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International Workshop on Quality of Service

作者： Bo Tan Haikun Liu Jia Rao Xiaofei Liao Hai Jin Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The University of Texas at Arlington

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

Serverless computing, also known as “Function as a Service (FaaS)”, is emerging as an event-driven paradigm of cloud computing. In the FaaS model, applications are programmed in the form of functions that are executed and managed separately. Functions are triggered by cloud users and are provisioned dynamically through containers or virtual machines (VMs). The startup delays of containers or VMs usually lead to rather high latency of response to cloud users. Moreover, the communication between different functions generally relies on virtual net devices or shared memory, and may cause extremely high performance overhead. In this paper, we propose Unikernel-as-a-Function (UaaF), a much more lightweight approach to serverless computing. Applications are abstracted as a combination of different functions, and each function are built as an unikernel in which the function is linked with a specified minimum-sized library operating system (LibOS). UaaF offers extremely low startup latency to execute functions, and an efficient communication model to speed up inter-functions interactions. We exploit an new hardware technique (namely VMFUNC) to invoke functions in other unikernels seamlessly (mostly like inter-process communications), without suffering performance penalty of VM Exits. We implement our proof-of-concept prototype based on KVM and deploy UaaF in three unikernels (MirageOS, IncludeOS, and Solo5). Experimental results show that U aaF can significantly reduce the startup latency and memory usage of serverless cloud applications. Moreover, the VMFUNC-based communication model can also significantly improve the performance of function invocations between different unikernels.

关键词： Cloud computing FAA Libraries Containers Task analysis Computer architecture Virtual machine monitors

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Superpage-Friendly Page Table Design for Hybrid Memory Systems

Superpage-Friendly Page Table Design for Hybrid Memory Syste...

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2020国际计算机前沿大会

作者： Xiaoyuan Wang Haikun Liu Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Service Computing Technology and System Lab Huazhong University of Science and Technology Cluster and Grid Computing Lab Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory(DRAM) and non-volatile memories(NVMs), to improve the system performance and energy ***, page migration introduces some side effects, such as more translation lookaside buffer(TLB) misses, breaking memory contiguity, and extra memory accesses due to page table updating. In this paper, we propose superpagefriendly page table called SuperPT to reduce the performance overhead of serving TLB misses. By leveraging a virtual hashed page table and a hybrid DRAM allocator, SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages.

关键词： Pagetable Hybridmemorysystem Pagemigration Multiplepagesizes Addresstranslation

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Partitioning large-scale property graph for efficient distributed query processing 21

Partitioning large-scale property graph for efficient distri...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data science and Systems, HPCC/SmartCity/DSS 2019

作者： Pang, Haohan Gan, Peng Yuan, Pingpeng Jin, Hai Hua, Qiangsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Clusters and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead. © 2019 IEEE.

关键词： Semantics

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Leveraging hardware-control imperfections for error mitigation via generalized quantum subspace

arXiv

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arXiv 2023年

作者： Ohkura, Yasuhiro Endo, Suguru Satoh, Takahiko Van Meter, Rodney Yoshioka, Nobuyuki Graduate School of Media and Governance Keio University SFC Kanagawa Fujisawa252-0882 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Keio University Quantum Computing Center Kanagawa Yokohama223-8522 Japan Graduate School of Science and Technology Keio University Kanagawa Yokohama223-8522 Japan Faculty of Environment and Information Studies Keio University SFC Kanagawa Fujisawa252-0882 Japan Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Wako-shi Saitama351-0198 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitigation algorithms under the framework of the quantum subspace expansion. Specifically, the fault-subspace method, a subclass of GSE method, constructs an error-mitigated quantum state with copies of quantum states with different noise levels. However, from the experimental aspect, it is nontrivial to determine how to reliably amplify the noise so that the error in the simulation result is efficiently suppressed. In this work, we explore the potential of the fault-subspace method by leveraging the hardware-oriented noise: intentional amplification of the decoherence, noise boost by insertion of identity, making use of crosstalk, and probabilistic implementation of noise channel. We demonstrate the validity of our proposals via both numerical simulations with the noise parameters reflecting those in quantum devices available via IBM Quantum, and also experiments performed therein. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Spatiotemporal water quality data reconstruction: A tensor factorization framework

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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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Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

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Journal of Physical Chemistry A 2025年第24期129卷 5373-5390页

