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SSRN 2023年

作者： Du, Wei Huo, Yongkang Tang, Shiyi Chen, Liang Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Engineering Shantou University Shantou515063 China Translational Medical Center for Stem Cell Therapy Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

Medical image segmentation is a crucial computer-aided diagnosis (CAD) procedure in various clinical applications. However, most existing methods rely on pixel-level annotations, which are costly and time-consuming. We propose a novel contrastive fusion model of generative and discriminative lesion maps for weakly supervised medical image segmentation to address this limitation. The generative lesion map is generated by a lesion region-guided cycle-consistent adversarial network (CCANet), and the discriminative lesion map is produced by a class-aware-based discriminative lesion localization network (DLLNet). CCANet and DLLNet share weights at the encoder layer, which contains skip connections and channel attention modules to enhance feature embedding. In order to fully utilize and integrate the generative and discriminative lesion maps, and improve the segmentation accuracy, we design a fusion method based on contrastive loss to jointly train CCANet and DLLNet. The final lesion segmentation result is obtained by applying dense conditional random fields (Dense CRF) to the fused lesion map. Extensive experiments on three benchmark medical image datasets show that our model outperforms the state-of-the-art methods on weakly supervised medical image segmentation, especially in datasets with few samples, validating the proposed model's good generalization ability and robustness in various medical image segmentation scenarios. The source code and experiment datasets are available on GitHub: https://***/mlcb-jlu/GDC4MedSeg. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding

arXiv

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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

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Grid Partition and Agglomeration for Bidirectional Hierarchical Clustering 2nd

Grid Partition and Agglomeration for Bidirectional Hierarchi...

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2nd EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2019

作者： Wu, Lei Chen, Hechang Yu, Xiangchun Chao, Sun Yu, Zhezhou Dou, RuiTing College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9783030213725

Clustering is an important data processing tool, which can be used to reveal the distribution structure of unfamiliar domain data, or as preprocess methods to magnify data object to accelerate subsequent processing or simplify models. However, the distribution of many real-world data in feature space is very complex or uneven. Besides, the similarity/distance is not easy to be properly defined in feature space with different dimensional quantity. Therefore, many existing clustering algorithms are not stable in real datasets, and better performance of different datasets relies on artificial special design, such as scale normalization. In this paper, we propose a bidirectional hierarchical clustering (BHC) algorithm with two phases. In the first phase (Top-down), based on the probability density function of data in different dimensions, the feature space is divided into over-segmented grids to adapt to the complex distribution of data. In the second phase (Bottom-up), based on statistical information, a robust distance instead of geometrical distance is defined to agglomerate the grids into a dendrogram. Compared with the individual data points, grids created in the first phase can carry more statistical information, and the magnified processing objects can accelerate the clustering process. The second phase enhances the algorithm’s ability by the ability of recognize arbitrary shape data clusters. The effectiveness of BHC is compared with 20 popular or recent clustering algorithms on 8 artificial datasets and 6 real-world datasets. And the results show that our algorithm can achieve good results on most datasets. In particular, BHC surpasses all the comparison algorithms involved in the experiment on all real-world datasets. In addition, in order to test the efficiency of the algorithm, we design an experiment which can test the influence of dimension and data size on the operation time. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommuni

关键词： Clustering algorithms

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Incomplete graph learning: A comprehensive survey

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Neural Networks 2025年 190卷

作者： Riting Xia Huibo Liu Anchen Li Xueyan Liu Yan Zhang Chunxu Zhang Bo Yang College of Computer Science Inner Mongolia University Hohhot 010021 China School of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields. 1

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Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Shenyao Liu Yuzhao Wang Yu Wang Dong Dong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China First hospital of Jilin University Changchun China

ISBN: (纸本)9781665429825

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model’s performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network’s performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods.

关键词： Training Perturbation methods Biological system modeling Predictive models Bones Reliability engineering Data models

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Research on the Task Scheduling System for Agricultural Plant Protection UAV 19

Research on the Task Scheduling System for Agricultural Plan...

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Proceedings of the 2019 2nd International Conference on Robot Systems and Applications

作者： Sun Fengjie Wang Xianchang Zhang Rui Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education China Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Chengdu Kestrel Artificial Intelligence Institute China

ISBN: (纸本)9781450365130

Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in agricultural plant protection such as watering, sowing, pesticide spraying. It helps in creating an autonomous manufacturing sys- tem by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet to the best of our knowledge, there are few re- search on practical UAV scheduling. This work proposes an algorithm to schedule the UAV actions in practice such as pesticide spraying, flying and charging. During the execution of the algorithm, the tasks are put into a queue then each task is assigned to the UAV that can complete it in the shortest amount of time, and then the UAVs perform tasks in the order in which they are as- signed. This proposed method is implemented into a scheduler and tested on datasets generated based on a real flight demonstration. Performance evaluation of the scheduler is discussed in details.

