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arXiv 2025年

作者： Pan, Hanlin Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following

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Journal of Bionic engineering 2021年第6期18卷 1439-1451页

作者： Tianjun Sun Zhenhai Gao Zhiyong Chang Kehan Zhao State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun130022China College of Automotive Engineering Jilin UniversityChangchun130022China College of Biological and Agricultural Engineering Jilin UniversityChangchun130022China College of Materials Science and Engineering Jilin UniversityChangchun130022China

The anthropomorphic intelligence of autonomous driving has been a research hotspot in the ***,current studies have not been able to reveal the mechanism of drivers'natural driving ***,this thesis starts from the perspective of cognitive decision-making in the human brain,which is inspired by the regulation of dopamine feedback in the basal ganglia,and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the *** this thesis,first,a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism;second,the above mechanism was transformed into a reinforcement Q-learning model,so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following;finally,the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.

关键词： Brain-like intelligent decision-making Dopamine in basal ganglia Reinforcement learning Longitudinal autonomous driving

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Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection 39

Reconsidering Feature Structure Information and Latent Space...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

关键词： Labeled data

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Impact of reservoir squeezing on quantum-memory-assisted entropic uncertainty relations

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Physical Review A 2025年第3期111卷 032418-032418页

作者： Hamad Ali Abdul Basit Peng-Bo Li Ministry of Education Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices School of Physics Xi'an Jiaotong University Xi'an 710049 China Quantum Information Quantum Simulation and Quantum Processing Research Group Research Institute for Science and Engineering University of Sharjah Sharjah 27272 United Arab Emirates

The entropic uncertainty principle imposes a limit on how precisely two incompatible observables can be measured, which can be reduced when quantum memory is involved. We theoretically investigate the dynamics of the quantum-memory-assisted entropic uncertainty relation when either quantum memory alone or both the measured particle and the quantum memory interact with a squeezed-vacuum bath through energy-preserving interactions. We show that in both scenarios, the squeezing phase of the reservoir decreases the entropic uncertainty (EU), whereas the squeezing strength increases the EU. In addition, we find that EU can be significantly reduced when both the measured particle and quantum memory are subjected to a common squeezed-vacuum bath. Furthermore, we explore the key factors controlling the behavior of EU by exploring the competition between quantum discord and minimal missing information. Moreover, we examine the tightness of the entropic uncertainty bound (EUB) in both system-environment setups and observe that the EUB is less tight when both the measured particle and quantum memory are coupled to a common squeezed-vacuum bath.

关键词： Quantum computers

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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection 29

A Coarse-to-fine Cascaded Evidence-Distillation Neural Netwo...

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29th International Conference on computational Linguistics, COLING 2022

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives. © 2022 Proceedings - International Conference on computational Linguistics, COLING. All rights reserved.

关键词： Distillation

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Conversion of biochar into sulfonate-bearing solid acids used for the hydrolysis of tylosin:the effect of aromaticity and degree of condensation

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Biochar 2023年第1期5卷 1514-1526页

作者： Qianqian Xie Xiao Yang Binoy Sarkar Xiaomin Dou Piumi Amasha Withana Yong Sik Ok College of Environmental Science and Engineering Beijing Forestry UniversityBeijing 100083People’s Republic of China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101People’s Republic of China Future Industries Institute University of South AustraliaMawson LakesSA 5095Australia Korea Biochar Research Center APRU Sustainable Waste Management Program&Division of Environmental Science and Ecological EngineeringKorea UniversitySeoul 02841Republic of Korea International ESG Association(IESGA) Seoul 06621Republic of Korea

In the last few decades,sulfonated carbon materials have garnered significant attention as Brøsted solid acid *** sulfonation process and catalytic activity of sulfonated biochar can be influenced by the aromaticity and degree of condensation exhibited by ***,the relationships between the aromaticity,sulfonating ability,and resultant catalytic activity are not fully *** this study,biochar samples pyrolyzed at 300-650℃ exhibiting different aromaticity and degrees of condensation were sulfonated and employed as sulfonate-bearing solid catalysts for hydrolytically removing *** exhibited excellent hydrolytic performance and their kinetic constants were positively correlated with the total acidity and negatively correlated with their *** study has uncovered the relationship between the structure,properties,sulfonating ability,and subsequent hydrolytic performance of biochar *** was observed that the aromaticity of biochar decreased as the pyrolysis temperature *** pyrolysis temperatures resulted in a reduced degree of condensation,smaller ring size,and an increased number of ring edge sites available for sulfonation,ultimately leading to enhanced catalytic *** findings provide valuable insights into the fundamental chemistry behind sulfonation upgrading of biochar,with the aim of developing functional catalysts for mitigating antibiotics in contaminated water.

关键词： Biochar upgrading Sulfonation Hydrolytic mitigation Tylosin removal Biochar-based solid acids

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A fast finite difference/finite element method for the two-dimensional distributed-order time-space fractional reaction-diffusion equation

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International Journal of Modeling, simulation, and Scientific Computing 2020年第2期11卷 115-132页

作者： Yaping Zhang Jiliang Cao Weiping Bu Aiguo Xiao School of Mathematics and Computational Science and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University 411105 HunanP.R.China College of Science Shaoyang University 422000 ShaoyangP.R.China

In this work,we develop a finite difference/finite element method for the two-dimensional distributed-order time-space fractional reaction-diffusion equation(2D-DOTSFRDE)with low regularity solution at the initial time.A fast evaluation of the distributedorder time fractional derivative based on graded time mesh is obtained by substituting the weak singular kernel for the *** stability and convergence of the developed semi-discrete scheme to 2D-DOTSFRDE are *** the spatial approximation,the finite element method is *** convergence of the corresponding fully discrete scheme is ***,some numerical tests are given to verify the obtained theoretical results and to demonstrate the effectiveness of the method.

