检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Guo, Zehua Chen, Weikun Liu, Ya-Feng Xu, Yang Zhang, Zhi-Li Department of Computer Science and Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States 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 School Of Computer Science Fudan University Shanghai200433 China

To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networking (SDN). The coexistence of legacy and SDN devices gives rise to a hybrid SDN. Existing hybrid SDNs do not consider the potential performance issues introduced by a centralized SDN controller: flow requests processed by a highly loaded controller may experience long tail processing delay;inappropriate multi-controller deployment could increase the propagation delay of flow requests. In this paper, we propose to jointly consider the deployment of SDN switches and their controllers for hybrid SDNs. We formulate the joint problem as an optimization problem that maximizes the number of flows that can be controlled and managed by the SDN and minimizes the propagation delay of flow requests between SDN controllers and switches under a given upgrade budget constraint. We show this problem is NP-hard. To efficiently solve the problem, we propose some techniques (e.g., strengthening the constraints and adding additional valid inequalities) to accelerate the global optimization solver for solving the problem for small networks and an efficient heuristic algorithm for solving it for large networks. The simulation results from real network topologies illustrate the effectiveness of the proposed techniques and show that our proposed heuristic algorithm uses a small number of controllers to manage a high amount of flows with good performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Software defined networking

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Formation of singularity for the rotating shallow water system

arXiv

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arXiv 2020年

作者： Huang, Yupei Chunjing, X.I.E. Department of mathematics Duke University United States School of mathematical Sciences Institute of Natural Sciences Ministry of Education Key Laboratory of Scientific and Engineering Computing SHL-MAC Shanghai Jiao Tong Vniversity 800 Dongchuan Road Shanghai China

In this paper, we investigate the formation of singularity for general two dimensional and radially symmetric solutions for rotating shallow water system from different aspects. First, the formation of singularity is proved via the study for the associated moments for two dimensional solutions. For the radial symmetric solutions, the formation of singularity is established for the initial data with compact support. Finally, the global existence or formation of singularity for the radial symmetric solutions of the rotating shallow water system are analyzed in detail when the solutions are of the form with separated variables. Copyright © 2020, The Authors. All rights reserved.

关键词： Waterworks

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Implications of circular strategies on energy, water, and GHG emissions in informal housing in Lima

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Energy and Buildings 2025年 344卷

作者： Sarmiento-Pastor, Jaime Lira-Chirif, Andrés Rondinel-Oviedo, Daniel R. Keena, Naomi Dyson, Anna Raugei, Marco Acevedo-De-los-Ríos, Alejandra Scientific Research Institute University of Lima Lima Peru Peter Guo-Hua Fu School of Architecture Faculty of Engineering McGill University Montreal Canada Yale Center for Ecosystems + Architecture 180 York Street New Haven 06511 CT United States School of Engineering Computing and Mathematics Oxford Brookes University United Kingdom Center for Life Cycle Analysis Columbia University NY United States Louvain Research Institute for Landscape Architecture and Built Environment (LAB) University of Louvain Louvain-la-Neuve Belgium

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THU676 Real-time Assessment Of The Beneficial Role Of Computer-Aided Diagnosis In The Diagnosis Of Thyroid Nodules On Ultrasound

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Journal of the Endocrine Society 2023年第SUPPLEMENT_1期7卷

作者： Youngsook Kim Sungjae Shin Eunjung Lee Daham Kim Jin Young Kwak Department of Internal Medicine Institute of Endocrine Research Yonsei University College of Medicine Seoul Korea Republic of Department of Internal Medicine National Health Insurance Service Ilsan Hospital Gyeonggi-do Korea Republic of School of Mathematics and Computing (Computational Science and Engineering) Yonsei University Seoul Korea Republic of Department of Radiology Severance Hospital Research Institute of Radiological Science Seoul Korea Republic of

Disclosure: Y. Kim:None.S. Shin:None.E. Lee:None.D. Kim:None.J. Kwak:None.

