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Dynamic finegrained structured pruning sensitive to filter texture distribution

AI COMMUNICATIONS 2023年第4期36卷 311-323页

作者： Li, Ping Wang, Yuzhe Wu, Cong Kang, Xiatao Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Hunan Peoples R China Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha Hunan Peoples R China

Pruning of neural networks is undoubtedly a popular approach to cope with the current compression of large-scale, high-cost network models. However, most of the existing methods require a high level of human-regulated pruning criteria, which requires a lot of human effort to figure out a reasonable pruning strength. One of the main reasons is that there are different levels of sensitivity distribution in the network. Our main goal is to discover compression methods that adapt to this distribution to avoid deep architectural damage to the network due to unnecessary pruning. In this paper, we propose a filter texture distribution that affects the training of the network. We also analyze the sensitivity of each of the diverse states of this distribution. To do so, we first use a multidimensional penalty method that can analyze the potential sensitivity based on this texture distribution to obtain a pruning-friendly sparse environment. Then, we set up a lightweight dynamic threshold container in order to prune the sparse network. By providing each filter with a suitable threshold for that filter at a low cost, a massive reduction in the number of parameters is achieved without affecting the contribution of certain pruning-sensitive layers to the network as a whole. In the final experiments, our two methods adapted to texture distribution were applied to ResNet Deep Neural Network (DNN) and VGG-16, which were deployed on the classical CIFAR-10/100 and ImageNet datasets with excellent results in order to facilitate comparison with good cutting-edge pruning methods. Code is available at https://***/wangyuzhe27/CDP-and-DTC.

关键词： Threshold neural network penalty pruning

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Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases

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BMC GENOMICS 2024年第1期25卷 584页

作者： Li, Wenjun Ma, Wanjun Yang, Mengyun Tang, Xiwei Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha Hunan Peoples R China Hunan First Normal Univ Sch Intelligent Mfg Changsha 410205 Hunan Peoples R China

Motivation The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature *** The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.

关键词： Drug repurposing Drug-Target-Disease ternary relationship DTD-GNN model

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MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

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METHODS 2024年 231卷 1-7页

作者： Tang, Xiwei Ma, Wanjun Yang, Mengyun Li, Wenjun Hunan First Normal Univ Sch Comp Sci Changsha Hunan Peoples R China Changsha Univ Sci & Technol Changsha Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha Hunan Peoples R China

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

关键词： Drug repurposing Drug target interaction Deep neural network Multi-scale learning

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Embedded Deep Learning Based CT Images for Rifampicin Resistant Tuberculosis Diagnosis 7th

Embedded Deep Learning Based CT Images for Rifampicin Resist...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Li, Wenjun Xig, Jiaojiao Peng, Huan Ma, Wanjun Liang, Weijun Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha Hunan Peoples R China Univ South China Affiliated Changsha Cent Hosp Hengyang Med Sch Changsha Hunan Peoples R China

ISBN: (纸本)9789819784950;9789819784967

In the treatment of tuberculosis (TB), drug-resistant tuberculosis arises when Mycobacterium tuberculosis undergoes genetic mutations or acquires resistance through horizontal gene transfer. Identifying the treatment response of TB patients to Rifampicin, a principal medication for TB treatment, is essential for healthcare professionals to make timely and accurate diagnoses. Not only can this approach save on the costs and duration of TB treatment, but it also helps prevent the disease's spread and fatalities. Traditional methods for diagnosing Rifampicin-resistant TB involve molecular biology tests and drug susceptibility testing, which are time-consuming, expensive, and labor-intensive. To assist physicians in diagnosing the treatment response of TB patients to Rifampicin more rapidly and efficiently, this study introduces a computer-aided diagnostic algorithm based on Embedded Deep Learning (EDL). Initially, CT images from target patients at two imaging centers were collected. The classifier model used in this research combines image preprocessing techniques, three convolutional neural networks, and decision fusion technology to enhance the model's classification efficiency and reduce overfitting. Additionally, the Grad-CAM model was utilized for visualizing the areas of lesions. In the test sets from both centers, the Embedded Deep Learning Model (EDL Model) demonstrated superior performance over other models by combining hard voting or soft voting mechanisms, with an average accuracy improvement of 3.16-16.87%, AUC increase of 3.05-12.66%, and F1-score enhancement of 6.38-22.49%. The diagnostic tool developed in this research for assisting in the diagnosis of TB patients' response to Rifampicin treatment has significant clinical potential, particularly in settings lacking specialized radiological expertise.