关键词： Distributed-order fractional derivative fractional reaction-diffusion equation fast evaluation graded mesh finite element method error estimate

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WHO IS YOUR RIGHT MIXUP PARTNER IN POSITIVE AND UNLABELED LEARNING 10

WHO IS YOUR RIGHT MIXUP PARTNER IN POSITIVE AND UNLABELED LE...

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10th International Conference on Learning Representations, ICLR 2022

作者： Li, Changchun Li, Ximing Feng, Lei Ouyang, Jihong College of Computer Science and Technology Jilin University China College of Computer Science Chongqing University China Imperfect Information Learning Team RIKEN Center for Advanced Intelligence Project Japan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Positive and Unlabeled (PU) learning targets inducing a binary classifier from weak training datasets of positive and unlabeled instances, which arise in many real-world applications. In this paper, we propose a novel PU learning method, namely Positive and unlabeled learning with Partially Positive Mixup (P3Mix), which simultaneously benefits from data augmentation and supervision correction with a heuristic mixup technique. To be specific, we take inspiration from the decision boundary deviation phenomenon observed in our preliminary experiments, where the learned PU boundary tends to deviate from the fully supervised boundary towards the positive side. For the unlabeled instances with ambiguous predictive results, we select their mixup partners from the positive instances around the learned PU boundary, so as to transform them into augmented instances near to the boundary yet with more precise supervision. Accordingly, those augmented instances may push the learned PU boundary towards the fully supervised boundary, thereby improving the classification performance. Comprehensive experimental results demonstrate the effectiveness of the heuristic mixup technique in PU learning and show that P3Mix can consistently outperform the state-of-the-art PU learning methods. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Heuristic methods

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Cost-Effective Mapping of Hyperlocal Air Pollution Using Large-Scale Mobile Monitoring and Land-Use Machine Learning

ACS ES&T Air

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ACS ES&T Air 2025年第2期2卷 151-161页

作者： Tie Zheng Yifan Wen Sheng Xiang Pan Yang Xuan Zheng Yan You Shaojun Zhang Ye Wu School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China State Key Laboratory of Pollution Control and Resource Reuse Tongji University Shanghai 200092 China；College of Environmental Science and Engineering Tongji University Shanghai 200092 China College of Chemistry and Environmental Engineering Shenzhen University Shenzhen 518060 China Macao Environmental Research Institute Macau University of Science and Technology Macau 999078 China；National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Macau University of Science and Technology Macao 999078 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China；Beijing Laboratory of Environmental Frontier Technologies Beijing 100084 China

The advent of large-scale mobile monitoring using fast-response instruments has enabled hyperlocal mapping (≤100 m) of traffic-related air pollution (TRAP), with important implications for air quality management. However, most related studies have been confined within small areas due to the high cost and labor intensity. This study pioneers a cost-effective TRAP mapping method by incorporating large-scale mobile monitoring and land-use machine learning (LUML). Here, over 4.6 million 1 Hz high-frequency measurements (∼1300 h) were collected on a part of major roadways in the Chinese megacity of Shenzhen. Unmeasured locations were estimated by LUML models to reduce measurement costs and labor intensity. Various ML algorithms and varying spatial aggregation segment lengths were incorporated to optimize the model performance. Hyperlocal maps of NO, NO2, and PM2.5were predicted across the entire road network covering over 1700 km2. Based on our results, LU-RF (random forest) for mapping NO and NO2and LU-GBM (Gradient Boosting Machine) for mapping PM2.5, demonstrated superior performance. Deep learning models, in contrast, did not yield comparable results. Finer partitioning of road segments (≤100 m) improved NO prediction performance, but worsened that for NO2and PM2.5. By deployment of optimal ML algorithms and segment lengths, the TRAP mapping accuracy increased by 20–80% compared to conventional land-use regression models. This study provides a promising and cost-effective approach to hyperlocal air pollution mapping and management in cities worldwide.

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Repercussions of COVID-19 pandemic on solid waste generation and management strategies

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Frontiers of Environmental science & engineering 2021年第6期15卷 241-258页

作者： Yangyang Liang Qingbin Song Naiqi Wu Jinhui Li Yuan Zhong Wenlei Zeng Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China Macao Environmental Research Institute Macao University of Science and TechnologyMacao 999078China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

It has been over ten months since the beginning of the 2019 coronavirus disease(COVID-2019),and its impact on solid waste management,especially medical waste,is becoming *** study systematically reviews the potential influences of the COVID-19 pandemic on medical waste,personal protection equipment waste and municipal solid waste(MSW),and discusses the corresponding measures and policies of solid waste management in typical *** results show that the generation of medical waste from the pandemic increased significantly,with 18%‒425%*** is estimated that the daily output of COVID-19 medical waste increased from 200 t/d on Feb.22 to over 29000 t/d at the end of September 2020 throughout the *** use of personal protective equipment will continue to grow in the long-term,while the blockade and isolation measures greatly reduced the volume of commercial waste,especially for tourist cities,and part of this waste was transferred to household ***’attitudes and behavior toward food waste have changed due to the COVID-19 *** response to the pandemic,international organizations and several countries have issued new policies and guidelines and adjusted their management strategies for medical waste and MSW *** pandemic has brought specific challenges to the disposal capacity of medical waste *** has also brought about the stagnation of policies related to the reduction of plastic products and waste *** study will provide some useful information for managers and governmental officials on effective solid waste management during and after the COVID-19 pandemic.

关键词： COVID-19 Medical waste Personal protective equipment Municipal solid waste Waste management

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