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arXiv 2023年

作者： Cao, Jiliang Wang, Wansheng Xiao, Aiguo Department of Mathematics Shanghai Normal University Shanghai200234 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

We derive optimal order a posteriori error estimates in the L∞(L2) and L1(L2)-norms for the fully discrete approximations of time fractional parabolic differential equations. For the discretization in time, we use the L1 methods, while for the spatial discretization, we use standard conforming finite element methods. The linear and quadratic space-time reconstructions are introduced, which are generalizations of the elliptic space reconstruction. Then the related a posteriori error estimates for the linear and quadratic space-time reconstructions play key roles in deriving global and pointwise final error estimates. Numerical experiments verify and complement our theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Finite element method

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Development of an in-situ H_(2)reduction and moderate oxidation method for 3,5-dimethylpyridine hydrogenation in trickle bed reactor

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Frontiers of Chemical Science and engineering 2022年第12期16卷 1807-1817页

作者： Tao Lin Xiaoxun Ma School of Chemical Engineering Northwest UniversityXi’an 710069China Kaili Catalyst&New Materials Co. Ltd.Xi’an 710201China Shaanxi Key Laboratory of Catalytic Materials and Technology Xi’an 710201China Chemical Engineering Research Center of the Ministry of Education(MOE)for Advanced Use Technology of Shanbei Energy Xi’an 710069China Shaanxi Research Center of Engineering Technology for Clean Coal Conversion Xi’an 710069China Collaborative Innovation Center for Development of Energy and Chemical Industry in Northern Shaanxi Xi’an 710069China International Scientific and Technological Cooperation Base of the Ministry of Science and Technology(MOST)for Clean Utilization of Hydrocarbon Resources Xi’an 710069China

The Ru/C catalyst prepared by impregnation method was used for hydrogenation of 3,5-dimethylpyridine in a trickle bed *** the same reduction conditions(300°C in H_(2)),the catalytic activity of the non-in-situ reduced Ru/C-n catalyst was higher than that of the in-situ reduced Ru/C-y ***,an in-situ H_(2)reduction and moderate oxidation method was developed to increase the catalyst ***,the influence of oxidation temperature on the developed method was *** catalysts were characterized by Brunauer–Emmett–Teller method,hydrogen temperature programmed reduction H_(2)-TPR,hydrogen temperature-programmed dispersion(H_(2)-TPD),X-ray diffraction,energy dispersive spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,O2 chemisorption and oxygen temperature-programmed dispersion(O2-TPD)*** results showed that there existed an optimal Ru/RuO_(x)ratio for the catalyst,and the highest 3,5-dimethylpyridine conversion was obtained for the Ru/C-i1 catalyst prepared by in-situ H_(2)reduction and moderate oxidation(oxidized at 100°C).Excessive oxidation(200°C)resulted in a significant decrease in the Ru/RuO_(x)ratio of the in-situ H_(2)reduction and moderate oxidized Ru/C-i2 catalyst,the interaction between RuO_(x)species and the support changed,and the hard-to-reduce RuO_(x)species was formed,leading to a significant decrease in catalyst *** developed in-situ H_(2)reduction and moderate oxidation method eliminated the step of the non-in-situ reduction of catalyst outside the trickle bed reactor.

关键词： Ru C catalyst in-situ H_(2)reduction and moderate oxidation in-situ reduction non-in-situ reduction hydrogenation of 3,5-dimethylpyridine

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MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

arXiv

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arXiv 2023年

作者： Lu, Yanzuo Shen, Meng Ma, Andy J. Xie, Xiaohua Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou China

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://***/YanzuoLu/MLNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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How to Perform Energy-Balanced Underwater Data Collection in AUV-Aided UASNs: A Social Welfare-Based Node Clustering Approach

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IEEE Transactions on Computational Social Systems 2024年

作者： Lin, Chuan Han, Guangjie Lu, Chang Hussain Shah, Syed Bilal Zhang, Yu Wang, Feng Northeastern University Software College Shenyang110819 China Hohai University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Nanjing210013 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Nanjing210013 China Dalian University of Technology School of Software Dalian116024 China Dar Al-Hekma University School of Engineering Computing and Informatics Jeddah21589 Saudi Arabia Suzhou Vocational University School of Engineering Suzhou215004 China

The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors. © 2014 IEEE.

关键词： Marine applications

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Estimator Meets Equilibrium Perspective: A Rectified Straight Through Estimator for Binary Neural Networks Training

Estimator Meets Equilibrium Perspective: A Rectified Straigh...

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International Conference on Computer Vision (ICCV)

作者： Xiao-Ming Wu Dian Zheng Zuhao Liu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Pengcheng Lab China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Binarization of neural networks is a dominant paradigm in neural networks compression. The pioneering work BinaryConnect uses Straight Through Estimator (STE) to mimic the gradients of the sign function, but it also causes the crucial inconsistency problem. Most of the previous methods design different estimators instead of STE to mitigate it. However, they ignore the fact that when reducing the estimating error, the gradient stability will decrease concomitantly. These highly divergent gradients will harm the model training and increase the risk of gradient vanishing and gradient exploding. To fully take the gradient stability into consideration, we present a new perspective to the BNNs training, regarding it as the equilibrium between the estimating error and the gradient stability. In this view, we firstly design two indicators to quantitatively demonstrate the equilibrium phenomenon. In addition, in order to balance the estimating error and the gradient stability well, we revise the original straight through estimator and propose a power function based estimator, Rectified Straight Through Estimator (ReSTE for short). Comparing to other estimators, ReSTE is rational and capable of flexibly balancing the estimating error with the gradient stability. Extensive experiments on CIFAR-10 and ImageNet datasets show that ReSTE has excellent performance and surpasses the state-of-the-art methods without any auxiliary modules or losses.

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Code Search Debiasing: Improve Search Results beyond Overall Ranking Performance

arXiv

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arXiv 2023年

作者： Zhang, Sheng Li, Hui Wang, Yanlin Wei, Zhao Xu, Yong Wang, Juhong Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Code search engine is an essential tool in software development. Many code search methods have sprung up, focusing on the overall ranking performance of code search. In this paper, we study code search from another perspective by analyzing the bias of code search models. Biased code search engines provide poor user experience, even though they show promising overall performance. Due to different development conventions (e.g., prefer long queries or abbreviations), some programmers will find the engine useful, while others may find it hard to get desirable search results. To mitigate biases, we develop a general debiasing framework that employs reranking to calibrate search results. It can be easily plugged into existing engines and handle new code search biases discovered in the future. Experiments show that our framework can effectively reduce biases. Meanwhile, the overall ranking performance of code search gets improved after debiasing. Our implementation is available at: https://***/KDEGroup/CodeSearchDebiasing. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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computing Challenges of UAV Networks: A Comprehensive Survey

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Computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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Efficient learning of continuous-variable quantum states

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Physical Review Research 2024年第3期6卷 033280页

作者： Ya-Dong Wu Yan Zhu Giulio Chiribella Nana Liu John Hopcroft Center for Computer Science QICI Quantum Information and Computation Initiative Department of Computer Science The Department of Computer Science for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Institute of Natural Sciences Ministry of Education Key Laboratory in Scientific and Engineering Computing School of Mathematical Sciences University of Michigan-

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation, and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(logM) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

关键词： Machine learning Quantum information processing with continuous variables Quantum tomography

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