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DEGENERATING HYPERBOLIC SURFACES anD SPECTRAL GAPS FOR LARGE GENUS

anALYSIS & PDE 2024年第4期17卷 1377-1395页

作者： Wu, Yunhui Zhang, Haohao Zhu, xuwen Tsinghua Univ Yau Math Sci Ctr Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Northeastern Univ Dept Math Boston MA USA

We study the differences of two consecutive eigenvalues lambda i - lambda i - 1 , i up to 2 g - 2, for the Laplacian on hyperbolic surfaces of genus g , and show that the supremum of such spectral gaps over the moduli space has infimum limit at least 4 1 as the genus goes to infinity. A min-max principle for eigenvalues on degenerating hyperbolic surfaces is also established.

关键词： spectral gaps min-max principle large genus

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Efficient Moiré pattern removal with lightweight multi-scale feature extraction

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JOURNAL OF ELECTRONIC IMAGING 2024年第2期33卷 023050-023050页

作者： Li, xuwen Gan, Min Su, JianNan Chen, GuangYong Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

In recent years, convolutional neural networks have excelled in image Moir & eacute;pattern removal, yet their high memory consumption poses challenges for resource-constrained devices. To address this, we propose the lightweight multi-scale network (LMSNet). Designing lightweight multi-scale feature extraction blocks and efficient adaptive channel fusion modules, we extend the receptive field of feature extraction and introduce lightweight convolutional decomposition. LMSNet achieves a balance between parameter numbers and reconstruction performance. Extensive experiments demonstrate that our LMSNet, with 0.77 million parameters, achieves Moir & eacute;pattern removal performance comparable to full high definition demoir & eacute;ing network (FHDe(2)Net) with 13.57 million parameters.

关键词： image demoir & eacute ing lightweight network information multi-distillation image restoration multi-scale feature extraction and fusion

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Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China

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GEODERMA 2024年 449卷

作者： Wang, Ruizhen Chen, Weitao Chen, Hao Qin, xuwen China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China China Geol Survey China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China

Fine-scale spatial distribution of soil physicochemical properties is crucial for soil quality management, agriculture planning and geotechnical engineering. Existing soil map databases are usually developed in national scale, potentially leading to issues of coarse resolution and restricted applicability in fine-scaled studies. For broad-scale area, conventional digital soil mapping methods are challenging due to the lack of representative soil profiles and their uneven distribution. This study addresses these challenges by developing a downscaling-based framework to map 11 soil physicochemical properties at a 30-m resolution. The methodology involves constructing regression models using soil properties derived from coarse national soil maps and soil-forming covariates. Predictions are subsequently refined using fine-resolution covariates. To capture fine-scale spatial variability across diverse landscapes, 30-m resolution remote sensing data, relief, climate, and spatial covariates were prepared. The fine-gridding framework employs tree-based machine learning models, particularly random forest (RF), to enhance prediction accuracy. Evaluation of the fine-gridded soil maps demonstrated commendable results, with high consistency in summary statistics, semi-variograms, and evaluation metrics compared to reference maps. Mass preservation of RF-predicted maps exhibit high performances with both concordance correlation coefficients (CCCs) and coefficients of determination (R(2)s) exceeding 0.9 across most scenarios. This study provides a robust approach for enhancing the spatial resolution of soil maps, facilitating their use in local fine-scale applications. The proposed strategy offers a valuable solution for broad-scale areas that require finer soil maps but lack sufficient qualified soil profiles.

关键词： Digital soil mapping Broad-scale Fine gridding Downscaling Machine learning

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Prediction model for coal chemical wastewater quality after catalytic ozonation process treatment based on deep learning algorithm: Performance evaluation and model comparisons

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PROCESS SAFETY anD ENVIRONMENTAL PROTECTION 2025年 197卷

作者： Qin, Yihe Yuan, Run Zhang, Xuewei He, xuwen Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China Tianjin Univ Sch Earth Syst Sci Tianjin 300072 Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing 100083 Peoples R China

