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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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Research on the Quantity Evaluation of Speech datasets for Model Training

Research on the Quantity Evaluation of Speech Datasets for M...

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International Conference on Intelligent computing, Automation and Systems (ICICAS)

作者： Sun Li Feng Cao Zishan Liu Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing China Institute of Technology and Standards China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9781665428118

With the maturity of intelligent speech technology and product application, the demand for high-quality speech datasets is increasing. There have been some researchers put effort on the quality evaluation of the structured data, but there are few standards appeared for the speech datasets. By analyzing the construction principle of speech algorithm model and analyzing the construction demand of speech datasets, a unified quality assessment framework for the speech datasets is presented. The framework proposes to evaluate the speech datasets in terms of four dimensions: breadth coverage, anthology distinction, professional field depth and data integrity. By proposing specific speech datasets quality evaluation metrics, calculation methods and evaluation steps, and analyzing the experimental examples and results of speech datasets quality evaluation in vehicle application field, this paper provides a reference basis for evaluating speech datasets quality and promoting datasets construction. Considering the diversified applicability, privacy issues, efficiency and automation requirements of speech datasets construction, some suggestions for the future development of high-quality speech datasets construction are put forward.

关键词： Measurement Training data privacy Analytical models Automation data integrity Quality assessment

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Outlier detection for functional data with R package fdaoutlier

arXiv

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arXiv 2021年

作者： Ojo, Oluwasegun Lillo, Rosa E. Anta, Antonio Fernández IMDEA Networks Institute Global Computing Group Department of Statistics Universidad Carlos III de Madrid uc3m-Santander Big Data Institute

Outlier detection is one of the standard exploratory analysis tasks in functional data analysis. We present the R package fdaoutlier which contains implementations of some of the latest techniques for detecting functional outliers. The package makes it easy to detect different types of outliers (magnitude, shape, and amplitude) in functional data, and some of the implemented methods can be applied to both univariate and multivariate functional data. We illustrate the main functionality of the R package with common functional datasets in the literature. © 2021, CC BY-NC-SA.

关键词： Anomaly detection

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FedLRDP: Federated Learning Framework with Local Random Differential Privacy

FedLRDP: Federated Learning Framework with Local Random Diff...

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International Joint Conference on Neural Networks (IJCNN)

作者： Runtian Zhou Anming Dong Jiguo Yu Qingyan Ding Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.

关键词： Training Differential privacy Privacy Accuracy Federated learning Noise data models

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Matter power spectrum induced by primordial magnetic fields: from the linear to the non-linear regime

arXiv

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arXiv 2024年

作者： Ralegankar, Pranjal Garaldi, Enrico 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 Beirut 2 Trieste34151 Italy INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Linear theory predicts that primordial magnetic fields (PMFs) enhance the matter power spectrum on small scales. However, the linear approximation breaks down on sufficiently small scales where PMF-induced baryon perturbations back-react onto the magnetic fields. Previous studies assumed that the baryon power spectrum would be sharply suppressed in this non-linear regime, based on arguments related to the magnetic Jeans scale. For the first time, we perform dedicated magnetohydrodynamic (MHD) simulations to investigate the transition from the linear to the non-linear regime. Our simulations confirm the expected linear behavior on large scales. In the non-linear regime, however, we find that the dimensionless baryon power spectrum saturates to an O(1) value, which contrasts with previous analytical expectations. Additionally, our results show that several past studies overestimated the total matter power spectrum by orders of magnitude near the transition to non-linearity. Thus, the results presented in this work are useful to obtain more accurate constraints on PMFs from structure formation processes and/or different tracers of cosmic structures. © 2024, CC BY.

关键词： Magnetic fields

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Strong Coupling of Antiferromagnetic Resonance with Subterahertz Cavity Fields

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Physical Review Applied 2021年第4期15卷 044018-044018页

作者： M. Białek J. Zhang H. Yu J.-Ph. Ansermet Institute of Physics École Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Strong coupling of electromagnetic cavity fields with antiferromagnetic spin waves in hematite (α-Fe2O3) is achieved above room temperature. A cube of hematite is placed in a metallic tube and the transmission is measured, using a continuous-wave terahertz spectrometer. The spectra, collected as a function of the temperature, reveal the formation of magnetic polaritons.

关键词： Magnons Polaritons Spin waves Antiferromagnets Noncollinear magnets Transition metal oxides Cavity resonators Terahertz spectroscopy

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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Ultrafast all-optical manipulation of the charge-density wave in VTe2

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Physical Review Research 2023年第4期5卷 043276-043276页

作者： Manuel Tuniz Davide Soranzio Davide Bidoggia Denny Puntel Wibke Bronsch Steven L. Johnson Maria Peressi Fulvio Parmigiani Federico Cilento Dipartimento di Fisica Università degli Studi di Trieste 34127 Trieste Italy Institute for Quantum Electronics Eidgenössische Technische Hochschule (ETH) Zürich 8093 Zurich Switzerland Elettra - Sincrotrone Trieste S.C.p.A. Strada Statale 14 km 163.5 34149 Trieste Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing 40121 Bologna Italy International Faculty University of Cologne Albertus-Magnus-Platz 50923 Cologne Germany

The charge-density-wave (CDW) phase in the layered transition-metal dichalcogenide VTe2 is strongly coupled to the band inversion involving vanadium and tellurium orbitals. In particular, this coupling leads to a selective disappearance of the Dirac-type states that characterize the normal phase, when the CDW phase sets in. Here, we investigate the broadband time-resolved reflectivity variations caused by collective and single-particle excitations in the CDW ground state of VTe2. With the aid of density functional perturbation theory simulations we unveil the presence of two collective amplitude modes of the CDW ground state. By applying a double-pulse excitation scheme, we show the possibility to manipulate these modes, gaining insights into the coupling between these two collective excitations and demonstrating a more efficient way to perturb the CDW phase in VTe2.

关键词： Carrier dynamics Charge density waves Coherent control Electronic structure of atoms & molecules First-principles calculations Lattice dynamics Topological materials Ultrafast optics Ultrafast phenomena

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The Adaptive Fault-tolerant Routing Based on an Improved Local Security Information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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A local enhanced spatiotemporal tensor decomposition for missing travel time completion

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Smart and Resilient Transportation 2022年第3期4卷 194-208页

作者： Yilong Ren Jianbin Wang School of Transportation Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China and Beihang Hangzhou Innovation Institute Yuhang Hangzhou China School of Transportation Science and Engineering Beihang University Beijing China and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

Purpose The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy. Design/methodology/approach The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type. Findings The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates. Originality/value This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

关键词： Travel time data completion Fibrosis missing scenarios Tensor decomposition

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