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SSRN 2022年

作者： Li, Jianbo Meng, Delong Wang, Xizhuo Lu, Zijing Zhang, Cui Peng, Yonghe Arauz Lara, Bernardo José Luis Song, Shaoxian Xia, Ling Hubei Key Laboratory of Mineral Resources Processing and Environment Luoshi Road 122 Hubei Wuhan430070 China Instituto de Física Universidad Autonoma de San Luis Potosí Av. Manuel Nava 6 S.L.P. San Luis Potosí78290 Mexico School of Resources and Environmental Engineering Wuhan University of Technology Luoshi Road 122 Hubei Wuhan430070 China School of Minerals Processing and Bioengineering Central South University Changsha410083 China Rizhao Big Data Research Institute of Geology and Geographic Information Shandong Rizhao276826 China Wuhan University of Technology China

Indigenous microorganisms can affect sedimentation by polyacrylamide biodegradation in the coal slurry system;however, they have never been examined in detail. In this study, the microbial community composition in coal slurry was explored using 16S rDNA, and the structural stability and functional prediction of the microbial community were analyzed. In the coal slurry, Bacteroidetes and Candidatus Nitrososphaera were the dominant strains at the phylum and genus levels. They maintained the stability of the microbial community by inhibiting the growth of other microorganisms. Based on the apparent organic compound degradation function of the microbial community, the functional bacteria DBY was isolated from the coal slurry and identified as Sphingomonas, which was used to biodegrade polyacrylamide flocculated in coal slurry. The biodegradation rate reached 66.4% within 76 h. In the biodegradation process, the high molecular weight polyacrylamide was degraded into smaller molecules by the bacteria DBY, and the sedimentation of the coal slurry was inhibited. © 2022, The Authors. All rights reserved.

关键词： Sedimentation

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Control of epileptiform waves in a neural mass model

Control of epileptiform waves in a neural mass model

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第三十八届中国控制会议

作者： Wei Wei Xiaofang Wei Min Zuo School of Computer and Information Engineering Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University

In order to achieve desired regulation,a regulation algorithm should be independent of the epileptic model and robust enough to *** this paper,by combining the advantages of active disturbance rejection control(ADRC)and iterative learning control(ILC),an active disturbance rejection iterative learning control(ADRILC)is designed to suppress the high amplitude epileptiform waves generated by Jansen's neural mass model(NMM).Simulation results show that,compared with ADRC,better performance can be obtained based on a more desired regulation signal generated by *** may provide a promising closed-loop regulation way for epilepsy in clinics.

关键词： Epilepsy active disturbance rejection control iterative learning control neural mass model closed-loop regulation

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Research on Automatic Proofreading Method of Sensitive Information in Content Security

Research on Automatic Proofreading Method of Sensitive Infor...

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第二届计算智能、工程与技术国际研讨会

作者： YonggangGong YuyingLi XiaoqinLian JunyingFu School of Computer and Information Engineering Beijing Technology and Business UniversityBeijing Key Laboratory of Food Safety Big Data Technology

Aiming at the problem of automatic proofreading of sensitive information in mass text content, an automatic proofreading method based on the combination of rule and SVM(Support Vector Machine) is proposed. To classify sensitive information based on important sensitive information provided in the "Newly Prohibited Texts and Cautions in Xinhua News Reports"(newest revision) and related central and online *** to the different categories, the paper constructs the classification processing rule base, designs the corresponding rules automatic Processing algorithm, and realizes the sensitive information automatic proofreading, At the same time, using the SVM model to analyze the result of the rule processing with emotion, which greatly reduces the false alarm rate. The test result shows that the recall rate of method is 89.98%, the accuracy rate is 98.31%, and 100,000 + text content is processed per second, which solves the key difficult problems in the practical engineering application.

关键词： Research on Automatic Proofreading Method of Sensitive Information in Content Security

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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Concatenated Attention Neural Network for Image Restoration

arXiv

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arXiv 2020年

作者： Tian, YingJie Wang, YiQi Yang, LinRui Qi, ZhiQuan School of Economics and Management University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science Technology University of Chinese Academy of Sciences Beijing China Sino-Danish College University of Chinese Academy of Sciences China

In this paper, we present a general framework for low-levelvision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this paper are as follows: First, by applying concise but effective concatenation and feature selection mechanism, we establish a novel connection mechanism which connect different modules in the modules stacking network. Second, both pixel-wise and channel-wise attention mechanisms are used in each module convolution layer, which promotes further extraction of more essential information in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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An Optimization-Based Truth Discovery Method with Claim Relation

An Optimization-Based Truth Discovery Method with Claim Rela...

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IEEE International Conference on big knowledge (ICBK)

作者： Jiazhu Xia Ying He Yuxin Jin Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.