In this study, we tested different models for predicting the effluent water quality of coal chemical wastewater treated by the catalytic ozonation process. The optimal model was further modified using the norm feature vector selection methods and intelligent algorithms. The average removal amount and average removal rate of COD in coal chemical wastewater treated by catalytic ozonation process were 223.62 mg/L and 57.98 %, respectively. AAO-pH, AAO-ammonia nitrogen, AAO-total phosphorus, AAO-COD, AAO-total phenol, SST-COD, SST-total phenol, and SST-turbidity were inputs for each model, while the catalytic ozonation-COD was used as the output for each model. We collected 500 samples of one year for the training, validation, and testing of various models. Compared to other models, the RF model had better predictive performance. Additionally, L1 method had a stronger optimization ability for the RF model than L2 method. Compared with BOA and SSA, IGWO had a larger contribution to improving the predictive ability. The L1-RF-IGWO model showed the best predictive ability (test set R2 of 0.8717). The present results can aid coal chemical plants achieve data-driven management of water quality prediction and monitoring, providing ideas for the construction of models for predicting water quality in engineering.

关键词： Predictive model Water quality analysis Coal chemical wastewater Catalytic ozonation

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Enhanced Photon Emission of Chemiluminescent Luminophore for Ultra-Fast and Semi-Automatic Immunoassay toward Single Molecule Detection

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anALYTICAL CHEMISTRY 2023年第20期95卷 8070-8076页

作者： Ren, Xiaoxuan Gao, xuwen Zou, Guizheng Shandong Univ Sch Chem & Chem Engn Jinan 250100 Peoples R China

Optical single molecule detection is normally achievedvia amplifyingthe total emission of photons of luminophores and is strongly anticipatedto extend the commercialized application of chemiluminescence (CL).To overcome the limited CL photons of molecule luminophores, herein,a nanocrystal (NC) luminophore self-amplified strategy is proposedto repetitively excite CL luminophores for amplifying the total CLphotons per luminophore, which can be exploited to perform CL immunoassays(CLIAs) toward single molecule detection via employing KMnO4 as the CL triggering agent and the dual-stabilizer-capped CdTe NCsas the CL luminophore. KMnO4 can oxidize the S elementfrom each stabilizer of mercaptopropionic acid (MPA) and release enoughenergy to excite the CdTe core for flash CL. The substantial MPA aroundeach CdTe core enables every CdTe luminophore to be repetitively excitedand give off amplified total CL photons in a self-enhanced way. TheCL of CdTe NCs/KMnO4 can release all photons rapidly, andthe collection of all these photons can be utilized to determine themodel analyte of thyroid-stimulating hormone antigen (TSH) with alimit of detection of 5 ag/mL (S/N = 3), which is corresponding toabout 2-4 TSH molecules in a 20 mu L sample. The wholeimmunologic operating process can be terminated within 6 min. Thisstrategy of repetitively breaking the CL reaction involving chemicalbonds within one luminophore is promising for semi-automatic as wellas fully automatic single molecule detection and extends the commercializedapplication of CL immunodiagnosis.

关键词： Elements Genetics Molecules Physical and chemical processes Quantum mechanics

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Surface Multi-Hazard Effects of Underground Coal Mining in Mountainous Regions

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REMOTE SENSING 2025年第1期17卷

作者： Tian, xuwen Yao, Xin Zhou, Zhenkai Tao, Tao Chinese Acad Geol Sci Inst Geomech Beijing 100081 Peoples R China Minist Nat Resources Key Lab Act Tecton & Geol Safety Beijing 100081 Peoples R China

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards.

关键词： coal mining InSAR surface hazards subsidence landslides

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Contamination transformation matrix mixture modeling for skewed data groups with heavy tails and scatter

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ADVanCES IN DATA anALYSIS anD CLASSIFICATION 2024年第1期18卷 85-101页

作者： Zhu, xuwen Melnykov, Yana Kolomoytseva, angelina S. Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA

Model-based clustering is a popular application of the rapidly developing area of finite mixture modeling. While there is ample work focusing on clustering multivariate data, an increasing number of advancements have been aiming at the expansion of existing theory to the matrix-variate framework. Matrix-variate Gaussian mixtures are most popular in this setting despite the potential misfit for skewed and heavy-tailed data. To overcome this lack of flexibility, a new contaminated transformation matrix mixture model is proposed. We illustrate its utility in a series of experiments on simulated data and apply to a real-life data set containing COVID-related information. The performance of the developed model is promising in all considered settings.

