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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Advances in intelligent computing approaches for solving problems related to photonic crystal fibers: Advances in Intelligent..: D. Yang et al.

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Optical and Quantum Electronics 2025年第1期57卷 1-31页

作者： Yang, Dan Zhao, Yuyu Liu, Hong Li, Yijin Tang, Chang Li, Shijun Lingye, Zhenglin College of Information Science & Engineering Northeastern University Shenyang110819 Thailand National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 Thailand

Photonic crystal fiber (PCF) has shown a promising application in various fields of modern optics due to its excellent transmission characteristics. However, conventional numerical solution methods have limitations such as high computational effort and low efficiency in designing PCF structures and characterizing pulse propagation in PCFs. With the advancement of artificial intelligence technology, various types of intelligent computing approaches, especially intelligent algorithm and machine learning algorithm (IML), provide new perspectives to solve the above difficulties. Therefore, an increasing number of researchers are committed to using IML to break through the limitations of conventional methods and accordingly open up a new interdisciplinary research direction. This paper begins with a brief overview of common optical properties and typical algorithms. Subsequently, it reviews specific areas where PCF and IML are combined, including the prediction and optimization of optical properties, the design of polarization devices, fiber lasers and amplifiers, as well as the control and prediction of Supercontinuum generation. Finally, this paper offers insights into the future developments and challenges in this emerging interdisciplinary field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supercontinuum generation

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Adaptive Intention Learning for Session-Based Recommendation

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ACM Transactions on Intelligent Systems and Technology 2025年第2期16卷 1-26页

作者： Zhang, Qingbo Yang, Xiaochun Chen, Hao Wang, Bin Sun, Zhu Zhou, Xiangmin Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Singapore University of Technology and Design Singapore Singapore Rmit University Melbourne Australia Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China

In recent years, session-based recommender systems (SRSs) have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of SRSs. However, existing techniques assume that all sessions have the same number of intentions or that the items in one category belonging to the same session reflect the same intention. In real applications, such as e-commerce, sessions may have different numbers of intentions, and the same type of items in a session may correspond to different intentions. As a result, existing techniques cannot guarantee high-quality user interest prediction. In this article, we propose a novel Adaptive Intention Learning Network (AILN) to capture an adaptive number of intentions for each session, thereby enhancing the accuracy of user interest inference. Specifically, we design an intention evaluation network (IEN) to evaluate whether a subsequence of a session corresponds to a valid intention, and an intention generation network (IGN) to learn the representation of a valid intention. By checking each subsequence of a session, IEN and IGN enable the incremental learning of a session-specific intention hierarchy (IH) to store valid intentions of the session. To reduce the cost of building the IH, we propose a pruning strategy that exploits the intention validity to avoid unnecessary evaluation. The representative intentions are selected from IH and input into a designed interest predictor to infer the user interest. Experimental results on two real-world datasets demonstrate the superiority of our proposed AILN. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Recommender systems

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Cosmological perturbations in the energy-momentum squared gravity theory:constraints from gravitational wave standard sirens and redshift space distortions

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Chinese Physics C 2025年第4期49卷 199-208页

作者： Qi-Ming Fu Xin Zhang Institute of Physics Shaanxi University of TechnologyHanzhong 723000China Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Provinceand Key Laboratory of Quantum Theory and Applications of MoELanzhou UniversityLanzhou 730000China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

We investigate the linear cosmological perturbations in the context of the so-called energy-momentum squared gravity(EMSG)*** research shows that the EMSG theory can reproduce a viable background cosmological evolution comparable toΛCDM,whereas the matter-dominated era exhibits slight *** this paper,we focus on power-law EMSG models and derive the equations for the linear cosmological *** explore the propagation of the gravitational wave(GW)and the growth of matter density perturbation at the first order,and we estimate the model parameters from the simulated GW and observed redshift space distortion *** analysis reveals that the model parameters should be small and positive in the confidence interval,which indicates that the theory agrees closely with the observational data and can be considered an alternative to the standard cosmological model.

关键词： energy-momentum squared gravity cosmological perturbations gravitational wave cosmological parameters

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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A quinolinyl analog of resveratrol improves neuronal damage after ischemic stroke by promoting Parkin-mediated mitophagy

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Chinese Journal of Natural Medicines 2025年第2期23卷 214-224页

作者： Qingqi Meng Yan Mi Libin Xu Yeshu Liu Dong Liang Yongping Wang Yan Wang Yueyang Liu Guoliang Chen Yue Hou Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110167China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Shenyang Key Laboratory of Vascular Biology Science and Research CenterDepartment of PharmacologyShenyang Medical CollegeShenyang 110034China Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical UniversityShenyang 110016China

Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or ***,extracted from Polygonum cuspidatum *** Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective ***,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately *** study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in *** research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic *** findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy ***,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in *** results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.

