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Scalable and adaptive log manager in distributed systems

Frontiers of Computer Science 2023年第2期17卷 45-62页

作者： Huan ZHOU Weining QIAN Xuan ZHOU Qiwen DONG Aoying ZHOU Wenrong TAN The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu UniversityChengdu 610041China School of Data Science and Engineering East China Normal UniversityShanghai 200062China

On-line transaction processing(OLTP)systems rely on transaction logging and quorum-based consensus protocol to guarantee durability,high availability and strong *** makes the log manager a key component of distributed database management systems(DDBMSs).The leader of DDBMSs commonly adopts a centralized logging method to writing log entries into a stable storage device and uses a constant log replication strategy to periodically synchronize its state to *** the advent of new hardware and high parallelism of transaction processing,the traditional centralized design of logging limits scalability,and the constant trigger condition of replication can not always maintain optimal performance under dynamic *** this paper,we propose a new log manager named Salmo with scalable logging and adaptive replication for distributed database *** scalable logging eliminates centralized contention by utilizing a highly concurrent data structure and speedy log hole *** kernel of adaptive replication is an adaptive log shipping method,which dynamically adjusts the number of log entries transmitted between leader and followers based on the real-time *** implemented and evaluated Salmo in the open-sourced transaction processing systems Cedar and *** results show that Salmo scales well by increasing the number of working threads,improves peak throughput by 1.56×and reduces latency by more than 4×over log replication of Raft,and maintains efficient and stable performance under dynamic workloads all the time.

关键词： distributed database systems transaction log-ging log replication scalable adaptive

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Enhancement of low-temperature toughness of Fe-Mn-C-Al alloy by rare earth Ce-modified inclusions

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Journal of Iron and Steel Research International 2024年第1期31卷 157-173页

作者： Guang-kai Yang Chang-ling Zhuang Yi-zhuang Li Chen Hu Shao-bo Li College of Materials and Metallurgy Guizhou UniversityGuiyang550025GuizhouChina Department of Mechanical Engineering University of Hong KongHong Kong999077China The State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang110819LiaoningChina State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025GuizhouChina

Fe-Mn-C-Al alloys have been recognized as promising materials for certain low-temperature applications due to their exceptional mechanical properties and ***,their limited low-temperature toughness restricts their large-scale applications in specific *** influence of trace amounts of rare earth cerium(Ce)on the low-temperature toughness of Fe-18Mn-0.6C-1.8Al alloys was *** addition of Ce effectively alters the inclu-sions in the alloy,transforming large-sized irregular inclusions into fine ellipsoidal rare earth *** leads to a significant reduction in both the proportion and average size of the inclusions,resulting in their effective dispersion throughout the matrix and improved cryogenic *** presence of Ce-containing inclusions within the matrix reduces stress concentration,thereby inhibiting microcrack formation and improving impact absorption ***-cally,the addition of rare earth Ce alters the fracture behavior of the material at room temperature and low temperature,changing from brittle cleavage fracture to a more ductile failure *** impact toughness of the Fe-Mn-C-Al alloy is significantly improved by the addition of 0.0048 wt.%Ce,particularly at-196℃where the impact toughness reaches 103.6 J/cm^(2),representing an impressive improvement of 87.3%.

关键词： Fe-Mn-C-Al alloy Rare earth Low-temperature toughness Inclusion Fracture

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RGB-D SLAM Method Based on Feature Association in Dynamic Environment 6

RGB-D SLAM Method Based on Feature Association in Dynamic En...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Fang, Baofu Wang, Hao Wang, Hao Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665453110

Simultaneous localization and mapping (SLAM) is one of the current research hotspots. However, in visual SLAM for dynamic environments, inaccurate detection of object motion states and incomplete dynamic region culling will lead to large localization errors. To address these issues, this paper proposes an RGB-D SLAM method based on feature association. The method has strongly correlated features in time and space according to the input image sequence. Using the moving probability of the feature points in the previous frame, the movement of the feature points in the current frame is calculated in combination with the dynamic corner points screened in the current frame. Then, the motion state of the object is determined according to the proportion of different feature points. Then combined with semantic information and object depth information, the fast search method is used to obtain accurate dynamic regions. Finally, the selected effective feature points are used to estimate the camera pose and establish a static map of the environment. This paper evaluates the robustness and accuracy of our method on the TUM dataset and real environment, and the results show that our method can significantly improve the system tracking effect and reduce the system tracking error compared with other SLAM methods in dynamic environments. © 2022 IEEE.

关键词： Semantics

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The Mass Assembly History for Galaxies with MaNGA

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Research in Astronomy and Astrophysics 2024年第3期24卷 58-69页

作者： Xue Ge Hong-Tao Wang Cheng-Long Lei Yun-Jun Guo Yi-Long Jiang Xiao-Xiao Cao School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Key Laboratory of Modern Astronomy and Astrophysics Nanjing UniversityMinistry of Education School of Science Langfang Normal University

How galaxies assemble masses through their own star formation or interaction with the external environment is still an important topic in the field of galaxy formation and *** use Value Added Catalogs with galaxy features that are spatially and temporally resolved from Sloan Digital Sky Survey data Release 17 to investigate the mass growth histories of early-type galaxies(ETGs)and late-type galaxies(LTGs).We find that the mass growth of ETGs is earlier than that of LTGs for massive galaxies(M_(*)>10^(10)M_⊙),while low-mass(M_(*)≤10^(10)M_⊙)ETGs have statistically similar mass assembly histories as low-mass *** stellar metallicity of all massive galaxies shows a negative gradient and basically does not change with ***,in low-mass galaxies,the stellar metallicity gradient of elliptical galaxies is negative,and the stellar metallicity gradient of lenticular and spiral galaxies evolves from positive to *** are not all in a high-density environment,but exhibit mass *** the tidal strength increases,the star formation rate of low-mass ETGs rapidly *** results support a picture where massive galaxies exhibit inside-out quenching mode,while low-mass galaxies show outside-in quenching *** effects play an important role in regulating the mass assembly histories of low-mass ETGs.