Disclosure: Y. Kim: None. S. Shin: None. E. Lee: None. D. Kim: None. J. Kwak: None.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

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arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Adding higher-order spherical harmonics in nonspinning eccentric binary black hole merger waveform models

引用

Physical Review D 2025年第12期111卷 124023-124023页

作者： Tousif Islam Gaurav Khanna Scott E. Field Kavli Institute for Theoretical Physics University of California Santa Barbara Kohn Hall Lagoon Road Santa Barbara California 93106 USA Theoretical AstroPhysics Including Relativity and Cosmology California Institute of Technology Pasadena California USA Department of Physics and Center for Computational Research University of Rhode Island Kingston Rhode Island 02881 USA Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA

gwnrhme is a recently developed framework that seamlessly converts a multimodal (i.e., with several spherical harmonic modes) quasicircular waveform into a multimodal eccentric waveform if the quadrupolar eccentric waveform is known. Here, we employ the gwnrhme framework to combine a multimodal quasicircular numerical relativity surrogate waveform model nrhybsur3dq8 and quadrupolar nonspinning post-Newtonian eccentric waveform model eccentricimr to construct multimodal nonspinning eccentric model nrhybsur3dq8-gwnrhme. Using a total of 35 eccentric numerical relativity (NR) simulations obtained from the SXS and RIT catalogs, we demonstrate that nrhybsur3dq8-gwnrhme model predictions agree well with NR (with typical relative L2 errors of ∼0.01 for the dominant quadrupolar mode) for mass ratios 1≤q≤4 and eccentricities up to ∼0.2 measured about 10 cycles before the merger. Our frequency-domain mismatches (calculated assuming advanced LIGO design sensitivity curve) are mostly below 0.01. To demonstrate the modularity of the gwnrhme framework, we further combine eccentricimr with the bhptnrsur1dq1e4bhptnrsur1dq1e4 model and develop a nonspinning eccentric model named bhptnrsur1dq1e4-gwnrhme. Finally, we develop a different variant of these models by replacing eccentricimr with eccentrictd. Both the gwnrhme framework and associated models are available through the gwmodels package.

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An orthogonalization-free parallelizable framework for all-electron calculations in density functional theory

arXiv

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arXiv 2020年

作者： Bin, G.A.O. Guanghui, H.U. Kuang, Yang Xin, L.I.U. ICTEAM Institute UCLouvain Louvain-la-Neuve Belgium Department of Mathematics University of Macau Macao SAR China Zhuhai UM Science & Technology Research Institute Guangdong Province China Department of Mathematics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

All-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations, both nonlinear eigenvalue problem and total energy minimization problem pursue orthogonal solutions. Most existing algorithms for solving these two models invoke orthogonalization process either explicitly or implicitly in each iteration. Their efficiency suffers from this process in view of its cubic complexity and low parallel scalability in terms of the number of electrons for large scale systems. To break through this bottleneck, we propose an orthogonalization-free algorithm framework based on the total energy minimization problem. It is shown that the desired orthogonality can be gradually achieved without invoking orthogonalization in each iteration. Moreover, this framework fully consists of Basic Linear Algebra Subprograms (BLAS) operations and thus can be naturally parallelized. The global convergence of the proposed algorithm is established. We also present a precondition technique which can dramatically accelerate the convergence of the algorithm. The numerical experiments on all-electron calculations show the efficiency and high scalability of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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Gradient flow based discretized kohn-sham density functional theory

arXiv

引用

arXiv 2019年

作者： Dai, Xiaoying Wang, Qiao Zhou, Aihui LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In this paper, we propose and analyze a gradient ow based Kohn-Sham density functional theory. First, we prove that the critical point of the gradient ow based model can be a local minimizer of the Kohn-Sham total energy. Then we apply a midpoint scheme to carry out the temporal discretization. It is shown that the critical point of the Kohn-Sham energy can be well-approximated by the scheme. In particular, based on the midpoint scheme, we design an orthogonality preserving iteration scheme to minimize the Kohn-Sham energy and show that the orthogonality preserving iteration scheme produces approximations that are orthogonal and conver- gent to a local minimizer under reasonable assumptions. Finally, we report numerical experiments that support our theory. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamical systems

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Structural connectome atlas construction in the space of riemannian metrics

arXiv

引用

arXiv 2021年

作者： Campbell, Kristen M. Dai, Haocheng Su, Zhe Bauer, Martin Thomas Fletcher, P. Joshi, Sarang C. Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Department of Neurology University of California Los Angeles Los AngelesCA United States Department of Mathematics Florida State University TallahasseeFL United States Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Utah Salt Lake CityUT United States

The structural connectome is often represented by fiber bundles generated from various types of tractography. We propose a method of analyzing connectomes by representing them as a Riemannian metric, thereby viewing them as points in an infinite-dimensional manifold. After equipping this space with a natural metric structure, the Ebin metric, we apply object-oriented statistical analysis to define an atlas as the Fréchet mean of a population of Riemannian metrics. We demonstrate connectome registration and atlas formation using connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project. Copyright © 2021, The Authors. All rights reserved.

关键词： Population statistics

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