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Sensitivity Analysis of Vegetation Coverage to Soil Loss Rate in Red Soil Region of South China

IOP Conference Series: Earth and Environmental Science 2020年第1期502卷

作者： Liu Yifeng Wang Xiaoqin Chen Miaojin Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Centre of Satellite Geospatial Information Technology Fuzhou University the Academy of Digital China (Fujian) Fuzhou 350108 China.

Vegetation is an important basic component of terrestrial ecosystem and one of the most sensitive factors affecting soil erosion. How to analyse the quantitative relationship between fractional vegetation cover (FVC) and soil erosion and determine the sensitive coverage of erosion is of great significance for guiding government departments to implement soil and water conservation policies. In this paper, through the variable-controlling mothed, the forest land, grassland and bare land in Changting County, a typical red soil region in southern China, were used as experimental areas to study the sensitivity of FVC change to soil erosion. The soil erosion amount was calculated by RUSLE model, and soil loss rate (rsl) were used to characterize soil erosion. The results show that when FVC changes from 0 to 0.8, the fitting curves rsl are in accordance with the logistic model distributions, with a high coefficient of determination R2, and the sensitivity of rsl is less sensitive in the range of 0~0.05, it is most sensitive when FVC reaches a certain level, and there is basically no sensitivity when it reaches 0.7 or above; the fitting curves of rsl at different slope levels are similar to their overall distribution, which are consistent with the logistic models, and R2 is above 0.99, the curves of rsl at each slope level are different in the same FVC interval, for areas with poor vegetation coverage (0~0.2) and high slope (>25°), the curve is less sensitive to terrain; rsl generally has higher sensitivity in the range of 0.2~0.6 coverage; in areas above 0.6, the sensitivity decreases to zero.

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Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

arXiv

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arXiv 2024年

作者： Wu, Xin Huang, Zhanchao Wang, Li Chanussot, Jocelyn Tian, Jiaojiao The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Spatial Data Mining and Information Sharing The Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China The National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Remote Sensing Technology Institute German Aerospace Center Weßling82205 Germany

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet. The codes of this work are available openly at https://***/Shank2358/MuDet. © 2024, CC BY-NC-SA.

关键词： Remote sensing

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Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

arXiv

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arXiv 2023年

作者： Xiao, Yubin Wang, Di Li, Boyang Chen, Huanhuan Pang, Wei Wu, Xuan Li, Hao Xu, Dong Liang, Yanchun Zhou, You The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of Computer Science University of Science & Technology of China Hefei230026 China The School of Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom College of Computer National University of Defense Technology Hunan Changsha410073 China The Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia United States The School of Computer Science Zhuhai College of Science and Technology Zhuhai519000 China

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because as shown in the literature, they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSPs demonstrate that NAR4TSP outperforms five state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Traveling salesman problem

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Large-Scale Tensorized Multi-View Kernel Subspace Clustering

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Guang-Yu Zhang Dong Huang Chang-Dong Wang College of Mathematics and Informatics South China Agricultural University China School of Computer Science and Engineering Sun Yat-sen University Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangdong Key Laboratory of Big Data Analysis and Processing Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.

关键词： data clustering Multi-view clustering Large-scale clustering Tensorized kernel subspace clustering Inter-view and intra-view awareness

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A Multi-Perspective Exploration of the Salinization Mechanisms of Groundwater in the Guanzhong Basin, China

SSRN

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SSRN 2024年

作者： Mu, Dawei Li, Peiyue De Baets, Bernard Li, Dongfeng Li, Zhaoyu He, Song School of Water and Environment Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region The Ministry of Education Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions The Ministry of Water Resources Chang’an University No.126 Yanta Road Shaanxi Xi’an710054 China KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 Ghent9000 Belgium PowerChina Sinohydro Bureau 3 Co. Ltd. No. 4069 Expo Avenue Chanba Ecological District Shaanxi Xi’an710024 China PowerChina Northwest Engineering Corporation Limited No. 18 Zhangbadong Road Shaanxi Xi’an710065 China

To determine the mechanism responsible for the saline groundwater enrichment in different regions of the Guanzhong Basin, China, and to reveal the factors influencing salinization, this study employed multivariate statistical methods, hydrochemical analysis, isotope studies, and hydrochemical modeling. The results indicate significant regional variations in total dissolved solids (TDS), with concentrations exceeding 1000 mg/L predominantly occurring north of the Weihe River and east of the Jinghe River. The correlations of groundwater chloride (Cl) with Cl/Br molar ratio and stable isotopes show that groundwater salinity in the Guanzhong Basin is mainly controlled by mineral dissolution, evaporation, and concentration. In addition, human activities, such as vertical irrigation recharge and excessive fertilizer use, exacerbate local salinity levels. Irrigation activities worsen the shallow groundwater salt enrichment in the runoff zone of the Central Basin, revealed by the high salinity (> 3000 mg/L), high Cl/Br ratios (> 2000), moderate δ2H (-57.5 to -67.5‰) and moderate δ18O (-8.1 to -8.9‰). High salinity (> 1000 mg/L), high nitrate concentration (> 100 mg/L), and moderate Cl (100 to 500 mg/L) indicate the impact of excessive fertilizer use. It is worth noting that intensive groundwater withdrawal disrupts the dynamic balance within the aquifer, causing shallow high-saline groundwater to percolate downward, thereby increasing the risk of deep groundwater pollution. The research enhances the understanding of groundwater salinity transport and provides insights into the effects of groundwater salinization in the irrigation area. © 2024, The Authors. All rights reserved.