关键词： task Assignment UAV swarm scheduling

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Reducing Fault-tolerant Overhead for Distributed Stream Processing with Approximate Backup

Reducing Fault-tolerant Overhead for Distributed Stream Proc...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Yuan Zhuang Xiaohui Wei Hongliang Li Mingkai Hou Yundi Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

The stream processing model continuously processes online data in an on-pass fashion that can be more vulnerable to failures than other offline-data processing schemes. Checkpoint-based fault-tolerant methods have been widely used to enhance the reliability of stream processing systems. To ensure exact data recoveries upon failures, full-backup mechanisms are used to store a complete copy of data, which introduces substantial runtime overhead and increases output latency. In the meantime, a wide range of online processing applications prefer quick-and-dirty results with a slight degradation inaccuracy to delayed exact results. This paper introduces a novel approximate fault-tolerant problem (OAFP) with the objective of reducing the failure-free fault-tolerant overhead and ensuring user-defiled output accuracy requirement upon failure at the same time. We present an approximate fault-tolerant scheme based on sampling backup mechanism and study the trade-off between fault-tolerant overhead and output accuracy in stream processing systems. We proposed two algorithms to compute backup plans for both single-node failure and correlated failure scenarios. Extensive experiments with different types of stream topologies are conducted on our simulator to verify the correctness and effectiveness of our approach. We prove our solution guarantees the output accuracy requirement with minimum FT latency for directed acyclic graph (DAG) stream topologies with single-node failures.

关键词： Task analysis Fault tolerance Fault tolerant systems Topology Checkpointing computational modeling Data models

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Crossed-Time Delay Neural Network for Speaker Recognition

arXiv

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arXiv 2020年

作者： Chen, Liang Liang, Yanchun Shi, Xiaohu Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education School of Computer Zhuhai College Jilin University Zhuhai519041 China

Time Delay Neural Network (TDNN) is a well-performing structure for deep neural network-based speaker recognition systems. In this paper we introduce a novel structure, named Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN for speaker recognition. Inspired by the multi-filters setting of convolution layers from convolution neural networks, we set multiple time delay units with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition, CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, Factorized-TDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition. Moreover, the proposed CTDNN can handle training with a larger batch more efficiently and hence, utilize calculation resources economically. The code of the new model is released at https://***/chenllliang/CTDNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Task replica assignment in mobile self-organized crowdsensing

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International Journal of Performability engineering 2020年第1期16卷 152-162页

作者： Wei, Xiaohui Sun, Bingyi Cui, Jiaxu 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

In modern society, people carry mobile devices everywhere. However, these mobile devices often stay in an idle status, which leads to wasted resources. Thus, many researchers have sought methods to place tasks on idle mobile devices to avoid resource waste. In this paper, we propose three cooperative algorithms. The first algorithm is employed to find credible participants, which is the foundation of the latter two algorithms. The second and third algorithms are task replica assignment algorithms based on credible participants in mobile self-organized crowdsensing, and they are used in offline and online situations, respectively. The latter two algorithms adopt the greedy strategy and include constraints in assignment strategy for replicas. The experiments show that the proposed algorithms dramatically increase the probability of finding accurate results, increasing from slightly more than 0.6 for the original algorithms to nearly 0.98, even though the proposed algorithms have slightly longer average execution times. © 2020 Totem Publisher, Inc.

关键词： Credit value Crowdsensing Mobile self-organized network Opportunistic sensing Task replica assignment

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Meta-path-based outlier detection in heterogeneous information network

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Frontiers of Computer Science 2020年第2期14卷 388-403页

作者： Lu LIU Shang WANG Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Communication Engineering Jilin UniversityChangchun 130012China Department of Computer Science New Jersey Institute of TechnologyUniversity HeightsNewark NJ 07102USA

Mining outliers in heterogeneous networks is crucial to many applications,but challenges *** this paper,we focus on identifying meta-path-based outliers in heterogeneous information network(HIN),and calculate the similarity between different types of *** propose a meta-path-based outlier detection method(MPOutliers)in heterogeneous information network to deal with problems in one go under a unified *** calculates the heterogeneous reachable probability by combining different types of objects and their *** discovers the semantic information among nodes in heterogeneous networks,instead of only considering the network *** also computes the closeness degree between nodes with the same type,which extends the whole heterogeneous ***,each node is assigned with a reliable weighting to measure its authority *** experiments on two real datasets(AMiner and Movies dataset)show that our proposed method is very effective and efficient for outlier detection.

关键词： data mining heterogeneous information network outlier detection short text similarity

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