance and experiments on common benchmarks. Code and algorithm implementations are publicly available at \url{https://github.com/ychen-stat-ml/GCN-layer-wise-sampling}.

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NeurIPS Conference 2023 Conference Paper

Hypervolume Maximization: A Geometric View of Pareto Set Learning

Xiaoyuan Zhang
Xi Lin
Bo Xue
Yifan Chen
Qingfu Zhang

This paper presents a novel approach to multiobjective algorithms aimed at modeling the Pareto set using neural networks. Whereas previous methods mainly focused on identifying a finite number of solutions, our approach allows for the direct modeling of the entire Pareto set. Furthermore, we establish an equivalence between learning the complete Pareto set and maximizing the associated hypervolume, which enables the convergence analysis of hypervolume (as a new metric) for Pareto set learning. Specifically, our new analysis framework reveals the connection between the learned Pareto solution and its representation in a polar coordinate system. We evaluate our proposed approach on various benchmark problems and real-world problems, and the encouraging results make it a potentially viable alternative to existing multiobjective algorithms. Code is available at \url{https: //github. com/xzhang2523/hvpsl/tree/master}.

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JBHI Journal 2023 Journal Article

Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network

Li Peng
Cheng Yang
Yifan Chen
Wei Liu

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experiments is time-consuming. Although some prediction methods have been proposed, they still have limitations. In this study, a novel computational framework, called GATCL2CD, is proposed to forecast unknown circRNA-disease associations (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity and semantic similarity for diseases, circRNA sequence similarity and function similarity, and GIPs for circRNAs. Then, we combine them to construct a heterogeneous graph. Thereafter, GATCL2CD proposes a feature convolution learning framework, that uses a multi-head dynamic attention mechanism to obtain different aggregated representations of features that correspond to the nodes in the heterogeneous graph. Then, it extracts rich higher-order features from the stacked feature representations of each node by using of a single-layer convolutional neural network with filter kernels of different sizes. Finally, a pairwise element-wise product operation is implemented to capture the interactions of higher-order feature representations, and a multilayer perceptron neural network is introduced as an efficient classifier for inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three different datasets show that GATCL2CD is superior to five other state-of-the-art methods. Furthermore, case studies demonstrate the suitability of GATCL2CD as a useful tool for identifying potential disease-related circRNAs.

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AIJ Journal 2022 Journal Article

Bayesian feature interaction selection for factorization machines

Yifan Chen
Yang Wang
Pengjie Ren
Meng Wang
Maarten de Rijke

Factorization machines are a generic supervised method for a wide range of tasks in the field of artificial intelligence, such as prediction, inference, etc. , which can effectively model feature interactions. However, handling combinations of features is expensive due to the exponential growth of feature interactions with the order. In nature, not all feature interactions are equally useful for prediction. Recently, a large number of methods that perform feature interaction selection have attracted great attention because of their effectiveness at filtering out useless feature interactions. Current feature interaction selection methods suffered from the following limitations: (1) they assume that all users share the same feature interactions; and (2) they select pairwise feature interactions only. In this paper, we propose novel Bayesian variable selection methods, targeting feature interaction selection for factorization machines, which effectively reduce the number of interactions. We study personalized feature interaction selection to account for individual preferences, and further extend the model to investigate higher-order feature interaction selection on higher-order factorization machines. We provide empirical evidence for the advantages of the proposed Bayesian feature interaction selection methods using different prediction tasks.

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EAAI Journal 2021 Journal Article

Meta-action reliability-based mechanical product optimization design under uncertainty environment

