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Lecture Notes in Computational science and Engineering 2006年 50卷

作者： Bücker, Martin Corliss, George Hovland, Paul Naumann, Uwe Norris, Boyana Institute for Scientific Computing RWTH Aachen University D-52056 Aachen Germany Department of Electrical and Computer Engineering Marquette University 1515 W. Wisconsin Avenue P.O. Box 1881 Milwaukee WI 53201-1881 United States Software and Tools for Computational Engineering RWTH Aachen University D-52056 Aachen Germany Mathematics and Computer Science Division Argonne National Laboratory 9700 S Cass Ave Argonne IL 60439 United States

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Optimal provable robustness of quantum classification via quantum hypothesis testing

arXiv

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arXiv 2020年

作者： Weber, Maurice Liu, Nana Li, Bo Zhang, Ce Zhao, Zhikuan Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China Department of Computer Science University of Illinois UrbanaIL61801 United States

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks. In order to develop defence mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in presence of natural noise sources or adversarial manipulation. From the observation that measurements involved in quantum classification algorithms are naturally probabilistic, we uncover and formalize a fundamental link between binary quantum hypothesis testing and provably robust quantum classification. This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial. Based on this result, we develop practical protocols to optimally certify robustness. Finally, since this is a robustness condition against worst-case types of noise, our result naturally extends to scenarios where the noise source is known. Thus, we also provide a framework to study the reliability of quantum classification protocols beyond the adversarial, worst-case noise scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Outcomes from the DOE workshop on turbulent flow simulation at the exascale 46

Outcomes from the DOE workshop on turbulent flow simulation ...

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46th AIAA Fluid Dynamics Conference, 2016

作者： Sprague, Michael A. Boldyrev, Stanislav Chang, Choong-Seock Fischer, Paul Grout, Ray Gustafson, William I. Hittinger, Jeffrey A. F. Merzari, Elia Moser, Robert National Renewable Energy Laboratory 15013 DenverWest Parkway GoldenCO80401 United States University of Wisconsin-Madison MadisonWI53706 United States Princeton University PrincetonNJ08543 United States University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Renewable Energy Laboratory GoldenCO80401 United States Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Livermore National Laboratory LivermoreCA94551 United States Argonne National Laboratory ArgonneIL60439 United States University of Texas at Austin AustinTX78712 United States Computational Science Center 15013 DenverWest Parkway United States Physics Department 1150 University Avenue United States Princeton Plasma Physics Laboratory P.O. Box 451 United States Department of Computer Science 201 North Goodwin Avenue United States Computational Science Center 5013 Denver West Parkway United States Atmospheric Sciences and Global Change Division P.O. Box 999 K9-30 United States Scientific Computing Group Center for Applied Scientific Computing PO Box 808 L-561 United States Mathematics and Computer Science 9700 S. Cass Avenue United States Institute for Computational Engineering and Sciences and Department of Mechanical Engineering 201 East 24th United States

ISBN: (数字)9781624104367

ISBN: (纸本)9781624104367

This paper summarizes the outcomes from the Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop, which was held 4-5 August 2015, and was sponsored by the U.S. Department of Energy (DOE) Office of Advanced scientific computing Research (ASCR). The workshop objectives were to define and describe the challenges and opportunities that computing at the exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the DOE applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The work-shop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Turbulent flow

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Junqi Yin Siming Liang Siyan Liu Feng Bao Hristo G. Chipilski Dan Lu Guannan Zhang National Center for Computational Science Oak Ridge National Laboratory Oak Ridge TN USA Department of Mathematics Florida State University Tallahassee FL USA Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA Department of Scientific Computing Florida State University Tallahassee FL USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF); and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework’s exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations.

关键词： Earth Foundation models Computational modeling High performance computing Scalability Atmospheric modeling Supercomputers Real-time systems Numerical models Data assimilation

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scientific Data Management: An Orphan in the Database Community?

Scientific Data Management: An Orphan in the Database Commun...

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International Conference on Data Engineering

作者： Randal Burns Susan B. Davidson Yannis Ioannidis Miron Livny Jignesh M. Patel Moderator Assistant Professor Department of Computer Science Johns Hopkins University. Department Chair Weiss Professor of Computer and Information Science ACM Fellow a Fulbright scholar founding co-Director of the Center for Bioinformatics at UPenn (PCBI) Professor Department of Informatics & Telecommunications University of Athens ACM Fellow ACM SIGMOD Vice-Chair member of the Scientific Advisory Board Max Planck Institute for Informatics. Professor of Computer Sciences director of the Center for High Throughput Computing Associate Professor faculty member EECS department University of Michigan

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A Fast Rankability Method Based on Fuzzy Entropy