关键词： Groundwater pollution

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Boosting Weak-to-Strong Agents in Multiagent Reinforcement Learning via Balanced PPO

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第5期PP卷 9136-9149页

作者： Huang, Sili Chen, Hechang Piao, Haiyin Sun, Zhixiao Chang, Yi Sun, Lichao Yang, Bo Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education School of Artificial Intelligence Changchun 130012 China Jilin University School of Artificial Intelligence Changchun 130012 China Northwestern Polytechnical University Unmanned System Research Institute Xi’an 710068 China Lehigh University Department of Computer Science and Engineering Bethlehem 18015 PA United States Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education School of Computer Science and Technology Changchun 130012 China

Multiagent policy gradients (MAPGs), an essential branch of reinforcement learning (RL), have made great progress in both industry and academia. However, existing models do not pay attention to the inadequate training of individual policies, thus limiting the overall performance. We verify the existence of imbalanced training in multiagent tasks and formally define it as an imbalance between policies (IBPs). To address the IBP issue, we propose a dynamic policy balance (DPB) model to balance the learning of each policy by dynamically reweighting the training samples. In addition, current methods for better performance strengthen the exploration of all policies, which leads to disregarding the training differences in the team and reducing learning efficiency. To overcome this drawback, we derive a technique named weighted entropy regularization (WER), a team-level exploration with additional incentives for individuals who exceed the team. DPB and WER are evaluated in homogeneous and heterogeneous tasks, effectively alleviating the imbalanced training problem and improving exploration efficiency. Furthermore, the experimental results show that our models can outperform the state-of-the-art MAPG methods and boast over 12.1 % performance gain on *** policy gradients (MAPGs), an essential branch of reinforcement learning (RL), have made great progress in both industry and academia. However, existing models do not pay attention to the inadequate training of individual policies, thus limiting the overall performance. We verify the existence of imbalanced training in multiagent tasks and formally define it as an imbalance between policies (IBPs). To address the IBP issue, we propose a dynamic policy balance (DPB) model to balance the learning of each policy by dynamically reweighting the training samples. In addition, current methods for better performance strengthen the exploration of all policies, which leads to disregarding the training differences in the

关键词： Dynamic policy balance (DPB) imbalance between policies (IBPs) multiagent system reinforcement learning (RL) weighted entropy regularization (WER)

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Neutrino Rocket Jet Model: An Explanation of High-velocity Pulsars and Their Spindown Evolution

arXiv

引用

arXiv 2022年

作者： Li, Zheng Peng, Qiu-He Kang, Miao Liu, Xiang Zhang, Ming Huang, Yong-Feng Chou, Chih-Kang Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi830011 China Key Laboratory for Radio Astronomy Chinese Academy of Sciences 2 West Beijing Road Nanjing210008 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing100049 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Key Laboratory of Radio Astrophysics in Xinjiang Province Urumqi830011 China School of Physics and Electronics Henan University Kaifeng China Ministry of Education Nanjing210023 China National Astronomical Observatories Chinese Academy of Sciences Beijing China

The fact that the spatial velocity of pulsars is generally higher than that of their progenitor stars has bothered astronomers for nearly 50 years. It has been extensively argued that the high pulsar velocity should be acquired during a natal kick process on a timescale of 100 ms-10 s in the supernova explosion, in which some asymmetrical dynamical mechanism plays a key role. However, a satisfactory picture generally is still lacking. In this study, it is argued that the neutrino rocket model can well account for the high speed as well as the long-term evolution behaviors of pulsars. The neutrinos are emitted from superfluid vortex neutrons through the neutrino cyclotron radiation mechanism. The unique characters of left-handed neutrinos and right-handed antineutrinos resulting from the nonconservation of parity in weak interactions play a major role in the spatial asymmetry. The continuous acceleration of pulsars can be naturally explained by this model, which yields a maximum velocity surpassing 1000 kms-1. The alignment between the spinning axis and the direction of motion observed for the Crab pulsar (PSR 0531) and the Vela pulsar (PSR 0833) can be well accounted for. The observed correlation between the spin-down rate and the period of long-period pulsars with P0.5 s can also be satisfactorily explained. Copyright © 2022, The Authors. All rights reserved.

关键词： Pulsars

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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

arXiv

引用

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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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

引用

Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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