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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： Yi, Yusheng Xiao, Jiang Wu, Song Li, Huichuwu Jin, Hai National Engineering Research Center for Big Data Technology System Services Computing Technology System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models. © 2019 IEEE.

关键词： Information retrieval

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DPABISurf:data processing&analysis for brain imaging on surface

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Science Bulletin 2021年第24期66卷 2453-2455页

作者： Chao-Gan Yan Xin-Di Wang Bin Lu CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China McGill Centre for Integrative Neuroscience Montreal Neurological InstituteMcGill UniversityMontréal H3A 2B4Canada Beijing Intelligent Brain Cloud Inc.Beijing 100036China

Functional magnetic resonance imaging(fMRI)is a prevalent technology in brain research of cognition,emotion,development,and brain *** traditional fMRI analysis is based on volume-based preprocessing pipelines and algorithms,which means that the brain MRI data is to be registered to a 3-dimensional(3D)coordinate[1].However,the relatively low spatial resolution of fMRI may lead to partial-volume-effect(e.g.,a 3D region may contain signals from grey matter,white matter and even cerebrospinal fluid).Given the human brain function is organized in a brain surface mesh manner,therefore,a growing number of studies conducted surface-based preprocessing pipelines and ***-based methods reconstructed the brain grey matter into 2-dimensional cortical surface which better represent the curving structure of the ***-based method is superior to volume-based method on brain registration,signal–noise ratio and reproducibility of algorithms[2].Specifically,the traditional volume-based approach was reported with a spatial localization that is only 35%of the best surface-based method[2].

关键词：脑影像 coordinate registered

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Predicting Friendship Using a Unified Probability Model 7th

Predicting Friendship Using a Unified Probability Model

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7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Kou, Zhijuan Wang, Hua Yuan, Pingpeng Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811518980

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter data set, High school Call data set) show that our model works as expected. © Springer Nature Singapore Pte Ltd 2019.

关键词： Social networking (online)

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Predicting Virtual Machine Resource Consumption Based on Optimized Grey Model

Predicting Virtual Machine Resource Consumption Based on Opt...

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第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Shi Aiwu Gao Di He Kai Cloud Computing and Big Data Research Center Wuhan Textile University

In order to relatively change the computing specifications of virtual machines(VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.

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Self-to-self transitions in open quantum systems:the origin and solutions

Self-to-self transitions in open quantum systems:the origin ...

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第三十九届中国控制会议

作者： Fang Gao Yaoxiong Wang Qing Gao Dewen Cao Feng Shuang College of Electrical Engineering Guangxi University Institute of Intelligent Machines Chinese Academy of Sciences School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University

The information of quantum pathways can be extracted in the framework of the Hamiltonianencoding and Observable-decoding *** closed quantum systems,only off-diagonal elements of the Hamiltonian in the Hilbert space is required to be encoded to obtain the desired *** open quantum systems,environment-related terms will appear in the diagonal elements of the Hamiltonian in the Liouville *** diagonal encodings have to be performed to differentiate different pathways,which will lead to self-to-self transitions and inconsistency of pathway amplitudes with Dyson *** this work,a well-designed transformation is proposed to avoid the counterintuitive transitions and the inconsistency,with or without control *** simulations show that the method are consistent with Dyson expansion.

关键词： Self-to-self transitions in open quantum systems:the origin and solutions origin

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Convergence Analysis of an SLF-NMU Algorithm for Non-negative Latent Factor Analysis on a High-Dimensional and Sparse Matrix

Convergence Analysis of an SLF-NMU Algorithm for Non-negativ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhigang Liu Xin Luo Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection. An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is highly efficient to build NLF model. However, its convergence ability on such a matrix is still unveiled in theory. This paper presents the convergence property of an SLF-NMU algorithm. We theoretically prove that it can guarantee its convergence to a Karush-Kuhn-Tucher (KKT) stationary point. Empirical studies on two HiDS matrices from practical applications indicate that an SLF-NMU algorithm make an NLF model to converge at a relatively steady state.

关键词： Sparse matrices Convergence Computational modeling Analytical models Linear programming data models Technological innovation

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Event-triggering-based H_∞ load frequency control for multi-area cyber–physical power system under DoS attacks

Franklin Open

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Franklin Open 2023年 3卷

作者： Liu, Xingyue Shi, Kaibo Zhou, Kun Wen, Shiping Tang, Yiqian Tang, Lin School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 China

In this work, the event-triggering-based resilient H∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicle... 详细信息

In this work, the event-triggering-based resilient H_∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicles (EVs). In MA-CPPS, the sampled measurements are transmitted through the communication network, which may be subjected to the energy-limited DoS attack with a resilient index of maximum allowable continuous data losses. Meanwhile, a novel adaptive event-triggering mechanism (AETM) characterized by parameters disturbances and dynamic adjustment mechanism of triggering threshold can effectively reduce the occupation of network bandwidth and computational burden in practical operation of sampled-data LFC in MA-CPPS. Hence a sampled-data LFC model of considering energy-limited DoS attack and AETM is established firstly. Then based on a novel AETM transmission interval-dependent loop functional approach, a resilient H_∞ state-feedback controller is designed with guaranteed H_∞ performance and resilient index of energy-limited DoS attack. Finally, the effectiveness and superiority of our proposed scheme are verified by one-area and three-areas LFC CPPS. © 2023 The Authors