关键词： Embedded Deep Learning Rifampicin Drug Resistance Tuberculosis CT Images Diagnostic Application

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Learning background-aware and spatial-temporal regularized correlation filters for visual tracking

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APPLIED INTELLIGENCE 2023年第7期53卷 7697-7712页

作者： Zhang, Jianming He, Yaoqi Feng, Wenjun Wang, Jin Xiong, Neal N. Changsha Univ Sci & Technol Sch Comp & Commun Engn Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410114 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

In visual tracking, correlation Filters (CFs) have attracted increasing research attention and achieved superior performance. However, owing to the larger search area, more background information is introduced to the shifted samples, meaning that tracking errors are prone to appear in the detection stage. Accordingly, in this work, firstly, hand-crafted features and deep features extracted from pre-trained convolutional networks are combined to improve the representation ability of object appearance. For deep features, we use two different VGG networks for extraction. Secondly, in an attempt to solve the problem of the object background of the traditional CF model not being modeled over time, and owing to the lack of spatial-temporal information of the image, we propose a new background-aware and spatial-temporal regularized correlation filters model (BSTCF) that introduces the background constraint and spatial-temporal regularization. The proposed BSTCF can effectively model not only the background but also variations in the background over time. Finally, we transform the objective function of BSTCF into an unconstrained Augmented Lagrange multiplier formular to promote convergence to the global optimum solution. Moreover, we adopt the alternating direction multiplier method (ADMM) to produce three sub-problems with closed-form solution, then propose a corresponding algorithm. Based on the above, we construct an intelligent tracking system and carry out extensive experiments to test its performance on OTB-2013, OTB-2015, TC128, UAV123, and VOT2016 public datasets. The experimental results demonstrate that the tracking algorithm achieves superior performance.

关键词： Convolutional neural network Correlation filter Background-aware Spatial-temporal ADMM

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An adaptive confidence-based data revision framework for Document-level Relation Extraction

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INFORMATION procESSING & MANAGEMENT 2025年第1期62卷

作者： Jiang, Chao Liao, Jinzhi Zhao, Xiang Zeng, Daojian Dai, Jianhua Natl Univ Def Technol Lab Big Data & Decis 109 Deya Rd Changsha 410073 Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China

Noisy annotations have become a key issue limiting Doc ument-level R elation E xtraction (DocRE). Previous research explored the problem through manual re-annotation. However, the handcrafted strategy is of low efficiency, incurs high human costs and cannot be generalized to large-scale datasets. To address the problem, we construct a confidence-based Re vision framework for D ocRE (ReD), ReD ), aiming to achieve high-quality automatic data revision. Specifically, we first introduce a denoising training module to recognize relational facts and prevent noisy annotations. Second, a confidence-based data revision module is equipped to perform adaptive data revision for long-tail distributed relational facts. After the data revision, we design an iterative training module to create a virtuous cycle, which transforms the revised data into useful training data to support further revision. By capitalizing on ReD, we propose ReD-DocRED, which consists of 101,873 revised annotated documents from DocRED. ReDDocRED has introduced 57.1% new relational facts, and concurrently, models trained on ReD-DocRED have achieved significant improvements in F1 scores, ranging from 6.35 to 16.55. The experimental results demonstrate that ReD can achieve high-quality data revision and, to some extent, replace manual labeling.

关键词： Noisy annotations Adaptive data revision Denoising training Document level Relation Extraction

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A multi-UAV assisted non-orthogonal multiple access based relay system for minimal average receiving rate maximization

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SOFT COMPUTING 2024年第11-12期28卷 7125页

作者： Tang, Qiang Qu, Xinyu Wang, Jin He, Shiming Changsha Univ Sci & Technol Sch Comp Sci & Commun Engn Changsha Hunan Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha 410004 Peoples R China

In this paper, we proposed a data relay system consisting of multiple unmanned aerial vehicles (UAVs) and acess point (AP) with the aim of maximizing the minimum receiving rate of Internet of things devices (IoTDs) as the optimization objective. To enhance the utilization of communication resources, we employed non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) techniques at the AP side. In addition, we used the Wireless power transmission (WPT) technique on the UAV side to charge the IoTDs and prolong their operational lifetime. We formulated the problem as a mixed integer nonlinear programming (MINLP). To solve the MINLP problem, it was decomposed into four subproblems: connection scheduling between UAVs and IoTDs, time slot allocation, UAV trajectory, and AP's power distribution. Finally, we solved all subproblems by using convex optimization techniques, such as successive convex approximations (SCA), and proposed an iterative algorithm. In the simulation, we compared our proposed algorithm with different optimization schemes. The results indicated that our scheme outperforms the others.

