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Spatiotemporal water quality data reconstruction: A tensor factorization framework

Ecological Informatics 2025年 90卷

作者： Xuke Wu Kun Shan Lan Wang Jingkai Wang Mingsheng Shang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China University of Chinese Academy of Sciences Beijing 100049 China

Automatic high-frequency monitoring (AHFM) of water quality parameters has gained growing attention for managing eutrophic lakes. However, missing data in water quality datasets remains a persistent challenge, often compromising the reliability of mathematical models and statistical analyses. While traditional imputation methods fail to adequately capture complex spatiotemporal dependencies among water quality variables, this study proposes a novel nonnegative tensor factorization (NTF) model designed to reconstruct missing values by effectively modeling variable-site-time triad interactions. Previous findings indicate that incorporating bias schemes into NTF architectures substantially reduces underfitting risks. Leveraging this insight, we develop and rigorously evaluate seven distinct biased NTF variants. Their diversified bias term designs not only enhance individual model performance but also enable highly effective ensemble learning through complementary strengths. To validate the proposed models, we conduct comprehensive experiments using real-world AHFM data from Lake Dianchi, China, under various missing data scenarios (20–80 % missing ratios and 1–4 weeks missing gaps). The key water quality parameters include chlorophyll- a concentration, water temperature, pH, dissolved oxygen, electrical conductivity, turbidity, chemical oxygen demand, ammonia, total phosphorus, and total nitrogen. The results demonstrate the superiority of the seven biased NTF models, achieving optimal performance with a root mean squared error (RMSE) of 0.2796 ± 0.0041, mean absolute error (MAE) of 0.1611 ± 0.0034, and Nash-Sutcliffe efficiency (NSE) of 0.9704 ± 0.0009 across all missingness scenarios. Compared to state-of-the-art models, these methods yield consistent improvements of 3.42 %–30.74 % in RMSE, 2.30 %–30.38 % in MAE, and 0.20 %–3.22 % in NSE. Notably, an ensemble of the seven models further elevates imputation accuracy, attaining an RMSE of 0.2409 ± 0.0018, MAE of 0.1384

关键词： Water quality Missing data imputation Nonnegative tensor factorization Ensemble Bias scheme Lake Dianchi

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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Class-specific discriminability and multiscale information-based multiview feature selection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Southwest Jiaotong University Institute of Artificial Intelligence School of Computing and Artificial Intelligence Chengdu 611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu 611756 China Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 China Southwest Jiaotong University Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu 611756 China Chongqing Normal University College of Computer and Information Science Chongqing 401331 China Department of Computer Science and Information Engineering Taichung 500 41354 Taiwan

Multiview data possess different discriminability in different views, which is challenging to catch but crucial for a feature selection model. Multiscale information, which represents vertical exploration in each view, is vital for further mining traits implied in multiview data. However, most existing studies neglect these beneficial multi-granulation characteristics. This study first embeds the multiscale information into the sparse learning framework for multiview feature selection. A class-specific discriminability and multiscale information-based multiview feature selection (CDMIMFS) method is proposed. It explores the fuzzy and uncertain class-specific discriminability which is inherently discrepant in different views by the fuzzy rough set theory. It relaxes the over strict requirement for complete consistency in multiscale information systems to make a trade-off, which further enhances discriminative feature selection. An effective iteration algorithm is proposed to solve the optimization. Both the theoretical proof and experimental demonstration of convergence are provided. Comprehensive experiments are conducted on the CDMIMFS compared with state-of-the-art algorithms. Results on different evaluation metrics exhibit the advantages of the proposed method. © 1991-2012 IEEE.

关键词： Feature selection fuzzy rough set granular computing multiscale multiview

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Cosmology with fast radio bursts in the era of SKA

arXiv

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arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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HAM:a deep collaborative ranking method incorporating textual information

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Frontiers of Information Technology & Electronic Engineering 2020年第8期21卷 1206-1216页

作者： Cheng-wei WANG Teng-fei ZHOU Chen CHEN Tian-lei HU Gang CHEN The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Hangzhou 310027China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual *** is highly desirable to explore a very deep neural network to capture the complicated nonlinear ***,training a deeper recommender is not as effortless as simply adding layers.A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based ***,textual descriptions probably contain noisy word *** extracting feature vectors from them can harm the recommender’s *** overcome these difficulties,we propose a new recommendation method named the HighwAy reco Mmender(HAM).HAM explores a highway mechanism to make gradient-based training methods stable.A multi-head attention mechanism is devised to automatically denoise textual ***,a block coordinate descent method is devised to train a deep neural *** studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

关键词： Deep learning Recommendation system Highway network Block coordinate descent

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Deep-Learning Based Colorectal Cancer Pathological Analysis with Hyperspectral Light Field Microscopy