关键词： Adaptive event-triggering mechanism Cyber–physical power system Denial-of-service attacks Resilient H∞ load frequency control scheme Transmission interval-dependent loop functional approach

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An integrated text-based hybrid RNN-CNN model for toxic comment classification

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AIP Conference Proceedings 2023年第1期2680卷

作者： Tan Chi Wee Chaw Jun Kit Tew Yiqi Wong Siaw Lang Gloria Jennis Tan Norhaida Mohd Suaib Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia Centre for IT Development and Services Universiti Malaysia Sarawak Malaysia UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia Malaysia

Forum and social media especially Facebook, Instagram and Twitter play a vital role in the communication and become the pivotal role in expressing feelings and opinions toward certain posts and news through leaving a comment and review. The comment and review can be positive, neutral, or negative. There have been several longitudinal studies involving online comments showed that one of five comments, 20 percent of comments are toxic comments. Toxic comments are those negative comments that can greatly affect a person emotionally which might cause people to suffer from mental problems. Hence, toxic comments detection is fast becoming a key instrument in protecting social media platforms’ users from cyberbullying. This paper seeks to remedy this issue by proposing and comparing different artificial intelligence models to classify the toxic online comments. Thus, six different models with various algorithms which are CNN, RNN, Hybrid-NN, Linear SVC, multinomial naive bayes and logistic regression will be trained with a pre-processed dataset and evaluated with cross validation and ROC AUC. These models are then compared by observing the accuracy of each model and result shows that Hybrid-Neural network, combination between RNN and CNN score the top of the chart with accuracy of 0.9778 which suggest the best model to classify the online comments.

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Weighing neutrinos with 21cm Intensity Mapping at the SKAO

arXiv

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arXiv 2025年

作者： Autieri, Gabriele Berti, Maria Spinelli, Marta Haridasu, Balakrishna Sandeep Viel, Matteo SISSA- International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut2 Trieste34151 Italy Département de Physique Théorique Center for Astroparticle Physics Université de Genève Quai E. Ansermet 24 Genève 4CH-1211 Switzerland Observatoire de la Côte d’Azur Laboratoire Lagrange Bd de l’Observatoire CS 34229 Nice06304 cedex 4 France Department of Physics and Astronomy University of the Western Cape Robert Sobukwe Road Cape Town7535 South Africa INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy

We explore the constraining power of future 21cm intensity mapping (IM) observations at the SKAO, focusing primarily on the sum of neutrino masses, Σmν. We forecast observations of the 21cm IM auto-power spectrum as well as the 21cm IM and galaxy surveys cross-correlation power spectrum. We construct different synthetic data sets of observations for the 21cm IM observables in the redshift range 0 ν = 0.06, 0.1, 0.4 eV, in the synthetic data sets. To investigate the constraining power of the forecasted 21cm observations, we build a likelihood code that will be made publicly available upon publication. The results of the analysis, obtained through Markov Chain Monte Carlo techniques, are promising. We find that the 21cm auto-power spectrum alone could provide an upper limit on the sum of neutrino masses of Σmν ν. This result is comparable to the upper limits provided by cosmic microwave background (CMB) observations alone. When combining the 21cm auto-power spectrum synthetic data set with Planck 2018 CMB measurements, we find a tighter upper limit of Σmν ν ν Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Is GN-z11 powered by a super-Eddington massive black hole?

arXiv

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arXiv 2024年

作者： Bhatt, Maulik Gallerani, Simona Ferrara, Andrea Mazzucchelli, Chiara D'Odorico, Valentina Valentini, Milena Zana, Tommaso Farina, Emanuele Paolo Chakraborty, Srija Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Avenida Ejercito Libertador 441 Santiago Chile INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 Roma00185 Italy Gemini Observatory NSF's NOIRLab 670 N A'ohoku Place HiloHI96720 United States

Context. Observations of z ∼ 6 quasars powered by supermassive black holes (SMBHs;MBH ∼ 108−10 Mo) challenge our current understanding of early black hole (BH) formation and evolution. The advent of the James Webb Space Telescope (JWST) has enabled the study of massive BHs (MBHs;MBH ∼ 106−7 Mo) up to z ∼ 11, thus bridging the properties of z ∼ 6 quasars to their ancestors. Aims. The JWST spectroscopic observations of GN-z11, a well-known z = 10.6 star-forming galaxy, have been interpreted with the presence of a super-Eddington (Eddington ratio ≡ λEdd ∼ 5.5) accreting MBH. To test this hypothesis, we used a zoom-in cosmological simulation of galaxy formation and BH co-evolution. Methods. We first tested the simulation results against the observed probability distribution function (PDF) of λEdd found in z ∼ 6 quasars. Then, in the simulation we selected the BHs that satisfy the following criteria: (a) 10 BH > 106 Mo. Next, we applied the extreme value statistics to the PDF of λEdd resulting from the simulation. Results. We find that the probability of observing a z ∼ 10 − 11 MBH accreting with λEdd ∼ 5.5 in the volume surveyed by JWST is very low ( © 2024, CC BY.

关键词： Uncertainty analysis

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