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4,298 results on '"Li, Mengmeng"'

1. Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives

Author

Ma, Lei, Yan, Ziyun, Li, Mengmeng, Liu, Tao, Tan, Liqin, Wang, Xuan, He, Weiqiang, Wang, Ruikun, He, Guangjun, Lu, Heng, and Blaschke, Thomas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep learning has gained significant attention in remote sensing, especially in pixel- or patch-level applications. Despite initial attempts to integrate deep learning into object-based image analysis (OBIA), its full potential remains largely unexplored. In this article, as OBIA usage becomes more widespread, we conducted a comprehensive review and expansion of its task subdomains, with or without the integration of deep learning. Furthermore, we have identified and summarized five prevailing strategies to address the challenge of deep learning's limitations in directly processing unstructured object data within OBIA, and this review also recommends some important future research directions. Our goal with these endeavors is to inspire more exploration in this fascinating yet overlooked area and facilitate the integration of deep learning into OBIA processing workflows.

Published
2024

2. Center Manifolds for Non-instantaneous Impulsive Equations Under Nonuniform Hyperbolicity

Author

Li, Mengmeng, Wang, JinRong, O’Regan, Donal, and Fečkan, Michal

Subjects
Mathematics, QA1-939
Abstract

In this paper, we establish the existence of smooth center manifolds for a class of nonautonomous differential equations with non-instantaneous impulses under sufficiently small perturbations of the linear homogeneous part which has a nonuniform exponential trichotomy. In addition, we show the $C^{1}$ smoothness of center manifolds outside the jumping times.

Published
2020
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3. TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit

Author

Hu, Pignge, Zhang, Xiaoteng, Li, Mengmeng, Zhu, Yingjie, and Shi, Li

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and its Retina-OT-Rt visual circuit is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical modeling based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each layer corresponding to neurons in the visual pathway. The Retina layer is responsible for accurately projecting input content, the SGC dendritic layer perceives and encodes spatial-temporal information, the SGC Soma layer computes complex motion information and extracts small objects, and the Rt layer integrates and decodes motion information from multiple directions to determine the position of small objects. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.

Published
2024

8. A Large Deviations Perspective on Policy Gradient Algorithms

Author

Jongeneel, Wouter, Kuhn, Daniel, and Li, Mengmeng

Subjects
Mathematics - Optimization and Control, Statistics - Machine Learning, 60F10, 90C26
Abstract

Motivated by policy gradient methods in the context of reinforcement learning, we identify a large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-{\L}ojasiewicz condition. Leveraging the contraction principle from large deviations theory, we illustrate the potential of this result by showing how convergence properties of policy gradient with a softmax parametrization and an entropy regularized objective can be naturally extended to a wide spectrum of other policy parametrizations., Comment: v3; comments are welcome

Published
2023

14. Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets

Author

Li, Mengmeng, Kuhn, Daniel, and Sutter, Tobias

Subjects
Mathematics - Optimization and Control, Computer Science - Machine Learning, 90C17, 90C26
Abstract

We propose policy gradient algorithms for robust infinite-horizon Markov decision processes (MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the robust MDP literature. Indeed, uncertainty sets that display statistical optimality properties and make optimal use of limited data often fail to be rectangular. Unfortunately, the corresponding robust MDPs cannot be solved with dynamic programming techniques and are in fact provably intractable. We first present a randomized projected Langevin dynamics algorithm that solves the robust policy evaluation problem to global optimality but is inefficient. We also propose a deterministic policy gradient method that is efficient but solves the robust policy evaluation problem only approximately, and we prove that the approximation error scales with a new measure of non-rectangularity of the uncertainty set. Finally, we describe an actor-critic algorithm that finds an $\epsilon$-optimal solution for the robust policy improvement problem in $\mathcal{O}(1/\epsilon^4)$ iterations. We thus present the first complete solution scheme for robust MDPs with non-rectangular uncertainty sets offering global optimality guarantees. Numerical experiments show that our algorithms compare favorably against state-of-the-art methods., Comment: comments are welcome

Published
2023

15. Approximate Query Processing Based on Approximate Materialized View

Author

Wu, Yuhan, Guo, Haifeng, Yang, Donghua, Li, Mengmeng, Zheng, Bo, Wang, Hongzhi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tari, Zahir, editor, Li, Keqiu, editor, and Wu, Hongyi, editor

Published
2024
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16. Schema Integration on Massive Data Sources

Author

Li, Tianbao, Guo, Haifeng, Yang, Donghua, Li, Mengmeng, Zheng, Bo, Wang, Hongzhi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tari, Zahir, editor, Li, Keqiu, editor, and Wu, Hongyi, editor

Published
2024
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17. Optimization of Stamping Process Parameters for Small Curved Beam Parts of Rail Train Based on GA-PSO-BP Algorithm

Author

Ji, Hongchao, Li, Mengmeng, Yao, Ran, Pei, Weichi, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Mocellin, Katia, editor, Bouchard, Pierre-Olivier, editor, Bigot, Régis, editor, and Balan, Tudor, editor

Published
2024
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38. Measuring Chinese Rural Science Teachers' Self-Perception towards Teaching Competencies: Instrument Development

Author

Li, Mengmeng, Cai, Le, Chen, Dimei, and Ye, Jianqiang

Abstract

The teaching competency of rural teachers is fundamental to providing rural pupils with a high-quality education. Nonetheless, China has not yet established standardized competence criteria for rural science teachers, making it difficult to ascertain the actual competence of the rural teaching force on the front lines. Consequently, this study aimed at accessing Chinese rural science teachers' teaching competency and exploring rural-urban differences by developing a measurement instrument. Factor analyses confirmed the reliability and validity of this instrument, which consisted of 21 items and four dimensions. In addition, the questionnaire was employed to examine 393 science teachers in China, discovering the significant rural-urban differences in terms of teaching implementation and learning evaluation together with teacher distribution. This study was hoped to promote the building of the teaching competency system and provide references for rural science teachers' professional development.

Published
2022

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