关键词： (E)-4-(3 5-dimethoxystyryl)quinoline Resveratrol Ischemic stroke Mitophagy Parkin

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Structure-Based Robust Fractal Graph Neural Network with Molecular Fingerprint BERT for Molecular Property Prediction

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Dong, Yaguo Xu, Meiling Tang, Lixin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

Accurate molecular property prediction is of great importance for AI-based drug design and bioinformatics. Despite recent promising progress, existing methods face challenges due to insufficient data labeling and imbalanced label distribution. Moreover, many current molecular property prediction models are based on 2D molecular graphs, neglecting the 3D characteristics of molecules. Therefore, in this paper, we propose the structure-based robust fractal graph neural network with Molecular Fingerprint Bidirectional Encoder Representations from Transformers (MFBERT) model for molecular property prediction. We construct the pre-trained MFBERT model to explore the use of unlabeled data and obtain representations from molecular extended connectivity fingerprints as the node features of molecular graphs. Then we design the fractal graph neural network that incorporates molecular 3D conformations while ensuring equivariance to molecular rotations and translations. A 3D feature extraction module and a coordinate update module are proposed to enhance the model's ability to capture molecular structural features. Additionally, weighted-gradient distributionally robust optimization is combined with the gradient descent algorithm to update model parameters, which can mitigate issues stemming from imbalanced label distribution and improve the robustness of our model. We conducted experiments on seven datasets from MoleculeNet, comprising three regression tasks and four classification tasks. Our method achieves the state-of-the-art performance compared to the existing models on all these datasets, which demonstrates its effectiveness for molecular property prediction. Additionally, we conducted ablation experiments on our designed modules, further validating the necessity of each component in our method. © 2025 IEEE.

关键词： Graph neural networks

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Natural polyphenols as novel interventions for aging and age-related diseases: Exploring efficacy, mechanisms of action and implications for future research

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Chinese Herbal Medicines 2025年第2期17卷 279-291页

作者： Wenze Wu Yan Mi Qingqi Meng Ning Li Wei Li Pu Wang Yue Hou Liaoning Key Laboratory of Bioresource Research and Development National Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationCollege of Life and Health SciencesNortheastern University Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City School of Traditional Chinese Materia MedicaShenyang Pharmaceutical University Faculty of Pharmaceutical Sciences Toho University College of Pharmacy Shenzhen Technology University

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent years. As far as we know, the studies of natural polyphenols in aging and aging-related diseases have never been extensively reviewed. In the present paper, we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural polyphenols. The results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***, structural modifications, and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related diseases. In conclusion, natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

关键词： aging cancer cardiovascular disease natural polyphenols neurodegenerative diseases

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Superradiant growth anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

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Science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 62-81页

作者： Nayun Jia Yin-Da Guo Gui-Rong Liang Zhan-Feng Mai Xin Zhang Department of Physics Southern University of Science and TechnologyShenzhen 518055China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Institute of Frontier and Interdisciplinary Science Key Laboratory of Particle Physics and Particle Irradiation(Ministry of Education)Shandong UniversityQingdao 266237China School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Ultralight vector particles can form evolving condensates around a Kerr black hole(BH)due to superradiant *** study the effect of near-horizon reflection on the evolution of this system:by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode,we present explicit analytical expressions for the corrected spectrum and the superradiant instability ***,in high-spin BH cases,we identify an anomalous situation where the superradiance rate is temporarily increased by the reflection parameter R,which also occurs in the scalar scenario,but is largely magnified in vector condensates due to a faster growth rate in dominant *** point out that the condition for the growth anomaly in the adiabatic case is that information carried per particle exceeds a certain value δ1/δN>2πκ_(B)√(1+R)/(1-R).We further construct several featured quantities to illustrate it,and formalize the anomalyinduced gravitational wave strain deformation.

关键词： superradiance ultralight boson black hole gravitational wave near-horizon reflection

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Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows

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Physical Review D 2025年第2期111卷 024019-024019页

作者： Chun-Yu Xiong Tian-Yang Sun Jing-Fei Zhang Xin Zhang Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang 110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China

Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as “glitches,” which greatly impact the search and identification of GWs. Recent research has achieved remarkable results in data denoising, often using effective modeling methods to remove glitches. However, for glitches from uncertain or unknown sources, current methods cannot completely eliminate them from the GW signal. In this work, we leverage the inherent robustness of machine learning to obtain reliable posterior parameter distributions directly from GW data contaminated by glitches. Our network model provides reasonable and rapid parameter inference even in the presence of glitches, without needing to remove them. We also investigate various factors affecting the rationality of parameter inference in our normalizing flow network, including glitch and GW parameters. The results demonstrate that the normalizing flow can reasonably infer the source parameters of GWs even with unknown contamination. We find that the nature of the glitch itself is the only factor that can affect the rationality of the inferred results. With improvements to our model, we anticipate accelerating the localization of electromagnetic counterparts and providing priors for more accurate deglitching, thereby speeding up subsequent data processing procedures.

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