检索结果-内蒙古大学图书馆

Scalable Inter-Domain Network Virtualization

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Sun, Jie Wo, Tianyu Liu, Xudong Ma, Tianjiao Mou, Xudong Lan, Jinghong Zhang, Nan Niu, Jianwei SKLSDE School of Computer Science and Engineering Beihang University Beijing China SKLSDE School of Software Beihang University Beijing China Research Center of Big Data and Computational Intelligence Hangzhou Institute of Beihang University Hangzhou China

To realize inter-domain network virtualization for hybrid cloud, the following challenges must be resolved. (1) Scalability. The network virtualization system should allow tenant virtual networks to use on-demand addressing schema and span multiple domains. This requirement, combined with the expansion of the network scale, can lead to a rapid increase in flow rule consumption, posing a significant challenge for network scalability. (2) Inter-domain network flow scheduling. To allocate bandwidth for inter-domain network flows with low cost and guaranteed bandwidth is complex (NP-hard). Existing researches fail to address this problem in a flexible yet efficient manner. This paper proposes a network virtualization solution that jointly considers the two challenges. Towards the scalability challenge, we use MAC translation to encode location hierarchy and tenant information into MAC addresses, thereby enabling L2 forwarding rule aggregation on each switch. Besides, we offload the L3 virtual network rules onto the first hop software switches and tackle L3 routing through VMAC-based forwarding. We formally prove that our approach can bound the worst-case rule consumption by combining these two techniques. Towards the second challenge, we formulate the inter-domain network flow scheduling (INFOS) problem into an integer linear programming (ILP) problem and prove its NP-hardness. We also propose a heuristic algorithm that can guarantee bandwidth lower- bound while avoiding causing significant profit loss or introducing too many network updates. Evaluation shows that our approach can reduce the average flow rule consumption significantly, compared with state-of-the-art approaches. Besides, towards the same bandwidth guarantee target, we can avoid approximately 95% of the profit loss and 92% of the network update operations. © 2022, The Authors. All rights reserved.

关键词： Profitability

DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional theory code using finite-element discretization

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Das, Sambit Motamarri, Phani Subramanian, Vishal Rogers, David M. Gavini, Vikram Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Computational and Data Sciences Indian Institute of Science Bangalore India National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States

We present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246, 106853 (2020)], to conduct fast and accurate large-scale density functional theory (DFT) calculations (reaching ∼ 100, 000 electrons) on both many-core CPU and hybrid CPU-GPU computing architectures. This work involves improvements in the real-space formulation-via an improved treatment of the electrostatic interactions that substantially enhances the computational efficiency-as well high-performance computing aspects, including the GPU acceleration of all the key compute kernels in DFT-FE. We demonstrate the accuracy by comparing the ground-state energies, ionic forces and cell stresses on a wide-range of benchmark systems against those obtained from widely used DFT codes. Further, we demonstrate the numerical efficiency of our implementation, which yields ∼ 20× CPU-GPU speed-up by using GPU acceleration on hybrid CPU-GPU nodes. Notably, owing to the parallel-scaling of the GPU implementation, we obtain wall-times of 80−140 seconds for full ground-state calculations, with stringent accuracy, on benchmark systems containing ∼ 6, 000 − 15, 000 electrons. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

Biomedical data and AI

学校读者我要写书评

暂无评论

science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

Multi-dimensional Graph Linear Canonical Transform

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Na Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for processing these graph M-D signals in the linear canonical transform domain. This paper proposes the two-dimensional graph linear canonical transform based on the central discrete dilated Hermite function (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). Then, extending 2-D CDDHFs-GLCT and 2-D CM-CC-CM-GLCT to M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of the computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analysis shows that the computational complexity of M-D CM-CC-CM-GLCT algorithm is obviously reduced. Simulation results indicate that M-D CM-CC-CM-GLCT achieves comparable additivity to M-D CDDHFs-GLCT, while M-D CM-CC-CM-GLCT exhibits better reversibility. Finally, M-D GLCT is applied to data compression to show its application advantages. The experimental results reflect the superiority of M-D GLCT in the algorithm design and implementation of data compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