Yifan Chen
Yan Ran
Zhichao Wang
Xinlong Li
Xin Yang
Genbao Zhang

Reliability allocation is an important procedure in the early stage of product design. However, there is not a good design scheme for the reliability allocation of mechanical products. Firstly, the reliability allocation of mechanical products is still at the system level or component level, which makes it impossible to obtain more detailed reliability input parameters. Secondly, to simplify the allocation process, the interactions among allocation objects are usually ignored, which makes the result of reliability allocation not optimistic. Thirdly, the uncertainty of reliability-based mechanical product optimization has not been dealt with effectively. Therefore, this paper proposes a multi-criteria decision-making (MCDM) for mechanical product optimization design based on meta-action reliability, which integrates decision-making trial and evaluation laboratory (DEMATEL), uncertain linguistic ordered weighted averaging (ULOWA) operator, and preference ranking organization method for enrichment evaluations (PROMETHEE) II method. Also, the interval number is used to deal with the uncertainty flexibly in the decision-making process. Finally, the application and feasibility of the proposed method are illustrated with the automatic pallet changer (APC) of a CNC machine tool as an example. This method provides guidance for the reliability optimization design of mechanical products.

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JBHI Journal 2021 Journal Article

MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images

Yue Zhang
Jiong Wu
Yilong Liu
Yifan Chen
Ed X. Wu
Xiaoying Tang

Stroke is a serious manifestation of various cerebrovascular diseases and one of the most dangerous diseases in the world today. Volume quantification and location detection of chronic stroke lesions provide vital biomarkers for stroke rehabilitation. Recently, deep learning has seen a rapid growth, with a great potential in segmenting medical images. In this work, unlike most deep learning-based segmentation methods utilizing only magnetic resonance (MR) images as the input, we propose and validate a novel stroke lesion segmentation approach named multi-inputs UNet (MI-UNet) that incorporates brain parcellation information, including gray matter (GM), white matter (WM) and lateral ventricle (LV). The brain parcellation is obtained from 3D diffeomorphic registration and is concatenated with the original MR image to form two-channel inputs to the subsequent MI-UNet. Effectiveness of the proposed pipeline is validated using a dataset consisting of 229 T1-weighted MR images. Experiments are conducted via a five-fold cross-validation. The proposed MI-UNet performed significantly better than UNet in both 2D and 3D settings. Our best results obtained by 3D MI-UNet has superior segmentation performance, as measured by the Dice score, Hausdorff distance, average symmetric surface distance, as well as precision, over other state-of-the-art methods.

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NeurIPS Conference 2021 Conference Paper

Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method

Yifan Chen
Qi Zeng
Heng Ji
Yun Yang

Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism. On the other hand, although kernel machines suffer from the same computation bottleneck in pairwise dot products, several approximation schemes have been successfully incorporated to considerably reduce their computational cost without sacrificing too much accuracy. In this work, we leverage the computation methods for kernel machines to alleviate the high computational cost and introduce Skyformer, which replaces the softmax structure with a Gaussian kernel to stabilize the model training and adapts the Nyström method to a non-positive semidefinite matrix to accelerate the computation. We further conduct theoretical analysis by showing that the matrix approximation error of our proposed method is small in the spectral norm. Experiments on Long Range Arena benchmark show that the proposed method is sufficient in getting comparable or even better performance than the full self-attention while requiring fewer computation resources.

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EAAI Journal 2020 Journal Article

An extended MULTIMOORA method based on OWGA operator and Choquet integral for risk prioritization identification of failure modes

Yifan Chen
Yan Ran
Zhichao Wang
Xinlong Li
Xin Yang
Genbao Zhang

Failure mode and effects analysis (FMEA) is one of the important methods for risk analysis, and has been used in various fields to improve the reliability of systems. However, in the fuzzy environment, the aggregation, weight calculation, risk evaluation and prioritization of evaluation information limit its wide application. Therefore, this paper proposes an extended multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA) method based on the ordered weighted geometric averaging (OWGA) operator and Choquet integral for FMEA. Firstly, trapezoidal fuzzy numbers (TrFNs) are used for describing the fuzzy ratings of failure modes. Secondly, considering the uncertainty preferences of decision-makers, the OWGA operator is adopted for aggregating the evaluation information given by multiple decision-makers. Thirdly, the TrFNs ranking method based on the relative preference relation is combined with Choquet integral to model the interactions among risk factors, thus capturing their importance weights. On this basis, an extended MULTIMOORA method is proposed to determine the risk priority of failure modes. Finally, two case studies are provided to illustrate the effectiveness and practicability of the proposed method.

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