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SSRN 2023年

作者： Wang, Kaiyu Zhang, Menglin Qu, Bohao Wang, Xianchang College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Chengdu Kestrel Artificial Intelligence Institute Chengdu611730 China

Multi-agent reinforcement learning (MARL) algorithms have achieved great breakthroughs in many aspects. The MARL algorithms can learn effective policies in ideal simulation environments. But different from the ideal simulation environments, noise is unavoidable in the real world. MARL algorithms need to learn effective policies in unavoidable fully noisy environments. In this paper, we consider a challenging multi-agent reinforcement learning problem: All agents cannot observe any noiseless observations from environments during the whole training process and MARL algorithms cannot learn effective policies in these fully noisy observation environments. To solve this problem, we propose a method called Robust Policy Learning under Fully Noisy Observation viA DeNoising REpresentation NeTwork (PLANET), which enables MARL algorithms learning effective policies in fully noisy observation environments. The PLANET method learns the effective policy through two steps. (1) Extracting the noise characteristics and motion laws information from fully noisy observation histories. (2) Making MARL algorithms extract information from the noise characteristics and motion laws information and learn effective policies. The results of a series of exhaustive experiments show that our method can eliminate the effects of noise and learn effective policies in fully noisy observation environments. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Color Image Super-Resolution and Enhancement with Inter-Channel Details at Trivial Cost

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Journal of Computer Science & Technology 2020年第4期35卷 889-899页

作者： Chuang-Ye Zhang Yan Niu Tie-Ru Wu Xi-Ming Li College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computer and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China School of Mathematics Jilin UniversityChangchun 130012China

Image super-resolution is essential for a variety of applications such as medical imaging,surveillance imaging,and satellite imaging,among ***,the most popular color image super-resolution is performed in each color channel *** this paper,we show that the super-resolution quality can be further enhanced by exploiting the cross-channel *** by the High-Quality Linear Interpolation(HQLI)demosaicking algorithm by Malvar et al.,we design an image super-resolution scheme that integrates intra-channel interpolation with cross-channel details by isotropic linear *** its simplicity,our super-resolution method achieves the accuracy comparable with the existing fastest state-of-the-art super-resolution algorithm at 20 times faster *** is well applicable to applications that adopt traditional interpolations,for improved visual quality at trivial computation *** comparative study verifies the effectiveness and efficiency of the proposed super-resolution algorithm.

关键词： image super-resolution isotropic linear interpolation inter-channel detail enhancement low cost

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AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

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Journal of Advanced Research 2025年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Kong, Yanwen Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Software Jilin University Jilin Changchun 130012 China School of Life Sciences Jilin University Jilin Changchun 130012 China Department of Computer Science Columbia University 116th and Broadway New York City 10027 NY United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang 550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun 130103 China Changchun Wenli High School Jilin Changchun 130062 China

Introduction: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. Objectives: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. Methods: This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Results: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the −log(MIC) values on the benchmark dataset. Conclusion: Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is ess

关键词： Activity cliff (AC) AMPCliff Antimicrobial peptide (AMP) Benchmark Minimum inhibitory concentration (MIC)

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Categorizing npm Packages by Analyzing the Text Information in Software Repositories

Categorizing npm Packages by Analyzing the Text Information ...

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Asia-Pacific Conference on Software engineering

作者： Yu Wang Huaxiao Liu Shanquan Gao Shujia Li College of computer science and technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9781665437851

To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.