作者： Hourahine, B. Berdakin, M. Bich, J.A. Bonafé, F.P. Camacho, C. Cui, Q. Deshaye, M.Y. Díaz Mirón, G. Ehlert, S. Elstner, M. Frauenheim, T. Goldman, N. González León, R.A. van der Heide, T. Irle, S. Kowalczyk, T. Kubař, T. Lee, I.S. Lien-Medrano, C.R. Maryewski, A. Melson, T. Min, S.K. Niehaus, T. Niklasson, A.M.N. Pecchia, A. Reuter, K. Sánchez, C.G. Scheurer, C. Sentef, M.A. Stishenko, P.V. Vuong, V.Q. Aradi, B. SUPA Department of Physics University of Strathclyde John Anderson Building 107 Rottenrow Glasgow G4 0NG United Kingdom Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC) Córdoba X5000HUA Argentina Universidad Nacional de Córdoba Facultad de Ciencias Químicas Departamento de Química Teórica y Computacional Córdoba X5000HUA Argentina Universidad Nacional de Córdoba Centro Láser de Ciencias Moleculares Córdoba X5000HUA Argentina Bremen Center for Computational Materials Science University of Bremen Bremen 28359 Germany Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Luruper Chaussee 149 Hamburg 22761 Germany School of Chemistry University of Costa Rica San José 11501-2060 Costa Rica Department of Chemistry Boston University Boston 02215 MA United States Department of Chemistry Advanced Materials Science & Engineering Center Institute for Energy Studies Western Washington University Bellingham 98225 WA United States Condensed Matter and Statistical Physics The Abdus Salam International Centre for Theoretical Physics Trieste 34151 Italy Microsoft Research AI for Science Schipol 1118 CZ Netherlands Institute of Physical Chemistry (IPC) Karlsruhe Institute of Technology Karlsruhe 76131 Germany School of Science Constructor University Bremen 28759 Germany Lawrence Livermore National Laboratory Livermore 94550 CA United States Department of Chemical Engineering University of California Davis 95616 CA United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge 37831 TN United States Center for Multidimensional Carbon Materials (CMCM) Institute for Basic Science (IBS) Ulsan 44919 South Korea Max Planck Computing and Data Facility Garching 85748 Germany Department of Chemistry Ulsan National Institute of Science and Technology (UNIST) 50 UNIST-gil Ulju-gun Ulsan

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features. © 2025 The Authors.

关键词： Open source software

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An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

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SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Distributions of cherries and pitchforks for the Ford model

arXiv

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arXiv 2021年

作者： Kaur, Gursharn Choi, Kwok Pui Wu, Taoyang Biocomplexity Institute University of Virginia CharlottesvilleVA22911 United States Department of Statistics and Data Science The Department of Mathematics National University of Singapore 117546 Singapore School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom

We study two fringe subtree counting statistics, the number of cherries and that of pitchforks for Ford's α model, a one-parameter family of random phylogenetic tree models that includes the uniform and the Yule models, two tree models commonly used in phylogenetics. Based on a nonuniform version of the extended Pólya urn models in which negative entries are permitted for their replacement matrices, we obtain the strong law of large numbers and the central limit theorem for the joint distribution of these two count statistics for the Ford model. Furthermore, we derive a recursive formula for computing the exact joint distribution of these two statistics. This leads to exact formulas for their means and higher order asymptotic expansions of their second moments, which allows us to identify a critical parameter value for the correlation between these two statistics. That is, when n is sufficiently large, they are negatively correlated for 0 ≤ α ≤ 1/2 and positively correlated for 1/2 © 2021, CC BY-NC-ND.

关键词： Computation theory

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce

Design and Comparison of Collaborative Filtering Technology ...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： P. Rajasekar B. Mohanraj S. N. Padhi N. Sivakumar Lavanya J Charles Prabu V Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur Campus Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665460859

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective.

关键词： Training Support vector machines Analytical models Machine learning algorithms data visualization Machine learning Predictive models

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Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

Evaluating the Use of Large Language Model to Identify Top R...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Huiqin Chen Fangwan Huang Xuanyun Liu Yinli Tan Dihua Zhang Dongqi Li Glen M. Borchert Ming Tan Jingshan Huang Department of Pediatrics The Third Affiliated Hospital Sun Yatsen University Guangzhou China College of Computer and Data Science Fuzhou University Fuzhou China Department of Nephrology The First Affiliated Hospital Sun Yat-sen University Guangzhou China Department of Economics & School of Information and Computer Sciences University of California Irvine Irvine U.S.A. Department of Pharmacology College of Medicine University of South Alabama Mobile U.S.A. Institute of Biochemistry and Molecular Biology Institute of Biomedical Sciences China Medical University Taichung Taiwan School of Computing College of Medicine University of South Alabama Mobile U.S.A.

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.

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