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arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Force-based gradient descent method for ab initio atomic structure relaxation

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Physical Review B 2022年第10期106卷 104101-104101页

作者： Yukuan Hu Xingyu Gao Yafan Zhao Xin Liu Haifeng Song State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China CAEP Software Center for High Performance Numerical Simulation Beijing 100088 China

Force-based algorithms for ab initio atomic structure relaxation, such as conjugate gradient methods, usually get stuck in the line minimization processes along search directions, where expensive ab initio calculations are triggered frequently to test trial positions before locating the next iterate. We present a force-based gradient descent method, WANBB, that circumvents the deficiency. At each iteration, WANBB enters the line minimization process with a trial step size capturing the local curvature of the energy surface. The exit is controlled by a nonrestrictive criterion that tends to accept early trials. These two ingredients streamline the line minimization process in WANBB. The numerical simulations on nearly 80 systems with good universality demonstrate the considerable compression of WANBB on the cost for the unaccepted trials compared with conjugate gradient methods. We also observe across the board significant and universal speedups as well as the superior robustness of WANBB over several widely used methods. The latter point is theoretically established. The implementation of WANBB is pretty simple, in that no a priori physical knowledge is required and only three parameters are present without tuning.

关键词： Crystal structure Structural properties Density functional theory development

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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A force-based gradient descent method for ab initio atomic structure relaxation

arXiv

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arXiv 2022年

作者： Hu, Yukuan Gao, Xingyu Zhao, Yafan Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China

Force-based algorithms for ab initio atomic structure relaxation, such as conjugate gradient methods, usually get stuck in the line minimization processes along search directions, where expensive ab initio calculations are triggered frequently to test trial positions before locating the next iterate. We present a force-based gradient descent method, WANBB, that circumvents the deficiency. At each iteration, WANBB enters the line minimization process with a trial stepsize capturing the local curvature of the energy surface. The exit is controlled by an unrestrictive criterion that tends to accept early trials. These two ingredients streamline the line minimization process in WANBB. The numerical simulations on nearly 80 systems with good universality demonstrate the considerable compression of WANBB on the cost for the unaccepted trials compared with conjugate gradient methods. We also observe across the board significant and universal speedups as well as the superior robustness of WANBB over several widely used methods. The latter point is theoretically established. The implementation of WANBB is pretty simple, in that no a priori physical knowledge is required and only two parameters are present without tuning. © 2022, CC BY.

关键词： Conjugate gradient method

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An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

arXiv

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arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit

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拓扑异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)高压诱导超导电性研究

引用

Science China Materials 2023年第7期66卷 2822-2828页

作者：裴翠颖朱鹏李炳谈赵毅高玲玲李昌华朱世豪张庆华应天平谷林高波缑慧阳姚延荪孙建刘寒雨陈宇林王秩伟姚裕贵齐彦鹏 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of Technology Beijing 100081China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of TechnologyBeijing 100081China Material Science Center Yangtze Delta Region Academy of Beijing Institute of TechnologyJiaxing 314011China State Key Laboratory of Superhard Materials and International Center for Computational Method and Software College of PhysicsJilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China 7 Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing 100190China Center for High Pressure Science and Technology Advanced Research Beijing 100094China Department of Physics and Engineering Physics University of SaskatchewanSaskatoonSaskatchewan S7N 5E2Canada National Laboratory of Solid State Microstructures School of Physics and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China ShanghaiTech Laboratory for Topological Physics ShanghaiTech UniversityShanghai 201210China Department of Physics Clarendon LaboratoryUniversity of OxfordParks RoadOxford OX13PUUK Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech UniversityShanghai 201210China

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急... 详细信息

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急剧下降.值得注意的是,当进一步施加压力到30 GPa以上时,出现了一种新的超导态,且T_(c)直到本实验最高压力仍未饱和.结合XRD和拉曼光谱,我们认为(PbSe)_(5)(Bi_(2)Se_(3))_(6)中两个超导态的出现与压力诱导的结构相变有关.拓扑异质结压力下出现新的量子态,为进一步研究拓扑和超导的关系提供了一个良好的平台.

关键词： high pressure superconductivity heterostructure topological material

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Unifying Farkas lemma and S-lemma: New theory and applications in nonquadratic nonconvex optimization

arXiv

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arXiv 2021年

作者： Yang, Meijia Xia, Yong Wang, Shu School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China State Key Laboratory of Software Development Environment LMIB of the Ministry of Education School of Mathematics and System Sciences Beihang University Beijing100191 China Institute of Computational Mathematics and Scientific/Engineering Computing State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

We unify nonlinear Farkas lemma and S-lemma to a generalized alternative theorem for nonlinear nonconvex system. It provides fruitful applications in globally solving nonconvex non-quadratic optimization problems via ... 详细信息

关键词： Quadratic programming

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DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

arXiv

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arXiv 2022年

作者： Mao, Runze Lin, Minqi Zhang, Yan Zhang, Tianhan John Xu, Zhi-Qin Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Ai for Science Institute Beijing100080 China Caep Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Department of Mechanics and Aerospace Engineering SUSTech Shenzhen518055 China Institute of Natural Sciences School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China MOE-LSC Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China

Recent developments in deep learning have brought many inspirations for the scientific computing community and it is perceived as a promising method in accelerating the computationally demanding reacting flow simulations. In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware. © 202

关键词： Graphics processing unit

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Pressure-Induced Superconductivity in Topological Heterostructure (PbSe)5(Bi2Se3)6

arXiv

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arXiv 2023年

作者： Pei, Cuiying Zhu, Peng Li, Bingtan Zhao, Yi Gao, Lingling Li, Changhua Zhu, Shihao Zhang, Qinghua Ying, Tianping Gu, Lin Gao, Bo Gou, Huiyang Yao, Yansun Sun, Jian Liu, Hanyu Chen, Yulin Wang, Zhiwei Yao, Yugui Qi, Yanpeng School of Physical Science and Technology ShanghaiTech University Shanghai201210 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Material Science Center Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314011 China State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Center for High Pressure Science and Technology Advanced Research Beijing100094 China Department of Physics and Engineering Physics University of Saskatchewan SaskatoonSKS7N 5E2 Canada National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom Shanghai Key Laboratory of High-Resolution Electron Microscopy ShanghaiTech University Shanghai201210 China

Recently, the natural heterostructure of (PbSe)5(Bi2Se3)6 has been theoretically predicted and experimentally confirmed as a topological insulator. In this work, we induce superconductivity in (PbSe)5(Bi2Se3)6 by implementing high pressure. As increasing pressure up to 10 GPa, superconductivity with Tc ~ 4.6 K suddenly appears, followed by an abrupt decrease. Remarkably, upon further compression above 30 GPa, a new superconducting state arises, where pressure raises the Tc to an unsaturated 6.0 K within the limit of our research. Combining XRD and Raman spectroscopies, we suggest that the emergence of two distinct superconducting states occurs concurrently with the pressure-induced structural transition in this topological heterostructure (PbSe)5(Bi2Se3)6. © 2023, CC BY-NC-ND.

关键词： Topology

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