SSRN

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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Listpodu 17 Czech Republic Department of Industrial Engineering University of Chile O’Higgins 1058 Santiago Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China School of Artificial Intelligence Nanjing University of Information Science and Technology 219 Ningliu Road China Instituto Sistemas Complejos de Ingeniería Santiago Chile Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

Rankability is a fundamental concept that evaluates a dataset's inherent capacity to establish a meaningful ranking of its elements, with widespread applications in web search, data mining, cybersecurity, and machine *** its significance, relatively little focus has been on evaluating datasets for ranking suitability. Several rankability measures have emerged, such as comparing graphs representing datasets with complete dominance graphs using combinatorial and linear algebraic techniques. However, these approaches typically involve solving eigenvalue problems for large matrices or training regression models on extensive datasets. In this paper, we propose to adopt efficient and straightforward statistical metrics, entropy, and variance to develop a novel rankability method. Our method is designed to be fast and efficient, addressing the challenges posed by existing techniques. We conducted experiments using three football tournament datasets, and the results demonstrate a strong correlation scores (correlation coefficients) in terms of Spearman and Kendall's tau evaluations with Elo rating, a system developed by Arpad Elo in the 1950s and applied in many competitions. Specifically, the Spearman correlation between the proposed method and the Elo rating reached 0.9205 on Atlantic Coast data with a p-value of 0.0004, which is higher than the compared algorithm by 0.2424 in correlation score. © 2024, The Authors. All rights reserved.

关键词： Entropy

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A hybrid FEM-PINN method for time-dependent partial differential equations

arXiv

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arXiv 2024年

作者： Feng, Xiaodong Shangguan, Haojiong Tang, Tao Wan, Xiaoliang Zhou, Tao Division of Science and Technology BNU-HKBU United International College Zhuhai519087 China School of Electrical and Computer Engineering Guangzhou Nanfang College Guangzhou510970 China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks. In contrast to the conventional deep learning-based formulation where the neural network is defined on a spatiotemporal domain, our methodology utilizes finite element basis functions in the time direction where the space-dependent coefficients are defined as the output of a neural network. We then apply the Galerkin or collocation projection in the time direction to obtain a system of PDEs for the space-dependent coefficients which is approximated in the framework of PINN. The advantages of such a hybrid formulation are twofold: statistical errors are avoided for the integral in the time direction, and the neural network’s output can be regarded as a set of reduced spatial basis functions. To further alleviate the difficulties from high dimensionality and low regularity, we have developed an adaptive sampling strategy that refines the training set. More specifically, we use an explicit density model to approximate the distribution induced by the PDE residual and then augment the training set with new time-dependent random samples given by the learned density model. The effectiveness and efficiency of our proposed method have been demonstrated through a series of numerical experiments. © 2024, CC BY.

关键词： Partial differential equations

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Learning Interface Conditions in Domain Decomposition Solvers

arXiv

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arXiv 2022年

作者： Taghibakhshi, Ali Nytko, Nicolas Zaman, Tareq MacLachlan, Scott Olson, Luke West, Matthew Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Computer Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Scientific Computing Program Memorial University of Newfoundland and Labrador St. John'sNL Canada Mathematics and Statistics Memorial University of Newfoundland and Labrador St. John'sNL Canada

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz domain decomposition methods to unstructured-grid problems, using Graph Convolutional Neural Networks (GCNNs) and unsupervised learning to learn optimal modifications at subdomain interfaces. A key ingredient in our approach is an improved loss function, enabling effective training on relatively small problems, but robust performance on arbitrarily large problems, with computational cost linear in problem size. The performance of the learned linear solvers is compared with both classical and optimized domain decomposition algorithms, for both structured- and unstructured-grid problems. © 2022, CC BY.

关键词： Domain decomposition methods

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Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms

arXiv

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arXiv 2023年

作者： Wang, Ziyue Liu, Ya-Feng Wang, Zhaorui Yu, Wei 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 School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. While the scaling law for the covariance-based activity detection in the single-cell scenario has been extensively analyzed in the literature, this paper aims to analyze the scaling law for the covariance-based activity detection in the multi-cell massive MIMO system. Specifically, this paper demonstrates a quadratic scaling law in the multi-cell system, under the assumption that the path-loss exponent of the fading channel γ > 2. This finding shows that, in the multi-cell massive MIMO system, the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). Moreover, in addition to analyzing the scaling law for the signature sequences randomly and uniformly distributed on a sphere, the paper also establishes the scaling law for signature sequences based on a finite alphabet, which are easier to generate and store. Finally, this paper proposes two efficient accelerated coordinate descent (CD) algorithms with a convergence guarantee for solving the device activity detection problem. The first algorithm reduces the complexity of CD by using an inexact coordinate update strategy. The second algorithm avoids unnecessary computations of CD by using an active set selection strategy. Simulation results show that the proposed algorithms exhibit excellent performance in terms of computational efficiency and detection error probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

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