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${\sf DeepNC}$DeepNC: Deep Generative Network Completion

IEEE Transactions on Pattern Analysis and Machine Intelligence 2020年第4期44卷 1837-1852页

作者： Cong Tran Won-Yong Shin Andreas Spitz Michael Gertz Department of Computer Science and Engineering Dankook University Yongin Republic of Korea Machine Intelligence & Data Science Laboratory Yonsei University Seoul Republic of Korea School of Mathematics and Computing (Computational Science and Engineering) Yonsei University Seoul Republic of Korea School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Lausanne Switzerland Institute of Computer Science Heidelberg University Heidelberg Germany

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missing parts of the networks by performing network completion should precede downstream applications. However, despite this need, the recovery of missing nodes and edges in such incomplete networks is an insufficiently explored problem due to the modeling difficulty, which is much more challenging than link prediction that only infers missing edges. In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs. Specifically, our method first learns a likelihood over edges via an autoregressive generative model, and then identifies the graph that maximizes the learned likelihood conditioned on the observable graph topology. Moreover, we propose a computationally efficient ${\sf DeepNC}$ algorithm that consecutively finds individual nodes that maximize the probability in each node generation step, as well as an enhanced version using the expectation-maximization algorithm. The runtime complexities of both algorithms are shown to be almost linear in the number of nodes in the network. We empirically demonstrate the superiority of DeepNC over state-of-the-art network completion approaches.

关键词： Matrix decomposition Prediction algorithms Deep learning Pattern analysis Social networking (online) Predictive models

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General liquid-crystal theory for anisotropically shaped molecules: Symmetry, orientational order parameters, and system free energy

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Physical Review E 2020年第6期102卷 062701-062701页

作者： Jie Xu Jeff Z. Y. Chen LSEC & NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing (ICMSEC) Academy of Mathematics and Systems Science (AMSS) Chinese Academy of Sciences Beijing 100190 China. Department of Physics and Astronomy University of Waterloo Ontario Canada N2L 3G1.

A general theory of liquid crystals is presented, starting from the group-theory symmetry analysis of the constituting molecules. A particular attention is paid to the type of elastic free-energies and their relationships with the molecular symmetries. The orientational order-parameter tensors are identified for each molecular symmetry, in a consideration of consistently keeping the leading, characteristic elastic free energies in a model. The order parameters are expressed in terms of symmetric traceless tensors, some of high orders, for all major molecular symmetries, including seven groups of axial symmetries and seven groups of polyhedral symmetries. For spatially inhomogeneous liquid crystals, the couplings of these tensors in the elastic energies are derived by expanding the interaction energies between these molecules. The aim is to provide a general view of the molecular symmetries of individual molecules, orientational order parameters characterizing the orientational distribution functions, and the elastic free energies, all under one single group-theory approach.

关键词： Bent-core liquid crystals Liquid crystals Lyotropic liquid crystals Nematic liquid crystals

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Meson spectroscopy in the Sp(4) gauge theory with three antisymmetric fermions

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Physical Review D 2025年第7期111卷 074511-074511页

作者： Ed Bennett Deog Ki Hong Ho Hsiao Jong-Wan Lee C.-J. David Lin Biagio Lucini Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University (Bay Campus) Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Korea Extreme Physics Institute Pusan National University Busan 46241 Korea Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Particle Theory and Cosmology Group Center for Theoretical Physics of the Universe Institute for Basic Science (IBS) Daejeon 34126 Korea Centre for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University (Bay Campus) Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom Centre for Mathematical Sciences University of Plymouth Plymouth PL4 8AA United Kingdom

We report the results of an extensive numerical study of the Sp(4) lattice gauge theory coupled to fermion matter content consisting of three (Dirac) flavors, transforming in the two-index antisymmetric representation of the gauge group. In the presence of (degenerate) fermion masses, the theory has an enhanced global SU(6) symmetry, broken explicitly and spontaneously to its SO(6) subgroup. This symmetry breaking pattern makes the theory interesting for applications in the context of composite Higgs models, as well as for the implementation of top partial compositeness. Alternatively, it can also provide a dynamical realization of the strongly interacting massive particle paradigm for the origin of dark matter. We adopt the standard plaquette gauge action, along with the Wilson-Dirac formulation for the fermions, and apply the (rational) hybrid Monte Carlo algorithm in our ensemble generation process. We monitor the autocorrelation and topology of the ensembles. We explore the bare parameter space, and identify the weak and strong coupling regimes, which are separated by a line of first-order bulk phase transitions.

