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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bai, Tian Li, Chu Peng, Xinyue Guan, Haotian Zhang, Zefan Wang, Guishen Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Jilin University School of Nursing Changchun China Jilin University School of Life Sciences Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy. © 2023 IEEE.

关键词： drug-drug interaction prediction graph-level classification line graph line subgraph strategy node feature enhancing method

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Structural Optimization of Causal Driven Model Based on Bayesian Network in High-dimensional Data Classification

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Applied Mathematics and Nonlinear Sciences 2025年第1期10卷

作者： Li, Kuo Wang, Aimin Wang, Limin Zhao, Yuetan Zhu, Xinyu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China

The Bayesian network (BN) model, as a big data graph model that integrates causal inference and probabilistic representation, has received widespread attention and research in both academia and industry. However, with the advent of the big data era, traditional BN structure learning algorithms have encountered unprecedented challenges in processing high-dimensional data, mainly manifested as a sharp increase in computational complexity and difficulty in achieving ideal accuracy requirements within an acceptable time range, which greatly limits their breadth and depth in practical applications. In response to this bottleneck problem, this article innovatively proposes a new approach that combines width learning theory with BN, referred to as Broad Bayesian Neural Network (Broad-BNN). This model effectively reduces the dimensionality of the original high-dimensional data by introducing a feature mapping layer and gradually expanding it, while achieving non-linear transformation of information and effective feature extraction. The experimental results show that the model proposed in this paper has achieved significant performance improvement in high-dimensional data classification problems, not only accelerating training speed but also significantly improving classification accuracy, providing a new perspective and solution for solving the difficulties of high-dimensional data processing. © 2025 Kuo Li et al., published by Sciendo.

关键词： Bayesian networks

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Attribute imputation autoencoders for attribute-missing graphs

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knowledge-Based Systems 2024年 291卷

作者： Xia, Riting Zhang, Chunxu Li, Anchen Liu, Xueyan Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China

Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data imputation techniques based on graph autoencoders are commonly used for attribute-missing graphs. However, this method cannot effectively integrate existing attributes and structural information during the encoding stage and is prone to introducing noise, resulting in inaccurate imputation. In addition, the expressiveness of decoders in existing methods is limited because their network architecture has not been adequately designed, which restricts the accuracy and robustness of the generated attributes. To address these issues, we propose a novel Attribute Imputation AutoEncoder for attribute-missing graphs, named AIAE. In particular, during the encoding stage, a dual encoder based on knowledge distillation is designed to encode both attribute and structural information into representations of attribute-missing nodes to achieve more accurate imputation. To avoid introducing noise, we fully exploit the observed information by reorganizing the representations of the attribute-missing and attribute-observed nodes. In the decoding stage, we propose a multi-scale decoder with masking to make the decoder more expressive and enhance its robustness and generative ability. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods in attribute-missing graphs. © 2024 Elsevier B.V.

关键词： Topology

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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

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China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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SMT(LIA) Sampling with High Diversity

arXiv

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： Software testing

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Towards More Accurate and Complete Iris Segmentation Using Hybrid Transformer U-Net

Towards More Accurate and Complete Iris Segmentation Using H...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ye Sun Yinan Lu Yuanning Liu Xiaodong Zhu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

ISBN: (纸本)9781665463959

Iris images captured in less-constrained environments often suffer from adverse noise, challenging many existing segmentation algorithms. In this paper, we propose an efficient Hybrid Transformer U-Net (HTU-Net) to address this dilemma. Unlike previous studies that only focus on utilizing popular CNN technology to predict iris masks accurately, HTU-Net can simultaneously obtain segmentation masks and parameterized pupillary and limbic boundaries by a multi-task network, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We explore the application of the Transformer in iris segmentation and propose a hybrid encoder that employs convolutional layers to extract local intensity features and the Transformer to capture long-range associative information. For decoding, we adopt a novel Multi-Head Dilated Attention to exploit the multi-scale contextual information by gating mechanism, thus emphasizing the important features and rendering powerful representations. Inspired by the consistent class characteristics of iris, we further devise a Pyramid Center-Aware Module to capture the global structural context of iris from a categorical perspective to improve performance. Experimental results show that our method, with fewer parameters than previous approaches, achieves competitive or new state-of-the-art performance in both iris segmentation and localization on three challenging iris datasets. Code will be released at https://***/Syloveslife/HTU-Net.

关键词： Image segmentation Biological system modeling Transformers Feature extraction Rendering (computer graphics) Prediction algorithms Multitasking

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Learning Stable Task-Level Manifold for Few-Shot Learning

Learning Stable Task-Level Manifold for Few-Shot Learning

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Siyi Yu Wei Luo Gang Li Bo Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China School of Information Technology Deakin University Geelong Australia

Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of the main challenges in FSL is the use of pretrained embeddings whose dimension is too high for the small sample size. While the large dimensionality helps capture diverse features in the training example, not all the features are relevant to the task at hand. Such task-irrelevant features from pre-trained embeddings can negatively impact the performance of the FSL model. To address this challenge, we propose a simple yet effective way to learn task-level manifolds that explicitly eliminate the negative effect of nuisance features. One key novelty of our approach is to address class imbalance in the training data by injecting supervised information through key virtual samples, which we named Manifold Support Points. Empirically, we demonstrate that the stability of the learnt task-level manifolds and their effectiveness in challenging tasks in FSL. Our approach shows competitive performance on several benchmark datasets, including tieredImageNet and CUB.

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Mask2Edge: Masking dependencies and dynamically capturing pixel differences in edge detection

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Expert Systems with Applications 2025年 286卷

作者： Zhou, Jianhang Zhao, Hongwei Qu, Daikun Xing, Long College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Army Artillery and Air Defense Academy Sergeant School Liaoning Shenyang110000 China

Edge detection plays an important role in computer vision tasks. Deep learning-based edge detectors commonly rely on encoding the long and short-term dependencies of pixel values to mine contextual information. They strongly focus on all positions in the image, ignoring the potential issue of over-encoding. Furthermore, most of these models have not attempted to leverage the inherent properties of edges. In this paper, we introduce a query-based edge detector named Mask2Edge, which is capable of masking dependencies and dynamically capturing pixel differences. Specifically, we first devise a masking strategy based on the sparsity of edges to alleviate the over-encoding issue. We propose a Region-guided Masked Attention, which adapts to edge detection and is capable of constraining cross-attention with appropriate masking intensity to extract relatively complete local features. Subsequently, we design a structure to capture the pixel differences that can help identify edges. We introduce dynamic convolutions into edge detection and refine the application scope and generation method of attention weights to effectively perceive changes in pixel gradients. Extensive experiments demonstrate the superiority of Mask2Edge compared with state-of-the-art methods. The source code is available at https://***/zhoujh2020/Mask2Edge. © 2025 Elsevier Ltd

关键词： Edge detection

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Hao Wu, Yirui Hu, Hexuan Lu, Hu Lai, Yong Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources China College of Computer and Information Hohai University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Computer Science and Communication Engineering Jiangsu University China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665468190

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like 'black box' without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods. © 2022 IEEE.

关键词： Forecasting

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