关键词： Galaxy evolution-galaxies star formation-Galaxy formation

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Semantic SLAM Based on Compensated Segmentation and Geometric Constraints in Dynamic Environments 6

Semantic SLAM Based on Compensated Segmentation and Geometri...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Fang, Baofu Zhou, Shuai Wang, Hao Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665453110

Most of the existing slam algorithms are designed based on the assumption of a static environment, this strong assumption limits the practical application of most slam systems. The main reason is that moving objects will cause feature mismatch in the pose estimation process, which in turn affects the accuracy of localization and mapping. In this paper, we propose a SLAM algorithm in a dynamic environment. First, we use the BlendMask network to detect potential moving objects to generate masks for dynamic objects. The geometrically constrained joint optical flow method is used to detect dynamic feature points. Secondly, aiming at the failure of semantic segmentation network segmentation, a missed detection compensation algorithm based on the invariance of adjacent frame speed is proposed. Finally, a keyframe selection strategy is proposed to construct a semantic octree graph containing only static objects. We evaluate our algorithm on TUM RGB-D and real scene datasets. The experimental results show that the algorithm has high accuracy and real-time performance. © 2022 IEEE.

关键词： Semantics

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第02期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

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Synthetic and Systems Biotechnology 2023年第3期8卷 498-508页

作者： Yalun Wu Qianqian Yuan Yongfu Yang Defei Liu Shihui Yang Hongwu Ma State Key Laboratory of Biocatalysis and Enzyme Engineering School of Life SciencesHubei UniversityWuhan430062HubeiChina Biodesign Center Key Laboratory of Systems Microbial BiotechnologyTianjin Institute of Industrial BiotechnologyChinese Academy of SciencesTianjin300308China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhan430062HubeiChina Artificial Intelligence and Knowledge Engineering Lab School of Computer Science and Information EngineeringHubei UniversityWuhan430062HubeiChina

High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology *** of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still *** mobilis *** ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical *** five GEMs of *** have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format *** this study,a high-quality GEM iZM516 of *** ZM4 was *** information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of ***,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of *** growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate *** addition,the potential endogenous succinate synthesis pathway of *** ZM4 was proposed through simulation and analysis using ***,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using *** results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species *** study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell fac

关键词： Genome-scale metabolic models(GEMSs) Non-model industrial microorganism Zymomonas mobilis Biolog phenotype microarray Succinate 1,4-Butanediol

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data-driven intelligent modeling framework for the steam cracking process

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Chinese Journal of Chemical engineering 2023年第9期61卷 237-247页

作者： Qiming Zhao Kexin Bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data Systems and Applications Tsinghua UniversityBeijing 100084China School of Chemical Engineering Sichuan UniversitySichuan 610065China Department of Bioprocess Engineering Institute of BiotechnologyTechnische Universität BerlinBerlin 10623Germany

Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical *** models of the cracking process can boost production efficiency and profit *** advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process ***,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational *** research presents a framework for data-driven intelligent modeling of the steam cracking *** data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means *** propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying *** framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.

关键词： Mathematical modeling data-driven modeling Process systems Steam cracking Clustering Multivariate adaptive regression spline

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AMSCF-Net: An adaptive multi-scale cost fusion stereo network for high-quality disparity estimation

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 76-101页

作者： Tian, Mao Fan, Jiajin Zhang, Qinghua Chen, Chi Yang, Bisheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism China

Accurate and complete reconstruction of the high-quality three-dimensional (3D) geometric information from stereo images has been one of the challenging tasks in the fields of photogrammetry and computer vision. Although tremendous efforts have been made in stereo matching by utilizing convolutional neural network (CNN)-based deep learning architectures, the matching ambiguities in the case of occlusions, disparity discontinuities, and poor and repetitive texture areas remain to be explored. In this paper, to address these issues, we propose a novel end-to-end disparity estimation network named AMSCF-Net, which consists of two main components: the context and geometry adaptive attention weight generation (CGA-AWG) subnetwork for yielding robust multi-scale attention weights, and the adaptive multi-scale cost volume fusion (AMS-CVF) subnetwork to enhance the feature representation of the cost volumes for accurate disparity map prediction. Firstly, the multi-scale feature maps are obtained from the stereo images by employing a weight-sharing hierarchical feature extraction subnetwork. Secondly, the CGA-AWG subnetwork is adopted to produce accurate and robust multi-scale attention weights. Thirdly, the adaptive multi-scale cost volume fusion (AMS-CVF) subnetwork is introduced to boost the robustness of the fused cost volume in the poor and repetitive texture areas, while simultaneously preserving the details of the scene's geometric structure in the rich texture regions. Finally, the context and geometry adaptive disparity map prediction (CGA-DMP) subnetwork is applied to further refine the cost volumes for high-accuracy disparity map reconstruction. Three publicly available benchmark datasets, i.e., the KITTI 2015, US3D, and Vaihingen stereo datasets, were used to evaluate the performance of AMSCF-Net. The comprehensive experiments demonstrate that AMSCF-Net achieves state-of-the-art disparity prediction results on all datasets in terms of the ("D1-bg", "D1-fg", "D1-a

关键词： Convolutional neural networks

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