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A Digital Twin-Based Intelligent Network architecture for Underwater Acoustic Sensor Networks

IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

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TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

arXiv

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arXiv 2023年

作者： Song, Rui Giunchiglia, Fausto Li, Yingji Tian, Mingjie Xu, Hao School of Artificial Intelligence Jilin University Changchun130012 China Department of Information Engineering and Computer Science University of Trento Via Sommarive Trento938123 Italy College of Computer Science and Technology Jilin University Changchun130012 China Chongqing Research Institute Jilin University Chongqing401123 China

Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domain-invariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned issues, this paper proposes TACIT, a target domain agnostic feature disentanglement framework which adaptively decouples robust and unrobust features by Variational Auto-Encoders. Additionally, to encourage the separation of unrobust features from robust features, we design a feature distillation task that compels unrobust features to approximate the output of the teacher. The teacher model is trained with a few easy samples that are easy to carry potential unknown shortcuts. Experimental results verify that our framework achieves comparable results to state-of-the-art baselines while utilizing only source domain data. © 2023, CC BY.

关键词： Distillation

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression

A study on the prediction model of dam seepage volume based ...

引用

International Conference on computer engineering and Applications (ICCEA)

作者： Guanyu Chen Suli Zhang Chenlin Zhang XiaoChun Qian Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun University of Technology School of Computer Science & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun Institute of Technology School of Computer Technology & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Jilin Institute of Chemical Technology School of Information and Control Engineering Changchun China

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features.

关键词：

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A Multi-Agent Deep Reinforcement Learning Method for Fully Noisy Observations

SSRN

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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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MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound-Protein Interactions

arXiv

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arXiv 2023年

作者： Zhang, Li Li, Wenhao Guan, Haotian He, Zhiquan Cheng, Mingjun Wang, Han School of Computer Science and Engineering Changchun University of Technology Changchun China School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China The College of Computer Science and Technology Jilin University Changchun China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

The identification of compound-protein interactions (CPI) plays a critical role in drug screening, drug repurposing, and combination therapy studies. The effectiveness of CPI prediction relies heavily on the features extracted from both compounds and target proteins. While various prediction methods employ different feature combinations, both molecular-based and network-based models encounter the common obstacle of incomplete feature representations. Thus, a promising solution to this issue is to fully integrate all relevant CPI features. This study proposed a novel model named MCPI, which is designed to improve the prediction performance of CPI by integrating multiple sources of information, including the PPI network, CCI network, and structural features of CPI. The results of the study indicate that the MCPI model outperformed other existing methods for predicting CPI on public datasets. Furthermore, the study has practical implications for drug development, as the model was applied to search for potential inhibitors among FDA-approved drugs in response to the SARS-CoV-2 pandemic. The prediction results were then validated through the literature, suggesting that the MCPI model could be a useful tool for identifying potential drug candidates. Overall, this study has the potential to advance our understanding of CPI and guide drug development efforts. © 2023, CC BY.

关键词： Forecasting

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Bayesian Network Structure Learning and Application

引用

Mobile Information Systems 2022年 2022卷

作者： Zhang, Liwei Liu, Dayou Wang, Zheng Wang, Xinlu College of Computer Science and Technology Jilin University Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin130012 China Department of Computer Aviation University of Air Force Jilin130022 China Engineering Training Center Changchun Institute of Engineering Jilin130012 China

With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level;the algorithm proposed in this study has obvious advantages in bibliometrics. © 2022 Liwei Zhang et al.

关键词： Bayesian networks

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Keyword extraction based on semantic similarity metric and multi-feature computing 7

Keyword extraction based on semantic similarity metric and m...

引用

7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Kang, Hui Sun, Xin Feng, Tie Lin, Shuhang Jilin University College of Computer Science and Technology Changchun China Jilin University College of Software Changchun China Beijing ByteDance Technology Co. Ltd. Department of Product R&D and Engineering Architecture Beijing China

ISBN: (纸本)9781665442527

Keyword extraction is an effective method to extract keywords from a text which can express the main content of the text. However, it is difficult to extract useful information quickly from a large number of documents. In this paper, a semantic similarity metric and multi-feature computing based keyword extraction algorithm is presented. First, a semantic similarity algorithm based on WordNet semantic dictionary toward distance, information and property (SSDIPA) is proposed, which quantifies the degree of association between words. Then, a multi-feature tuple (word frequency, word length, word span, word position, semantic similarity) based computing method is presentssd to score the candidate keywords. Finally, the feature decision tree is used to adjust the proportional relationship among the features so that the keyword extraction results can meet the use's subjective preferences. The result shows that the proposed algorithm outperforms other comparison algorithms, and the multi-feature computing based keywords extraction algorithm can improve the accuracy. © 2021 IEEE.

关键词： Decision trees

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L-Carnitine Can Promote the Growth Performance of Rotifers (Brachionus Rotundiformis) by Improving Their Feeding Behavior and Nutrient Composition

SSRN

引用

SSRN 2024年

作者： Gao, Yongsheng Liu, Fayou Zhang, Xinping Wang, Sibu Zhang, Dongming Wang, Qiuju Wang, Guiqin Huang, Quan Chen, Yuke Guo, Zhi xin College of Animal Science and Technology Jinlin Agricultural University Changchun130118 China Water Conservancy and Hydropower investigation and Design Institute of Jilin Province Changchun130000 China Tonghua Normal University College of life science Jilin Tonghua134001 China Aquatic technology promotion station of Da'an Jilin 131300 China Department of Agronomy of Hetao College Bayan Nur015000 China Changchun University of Architecture and Civil Engineering Changchun130607 China

Rotifer are important food resources in aquaculture hatcheries, and several studies have shown that the L-carnitine (LC) can increase the rotifer population density. The aim of this study was to investigate the nature of beneficial effects of LC on rotifer Brachionus rotundiformis. The feeding behavior of rotifers fed with baker’s yeast (Saccharomyces cerevisiae) or microalgae (Chlorella sp.) were evaluated at different LC concentrations (0, 1, 10 and 100 mg L-1). Meanwhile, the effects of LC on growth performance, free LC content, amino acid and fatty acid composition of rotifers were analyzed. The results indicated that LC (10 and 100 mg L-1) treatments significantly improved the filtration and ingestion rates of rotifers fed microalgae or baker’s yeast, and the peak values for both filtration and ingestion rates of rotifer fed baker’s yeast were 0.13 uL h-1 rot-1, 5.09 ×103 cell h-1 rot-1, respectively. In addition, LC (10 and 100 mg L-1) not only significantly accumulated in rotifers, but also significantly improved the rotifer growth performance. After 48 h of LC treatment, the rotifer population density significantly increased and the rotifer egg ratio significantly decreased. Concerning the biochemical composition of rotifers, 100 mg L-1 LC can increase the carbohydrates content, decrease the protein and lipid content in the rotifers. And the LC treatment could significantly decrease the total SFA content of rotifers, while significantly increase the total HUFA content of rotifers. Moreover, the levels of LC supplementation were positively correlated with the contents of Thr, Gly, Ala, Leu, Tyr, Phe, Lys, His, Arg and Pro in rotifers. Overall, adding LC could not only accumulate in rotifers, but also promote the growth performance of rotifers by improving their feeding behavior and their nutritional value. © 2024, The Authors. All rights reserved.

关键词： Microalgae

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DPA-2:a large atomic model as a multitask learner

引用

npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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