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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

引用

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 v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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PRACTICAL NEWTON METHODS FOR ELECTRONIC STRUCTURE CALCULATIONS ∗

arXiv

引用

arXiv 2020年

作者： Dai, Xiaoying Zhang, Liwei 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 some practical Newton methods for electronic structure calculations. We show the convergence and the local quadratic convergence rate for the Newton method when the Newton search directions are well-obtained. In particular, we investigate some basic implementation issues in determining the search directions and step sizes which ensures the convergence of the subproblem at each iteration and accelerates the algorithm, respectively. It is shown by our numerical experiments that our Newton methods perform better than the existing conjugate gradient method, and the Newton method with the adaptive step size strategy is even more efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI

arXiv

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arXiv 2025年

作者： Karpov, Petr Marek, Andreas Melson, Tobias Pöppl, Alexander Yu, Victor Wen-Zhe Hourahine, Ben Garcia, Alberto Dawson, William Yao, Yi Huhn, William Moussa, Jonathan Hall, Sam Maurer, Reinhard Herath, Uthpala Lion, Konstantin Kokott, Sebastian Blum, Volker Max Planck Computing and Data Facility Garching Germany Intel Corporation Santa ClaraCA United States Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Physics SUPA University of Strathclyde John Anderson Building107 Rottenrow GlasgowG4 0NG United Kingdom Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain RIKEN Center for Computational Science Kobe650-0047 Japan Molecular Simulations from First Principles e.V. BerlinD-14195 Germany Intel Corporation 500 Beaver Brook Road BoxboroughMA01719 United States Molecular Sciences Software Institute BlacksburgVA24060 United States University of Warwick United Kingdom Department of Chemistry Duke University DurhamNC27708 United States

In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims. ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem. ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others. ELSI, ELPA, and other solver libraries supported in ELSI are mature, well-tested software, ensuring efficient support for large-scale simulations on current HPC systems. Future plans for ELPA include further optimization of GPU routines, particularly integrating the GPU collective communication libraries (NVIDIA’s NCCL and AMD’s RCCL) into the solution tridiagonal of the matrix problem and backtransformation of the eigenvectors in standard eigenproblem solvers. Similarly expanding support for Intel GPUs through deeper integration with Intel’s oneAPI libraries, specifically oneCCL, is planned. These advancements aim to ensure that ELPA, accessible either with its own Fortran/C/C++ APIs or via ELSI, remains at the forefront of large-scale computations, offering researchers

关键词： Open source software

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Cooperation dynamics under pandemic risks and heterogeneous economic interdependence

arXiv

引用

arXiv 2021年

作者： Serrano, Manuel Chica Hernández, Juan M. Santos, Francisco C. Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia University of Las Palmas de Gran Canaria Las Palmas35017 Spain INESC-ID Instituto Superior Técnico Universidade de Lisboa Porto Salvo2744-016 Portugal

The spread of COVID-19 and ensuing containment measures have accentuated the profound interdependence among nations or regions. This has been particularly evident in tourism, one of the sectors most affected by uncoordinated mobility restrictions. The impact of this interdependence on the tendency to adopt less or more restrictive measures is hard to evaluate, more so if diversity in economic exposures to citizens' mobility are considered. Here, we address this problem by developing an analytical and computational game-theoretical model encompassing the conflicts arising from the need to control the economic effects of global risks, such as in the COVID-19 pandemic. The model includes the individual costs derived from severe restrictions imposed by governments, including the resulting economic interdependence among all the parties involved in the game. By using tourism-based data, the model is enriched with actual heterogeneous income losses, such that every player has a different economic cost when applying restrictions. We show that economic interdependence enhances cooperation because of the decline in the expected payoffs by free-riding parties (i.e., those neglecting the application of mobility restrictions). Furthermore, we show (analytically and through numerical simulations) that these cross-exposures can transform the nature of the cooperation dilemma each region or country faces, modifying the position of the fixed points and the size of the basins of attraction that characterize this class of games. Finally, our results suggest that heterogeneity among regions may be used to leverage the impact of intervention policies by ensuring an agreement among the most relevant initial set of cooperators. © 2021, CC BY-NC-ND.

关键词： Game theory

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An a posteriori error estimator for the spectral fractional power of the Laplacian

arXiv

引用

arXiv 2022年

作者： Bulle, Raphaël Barrera, Olga Bordas, Stéphane P.A. Chouly, Franz Hale, Jack S. Institute of Computational Engineering University of Luxembourg 6 Avenue de la Fonte Esch-sur-Alzette4362 Luxembourg Université Bourgogne Franche-Comté Institut de Mathématiques de Bourgogne Dijon21078 France Center for Mathematical Modeling Department of Mathematical Engineering University of Chile IRL 2807 - CNRS Santiago Chile School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Departamento de Ingenería Matématica CI2MA Universidad de Concepción Casilla 160-C Concepción Chile

We develop a novel a posteriori error estimator for the L2 error committed by the finite element discretization of the solution of the fractional Laplacian. Our a posteriori error estimator takes advantage of the semi-discretization scheme using rational approximations which allow to reformulate the fractional problem into a family of non-fractional parametric problems. The estimator involves applying the implicit Bank-Weiser error estimation strategy to each parametric non-fractional problem and reconstructing the fractional error through the same rational approximation used to compute the solution to the original fractional problem. In addition we propose an algorithm to adapt both the finite element mesh and the rational scheme in order to balance the discretization errors. We provide several numerical examples in both two and three-dimensions demonstrating the effectivity of our estimator for varying fractional powers and its ability to drive an adaptive mesh refinement *** Codes 65N15, 65N30 © 2022, CC BY-NC-ND.

关键词： Finite element method

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