关键词： Task analysis knowledge engineering big data Crowdsourcing Reliability engineering Meteorology

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Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition

arXiv

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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

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Cannabinoid overrides triggers of GABAergic plasticity in vestibular circuits and distorts the development of navigation

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iScience 2025年第6期28卷 112566页

作者： Shi, Wei Wu, Kenneth Lap-Kei Yang, Mengliu De Nogueira Botelho, Francisco Paulo Chua, Oscar Wing-Ho Hu, Hui-Jing Ng, Ka-Pak Lam, Ulysses Tsz-Fung Tam, Kin-Wai Ma, Chun-Wai Shum, Daisy Kwok-Yan Chan, Ying-Shing School of Biomedical Sciences Li Ka Shing Faculty of Medicine The University of Hong Kong Pokfulam Hong Kong SAR China State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Pokfulam Hong Kong SAR China Neuroscience Research Centre The University of Hong Kong Pokfulam Hong Kong SAR China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering and Medicine Beihang University Beijing China

Early life exposure to cannabis can result in long-lasting deficits in spatial navigation. We ask if the development of this behavior is subject to early life activity of type I cannabinoid receptor (CB1R) in the vestibular nucleus. In rodents, we found that local exposure to CB1R agonist within the first postnatal week, but not thereafter, led to a decline in the induction efficacy of long-term depression at GABAergic synapses (LTD_GABA), a key step in the hard-wiring of vestibular circuits. Within this critical period, endocannabinoid-mediated LTD_GABA at inhibitory neurons was selectively triggered by cholecystokinin, whereas that at excitatory neurons was by serotonin. Neonatal exposure to cannabinoids extended the phase of high GABAergic synaptic plasticity and overrode the synapse-specific, modulatory mechanism for plasticity. Such treatment delayed the postnatal emergence of vestibular-dependent reflexes and deranged adult navigational behavior. Deficits in higher functions are thus attributable to the maldevelopment of sensory processing circuits resulting from early cannabis exposure. © 2025 The Authors

关键词： Biological sciences Natural sciences Neuroscience

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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables

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International Journal of Applied Earth Observation and Geoinformation 2025年 140卷

作者： Li, Mingqi Wang, Pengxin Tansey, Kevin Zhang, Yue Guo, Fengwei Liu, Junming Li, Hongmei College of Information and Electrical Engineering China Agricultural University Beijing 100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing 100083 China School of Geography Geology and the Environment University of Leicester Leicester LE1 7RH United Kingdom College of Land Science and Technology China Agricultural University Beijing 100193 China Shaanxi Provincial Meteorological Bureau Xi'an 710014 China

Accurate crop yield estimation enables informed decisions that support efficient and sustainable food production systems. Despite some success in crop yield estimation using deep learning models, they are often referred to as “black boxes” due to their lack of interpretability. Meanwhile, most current models are designed to provide yield estimations without assessing the uncertainty and verifying the contribution of components within the models. This work developed a novel deep learning approach to estimate winter wheat yield in the Guanzhong Plain, PR China by using three remotely sensed indices, vegetation temperature condition index (VTCI), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) during main growth stages of winter wheat. The attention mechanism (AM) and an interpretable attribution method, backpropagation-based integrated gradients (IG), were incorporated into the deep learning approach to enhance interpretability. Additionally, to address uncertainty limitations the Monte Carlo (MC) dropout was applied to the deep learning approach to assess the uncertainty over time during data accumulation. The proposed approach (AM-CNN-LSTM) combined a one-dimensional convolutional neural network (1D-CNN) to capture local dependencies in sequences, the temporal data processing capability of long short-term memory (LSTM), and the interpretability of the AM. The AM-CNN-LSTM model had enhanced precision in yield estimation (R² = 0.64, RMSE = 498.08 kg/ha) compared with the CNN, AM-CNN, and CNN-LSTM. The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. The results of IG showed that the FPAR at the jointing and heading-filling stages, LAI at the heading-filling and milk maturity stages, and VTCI at the jointing stage contributed more to the yield. The MC dropout results showed that the level of model uncertainty decreased steadily as time advanced from la

关键词： Deep learning Interpretability Uncertainty Vegetation temperature condition index (VTCI) Yield estimation

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Ensemble Classification Method Based on Truth Discovery

Ensemble Classification Method Based on Truth Discovery

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IEEE International Conference on big knowledge (ICBK)

作者： Yuxin Jin Ze Yang Ying He Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

Classification is a hot topic in such fields as machine learning and data mining. The traditional approach of machine learning is to find a classifier closest to the real classification function, while ensemble classification is to integrate the results of base classifiers, then make an overall prediction. Compared to using a single classifier, ensemble classification can significantly improve the generalization of the learning system in most cases. However, the existing ensemble classification methods rarely consider the weight of the classifier, and there are few methods to consider updating the weights dynamically. In this paper, we are inspired by the idea of truth discovery and propose a new ensemble classification method based on the truth discovery (named ECTD). As far as we know, we are the first to apply the idea of truth discovery in the field of ensemble learning. Experimental results demonstrate that the proposed method performs well in ensemble classification.

关键词： Reliability Machine learning Classification algorithms Task analysis Buildings Computational modeling knowledge engineering

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