关键词： Correlation Codes Ecosystems Wheels Tagging Software Libraries

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A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion

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Computers in Biology and Medicine 2024年 175卷 108549-108549页

作者： Lin, Qifeng Guo, Xiaoxin Feng, Bo Guo, Juntong Ni, Shuang Dong, Hongliang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University 2699 Qianjin Street Changchun130012 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China College of Software Jilin University 2699 Qianjin Street Changchun130012 China

In this paper, we propose a multi-task learning (MTL) network based on the label-level fusion of metadata and hand-crafted features by unsupervised clustering to generate new clustering labels as an optimization goal. We propose a MTL module (MTLM) that incorporates an attention mechanism to enable the model to learn more integrated, variable information. We propose a dynamic strategy to adjust the loss weights of different tasks, and trade off the contributions of multiple branches. Instead of feature-level fusion, we propose label-level fusion and combine the results of our proposed MTLM with the results of the image classification network to achieve better lesion prediction on multiple dermatological datasets. We verify the effectiveness of the proposed model by quantitative and qualitative measures. The MTL network using multi-modal clues and label-level fusion can yield the significant performance improvement for skin lesion classification. © 2024 Elsevier Ltd

关键词： Metadata

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A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems

arXiv

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arXiv 2022年

作者： Xu, Yuanbo Wang, En Yang, Yongjian Chang, Yi The Department of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University Changchun130012 China The School of Artificial Intelligence Jilin University Jilin Changchun130012 China

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. Firstly, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representa

关键词： Recommender systems

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Rule-based modeling and simulation of gene expression process 10

Rule-based modeling and simulation of gene expression proces...

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2020 10th International Workshop on Computer Science and engineering, WCSE 2020

作者： Zhao, Tianqi Luo, Cuntong Yang, Yi Feng, Liwei Lu, Xinhua College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789811447877

A process of gene expression which is designed and modeled by machine language, and by using to process complex data. At the molecular biological level, classifying and sorting the related substances involved in the transcription and translation processes, and using a rule-based modeling method to simulate the gene expression process. By entering the initial data of the designed gene into the program, the computer can give the result of the gene expression process. The experimental data shows that this gene expression result is correct. In this paper, we summarize the rules of the transcription and translation of the gene expression process and the basic part of the central dogma, and then formalize it, and then establish a model to realize the biological information processing in uncomplicated situations, and then realize the gene expression process on the computer. © WCSE 2020.

关键词： Transcription

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Divergent Projection Analysis for Unsupervised Dimensionality Reduction

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Procedia Computer Science 2022年 199卷 384-391页

作者： Shanshan Wang Lan Bai Xu Chen Zhen Wang Yuan-Hai Shao School of Mathematical Sciences Inner Mongolia University Hohhot 010021 P.R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P.R. China School of Management Hainan University Haikou 570228 P.R. China

Principal component analysis (PCA) has been extended to a series of classical methods in dimensionality reduction for unsu¬pervised learning. However, the differences among the projected data samples may not be sufficient by PCA, causing problems both in data visualization and recognition. Though some nonlinear dimensionality reduction methods have been proposed to preserve the samples’ differences, their nonlinear mappings hide the relationships among the data features, e.g., Isometric mapping (Isomap) and Locally Linear Embedding (LLE). In this paper, we propose a linear dimensionality reduction method, called Divergent Projection Analysis (DPA), to preserve the largest differences among the samples. Our DPA projects the sam¬ples into a low-dimensional space such that any two projected samples have a certain distance, resulting in solving a nonconvex optimization problem. The nonconvex problem is solved by the subgradient descent algorithm and particle swarm optimiza¬tion algorithm, and their solution qualities are compared in the experiments. Experiments on a synthetic dataset and three face recognition problems confirm the effectiveness of the proposed method compared with other state-of-the-art methods.

关键词： Unsupervised learning Dimensionality reduction Linear dimensionality reduction Largest differences Nonconvex optimization

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Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator

arXiv

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arXiv 2025年

作者： Wang, Bing Zhao, Bingrui Li, Ximing Li, Changchun Gao, Wanfu Wang, Shengsheng College of Computer Science and Technology Jilin University Jilin Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University China

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistently extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CameRed. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CameRed outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Tweets

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

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.

关键词： Deep learning Training Visualization Image analysis Semantics Feature extraction Risk management

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