关键词： data relay system Non-orthogonal multiple assess UAV trajectory optimization Mimimal average receiving rate maximization

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Multi-label Feature Selection With Missing Features via Implicit label Replenishment and Positive Correlation Feature Recovery

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IEEE tranSACTIONS ON KNOWLEDGE AND data ENGINEERING 2025年第4期37卷 2042-2055页

作者： Dai, Jianhua Chen, Wenxiang Qian, Yuhua Hunan Normal Univ Coll Informat Sci & Engn Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China

Multi-label feature selection can effectively solve the curse of dimensionality problem in multi-label learning. Existing multi-label feature selection methods mostly handle multi-label data without missing features. However, in practical applications, multi-label data with missing features exist widely, and most existing multi-label feature selection methods are not directly applicable. Therefore, we propose a feature selection method for multi-label data with missing features. First, we propose a method to extract implicit label information from the feature space to replenish the binary label information. Second, we learn the positive correlation between features to construct a feature correlation recovery matrix to recover missing features. Finally, we design a sparse model-based multi-label feature selection method for processing multi-label data with missing features and prove the convergence of this method. Comparative experiments with existing feature selection methods demonstrate the effectiveness of our method.

关键词： Feature extraction Correlation Sparse matrices data mining Manifold learning Matrix decomposition data models Convergence Vectors Training Multi-label feature selection missing features implicit label information sparse model

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ETBRec: a novel recommendation algorithm combining the double influence of trust relationship and expert users

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APPLIED INTELLIGENCE 2022年第1期52卷 282-294页

作者： Duan, Zhenchun Xu, Weihong Chen, Yuantao Ding, Lin Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410114 Hunan Peoples R China

The recommendation system has become the primary tool used by many Internet application platforms to solve the problem of information overload, and it faces issues such as data sparsity, cold start, and scalability. At present, most social recommendation algorithms only consider the influence of the trust relationship on the user's feature vector, which indirectly affects the predicted rating, or consider directly trusting friends as neighbor users, which directly affects the predicted rating, but does not consider the direct influence and indirect combining influences to make rating predictions. Therefore, this paper proposes a collaborative filtering recommendation algorithm (ETBRec), which not only considers the trust difference between users but also proposes the definition of experts and considers the direct impact of expert users on prediction ratings and the trustees' indirect impact of ratings. Among them, the trust difference is realized through trust metrics, including direct trust metrics and indirect trust metrics;the selection of expert users takes into account the user's degree of trust and user rating attitude;experimental comparisons with various social recommendation algorithms and related recommendation algorithms on the Ciao and Douban datasets. The experimental results show that the ETBRec algorithm performs better on some evaluation indexes such as mean absolute error (MAE) and root mean squared error (RMSE).

关键词： Recommender systems Trust relationship User similarity Social networks Expert user

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Improved kernel and algorithm for claw and diamond free edge deletion based on refined observations

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THEORETICAL COMPUTER SCIENCE 2022年 906卷 83-93页

作者： Li, Wenjun Peng, Huan Yang, Yongjie Changsha Univ Sci & Technol Sch Comp & Commun Engn Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha Peoples R China Saarland Univ Chair Econ Theory Saarbrucken Germany

In the {CLAW, DIAMOND}-FREE EDGE DELETION PROBLEM (CDFED), we are given a graph G and an integer k > 0, and the question is whether there are at most k edges whose deletion results in a graph without claws and diamonds as induced subgraphs. Based on some refined observations, we propose a kernel of O (k(3)) vertices and O (k(4)) edges, significantly improving the previous kernel of O (k(12)) vertices and O (k(24)) edges. In addition, we derive an O*(3.792(k))-time algorithm for CDFED. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Edge deletion Kernelization FPT-algorithms Claw Diamond

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