Adaptive algorithm for quantum circuit simulation

学校读者我要写书评

暂无评论

Physical Review A 2020年第4期101卷 042335-042335页

作者： Roman Schutski Danil Lykov Ivan Oseledets Center for Computational and Data-Intensive Science and Engineering Skoltech Skolkovo Innovation Center Moscow Region 121205 Russian Federation Moscow Institute of Physics and Technology Dolgoprudny Moscow Region 141701 Russian Federation

Efficient simulation of quantum computers is essential for the development and validation of near-term quantum devices and the research on quantum algorithms. Up to date, two main approaches to simulation have been use, based on either full-state or single-amplitude evaluation. We propose an algorithm that efficiently interpolates between these two possibilities. Our approach elucidates the connection between quantum circuit simulation and partial evaluation of expressions in tensor algebra.

关键词： Quantum benchmarking Quantum gates Quantum simulation

An analysis of reconstruction noise from undersampled 4D flow MRI

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Partin, Lauren Schiavazzi, Daniele E. Long, Carlos A. Sing Department Of Applied And Computational Mathematics And Statistics University Of Notre Dame Notre DameIN United States Institute For Mathematical And Computational Engineering Institute For Biological And Medical Engineering Pontificia Universidad Católica De Chile Santiago Chile ANID - Millennium Science Initiative Program Millennium Nucleus Center For The Discovery Of Structures In Complex Data Chile ANID - Millennium Science Initiative Program Millennium Nucleus Center For Cardiovascular Magnetic Resonance Chile

Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease. To reduce the acquisition times, reconstruction methods from undersampled measurements are routinely used, that leverage representations designed to increase image compressibility. Reconstructed anatomical and hemodynamic images may present visual artifacts. Although some of these artifact are essentially reconstruction errors, and thus a consequence of undersampling, others may be due to measurement noise or the random choice of the sampled frequencies. Said otherwise, a reconstructed image becomes a random variable, and both its bias and its covariance can lead to visual artifacts;the latter leads to spatial correlations that may be misconstrued for visual information. Although the nature of the former has been studied in the literature, the latter has not received as much attention. In this study, we investigate the theoretical properties of the random perturbations arising from the reconstruction process, and perform a number of numerical experiments on simulated and MR aortic flow. Our results show that the correlation length remains limited to two to three pixels when a Gaussian undersampling pattern is combined with recovery algorithms based on 1-norm minimization. However, the correlation length may increase significantly for other undersampling patterns, higher undersampling factors (i.e., 8× or 16× compression), and different reconstruction methods. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Liyan Yang, Qinyu Wang, Cong Wang, Wei Pan, Jinshan Su, Zhixun School of Mathematical Sciences Dalian University of Technology Dalian China The Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province The Liaoning Center for Applied Mathematics China The Department of Computing The Hong Kong Polytechnic University Hong Kong Dalian University of Technology Dalian China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme. The DFSA and DFN respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to facilitate better restoration. In the coarse training stage, our C2FDFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training scheme. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on 3 tasks, including image deraining, image deblurring, and real image denoising. Code is available at https://***/wlydlut/C2F-DFT. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Su, Jinyan Zhao, Changhong Wang, Di Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Information Engineering The Chinese University of Hong Kong Hong Kong Provable Responsible AI and Data Analytics Lab India Computational Bioscience Research Center India Division of CEMSE King Abdullah University of Science and Technology Saudi Arabia

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general dp spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space;(2) unconstrained DP-SCO in dp space;(3) DP-SCO with heavy-tailed data over a constrained and bounded set in dp space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set, rather than the dimension of the space. Moreover, we also show the bound for strongly convex functions is optimal up to a logarithmic factor. For problems (2) and (3), we propose several novel algorithms and provide the first theoretical results for both cases when 1 © 2023, CC BY.

关键词： Convex optimization

Reconstructing the evolution history of networked complex systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Junya Zhang, Yi-Jiao Xu, Cong Li, Jiaze Sun, Jiachen Xie, Jiarong Feng, Ling Zhou, Tianshou Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht6200MD Netherlands Tencent Inc Shenzhen518000 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China 138632 Singapore Department of Physics National University of Singapore 117551 Singapore School of Mathematics Sun Yat-sen University Guangzhou510275 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks. © 2024, CC BY-NC-ND.

关键词： Restoration