关键词： Cluster analysis Finite mixture model Matrix-variate distribution Skewness Transformation

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Geological Background Prototype Learning-Enhanced Network for Remote-Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS anD REMOTE SENSING 2024年 17卷 8794-8809页

作者： Ouyang, Shubing Chen, Weitao Qin, xuwen Yang, Jinzhong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Geol Survey China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan 430074 Peoples R China

Remote-sensing-based interpretation of engineering geological lithology units provides essential lithological information for engineering planning, design, and construction projects. However, dense vegetation cover can easily interfere with the lithological spectral characteristics of optical remote sensing. Utilizing known geological background prototype-guided knowledge as a guide can mitigate interpretation interference from vegetation. Hence, for the interpretation of engineering geological lithology units, we aimed to develop a novel semantic segmentation network, LSBPnet, which combines a geological background prototype-guided learning model with a U-Net-based model. The geological background prototype-guided learning model employs lithological prototype features learned from the surrounding geological units of the predicted target to identify similarities with the predicted target's features. The model encompasses three steps: inputting known background images and known labels, learning prototype features of the background information, and learning target features based on the background information prototype perception. Using the engineering geological lithology of the Yangtze River's middle reaches as the focus area, which is a region with high vegetation cover, the proposed network achieved a 13.94% increase in the overall accuracy compared with that of commonly prevalent semantic segmentation networks. Incorporating the geological background prototype-guided knowledge helped to strengthen the distinction and relationship between the target and its surrounding lithological features, thereby improving the assessment of prediction outcomes. The diverse ablation experiments further highlighted the importance of incorporating background prototype-guided knowledge.

关键词： Geology Rocks Vegetation mapping Feature extraction Remote sensing Surveys Soft sensors Deep learning engineering geological lithology units prototype learning remote sensing semantic segmentation

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Heterogeneous temporal graph powered DRL algorithm for channel allocation in Maritime IoT Systems

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COMPUTER COMMUNICATIONS 2024年 213卷 260-270页

作者： Li, Zongwang Xie, Zhuochen He, Xiaohe Liang, xuwen Chinese Acad Sci Innovat Acad Microsatellites 1 Xueyang Rd Shanghai 201304 Peoples R China Univ Chinese Acad Sci 1 Yanqihu East Rd Beijing 101408 Peoples R China

In actual maritime Internet of Things systems, the communication environment is characterized by its time-varying nature and the presence of highly heterogeneous network structures. Those attributes present considerable challenges in devising resource allocation strategies. Given the limited availability of frequency resources, designing a reasonable and flexible channel allocation strategy (CAS) is the primary task for meeting diverse and dynamic communication demands. In this paper, a heterogeneous temporal graph powered deep reinforcement learning algorithm is proposed to optimize the CAS to maximize the channel efficiency in a real-world maritime Internet of Things system. Specifically, we build relation-based heterogeneous edges to connect different types of terminal nodes and adopt a time encoding technology to capture the dynamic evolution of communication scenarios over time. The memory and public mailbox modules are constructed as the implementation entities of the information aggregation method based on the attention mechanism. In addition, we develop a corresponding heterogeneous temporal neural network to estimate the real-time resource requirements of target terminals, and subsequently learn the optimal CAS based on the deep reinforcement learning algorithm from the perspective of maximizing the cumulative channel efficiency. Simulation results prove that the proposed algorithm significantly outperforms the other state-of-the-art algorithms in terms of the channel efficiency and generalization ability.

关键词： Maritime IoT Heterogeneous temporal graph Deep reinforcement learning

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On regime changes in text data using hidden Markov model of contaminated vMF distribution

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DATA MINING anD KNOWLEDGE DISCOVERY 2024年第6期38卷 3563-3589页

作者： Zhang, Yingying Sarkar, Shuchismita Chen, Yuanyuan Zhu, xuwen Western Michigan Univ Kalamazoo MI 49008 USA Bowling Green Univ Bowling Green OH 43402 USA Univ Alabama Tuscaloosa AL 35487 USA

This paper presents a novel methodology for analyzing temporal directional data with scatter and heavy tails. A hidden Markov model with contaminated von Mises-Fisher emission distribution is developed. The model is implemented using forward and backward selection approach that provides additional flexibility for contaminated as well as non-contaminated data. The utility of the method for finding homogeneous time blocks (regimes) is demonstrated on several experimental settings and two real-life text data sets containing presidential addresses and corporate financial statements respectively.

关键词： Finite mixture model Model-based clustering Hidden Markov model Von-Mises Fisher distribution Regime change EM algorithm

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