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iscience 2025年

作者： Hao Sheng Yingying Zhang Ruixuan Cong Shuai Wang Da Yang Zhenglong Cui Xuefei Huang Rongshan Chen Jiapeng Liu Wei Ke Weifeng Lv Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Beihang University 311115 Hangzhou China Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University 100191 Beijing China Faculty of Applied Sciences Macao Polytechnic University 999078 Macao China

5D hyperspectral light field (H-LF) integrates multi-angular and multi-spectral observation, offering a comprehensive opportunity to capture more detailed information from biological samples. In this paper, we integrate hyperspectral light field microscopy imaging to analyze H&E-stained whole slide images (WSIs) of colorectal cancer (CRC). Specifically, we design a triple separable transformer encoder (HLFTST) that efficiently extracts features by decoupling the 5D H-LF data into lower-dimensional components and applying self-attention for global interaction. We also introduce a text encoder-decoder to align H-LF features with language, enabling automatic cell classification and pathology report generation through a three-stage training pipeline. Experiments show our method outperforms 2D, 3D, and 4D baselines, improving precision by up to 4.88% and F1 score by 4.21% across five CRC cell categories. Additionally, it generates meaningful pathology descriptions, highlighting its potential for enhancing diagnostics and supporting personalized treatment in broader biomedical settings.

关键词： 5D hyperspectral light field Colorectal Cancer Pathological Image Analysis Pathology Report Generation

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HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

arXiv

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arXiv 2022年

作者： Ma, Minbo Xie, Peng Teng, Fei Li, Tianrui Wang, Bin Ji, Shenggong Zhang, Junbo School of Computing and Artifical Intelligence Southwest Jiaotong University Chengdu611756 China National Enginnering Laboratory of Integrated Transportation Big Data Application Technology The School of Computer Science and Technology Ocean University of China Qingdao266100 China Tencent Inc. Shenzhen China JD iCity JD Technology JD Intelligent Cities Research Beijing China

Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performance for spatio-temporal forecasting. However, the canonical GNNs-based methods only individually model the local graph of meteorological variables per station or the global graph of whole stations, lacking information interaction between meteorological variables in different stations. In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations. An adaptive graph learning layer and spatial graph convolution are employed to construct self-learning graph and study hidden dependency among nodes of variable-level and station-level graph. For capturing temporal pattern, the dilated inception as the backbone of gate temporal convolution is designed to model long and various meteorological trends. Moreover, a dynamic interaction learning is proposed to build bidirectional information passing in hierarchical graph. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method. © 2022, CC BY.

关键词： Weather forecasting

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MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges

arXiv

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arXiv 2025年

作者： Qin, Shixi Yang, Zhiyong Bao, Shilong Wang, Shi Xu, Qianqian Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper focuses on implanting multiple heterogeneous backdoor triggers in bridge-based diffusion models designed for complex and arbitrary input distributions. Existing backdoor formulations mainly address single-attack scenarios and are limited to Gaussian noise input models. To fill this gap, we propose MixBridge, a novel diffusion Schrödinger bridge (DSB) framework to cater to arbitrary input distributions (taking I2I tasks as special cases). Beyond this trait, we demonstrate that backdoor triggers can be injected into MixBridge by directly training with poisoned image pairs. This eliminates the need for the cumbersome modifications to stochastic differential equations required in previous studies, providing a flexible tool to study backdoor behavior for bridge models. However, a key question arises: can a single DSB model train multiple backdoor triggers? Unfortunately, our theory shows that when attempting this, the model ends up following the geometric mean of benign and backdoored distributions, leading to performance conflict across backdoor tasks. To overcome this, we propose a Divide-and-Merge strategy to mix different bridges, where models are independently pre-trained for each specific objective (Divide) and then integrated into a unified model (Merge). In addition, a Weight Reallocation Scheme (WRS) is also designed to enhance the stealthiness of MixBridge. Empirical studies across diverse generation tasks speak to the efficacy of MixBridge. The code is available at: https://***/qsx830/ MixBridge. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential equations

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Group-wise Inhibition based Feature Regularization for Robust Classification

Group-wise Inhibition based Feature Regularization for Robus...

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International Conference on Computer Vision (ICCV)

作者： Haozhe Liu Haoqian Wu Weicheng Xie Feng Liu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training.

关键词： Training Computer vision Codes Heuristic algorithms Robustness Classification algorithms Convolutional neural networks

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Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

Dual attention mechanism object tracking algorithm based on ...

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International Conference on Networking and Network Applications (NaNA)

作者： Sugang Ma Zixian Zhang Lei Zhang Yanping Chen Xiaobao Yang Lei Pu Zhiqiang Hou School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information Engineering Chang’an University Xi’an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information and Navigation Air Force Engineering University Xi’an Shaanxi China

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking.

关键词： Target tracking Feature extraction Real-time systems Robustness Object tracking Task analysis

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