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RANK: Large-scale inference with graphical nonlinear knockoffs

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arXiv 2017年

作者： Fan, Yingying Demirkaya, Emre Li, Gaorong Lv, Jinchi Data Sciences and Operations Department Marshall School of Business University of Southern California Los AngelesCA90089 United States Department of Mathematics University of Southern California Los AngelesCA90089 United States Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the power and robustness for the modelfree knockoffs procedure introduced recently in Candès, Fan, Janson and Lv (2016) in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity conditions, the power of the oracle knockoffs procedure with known covariate distribution in high-dimensional linear models is asymptotically one as sample size goes to infinity. When moving away from the ideal case, we suggest the modified model-free knockoffs method called graphical nonlinear knockoffs (RANK) to accommodate the unknown covariate distribution. We provide theoretical justifications on the robustness of our modified procedure by showing that the false discovery rate (FDR) is asymptotically controlled at the target level and the power is asymptotically one with the estimated covariate distribution. To the best of our knowledge, this is the first formal theoretical result on the power for the knockoffs procedure. Simulation results demonstrate that compared to existing approaches, our method performs competitively in both FDR control and power. A real data set is analyzed to further assess the performance of the suggested knockoffs procedure. Copyright © 2017, The Authors. All rights reserved.

关键词： Nonlinear systems

Containment for double-integrator multi-agent systems in heterogeneous networks

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Containment for double-integrator multi-agent systems in het...

IEEE Annual Conference on Decision and Control

作者： Jiahu Qin Wei Xing Zheng Huijun Gao Qichao Ma University of Science and Technology of China Hefei China School of Computing Engineering and Mathematics University of Western Sydney Penrith NSW 2751 Australia Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150080 Heilongjiang Province China Department of Automation University of Science and Technology of China Hefei China

ISBN: (纸本)9781467360890

This paper is concerned with the containment control for a group of double-integrator agents with multiple dynamic leaders communicating over heterogeneous networks, i.e., the position and velocity network topologies are modeled by different graphs. Different containment control algorithms are proposed and analyzed for the two cases that the leaders move with the same constant velocity and the same time-varying velocity, respectively. For the former case the sufficient conditions provided for guaranteeing the containment control are structural and thus easy to verify. Though the conditions proposed for the latter case that leaders move with time-varying velocity are algebraic, such algebraic conditions can be satisfied if the position and velocity network topologies among the follower agents are undirected.

关键词： Network topology Eigenvalues and eigenfunctions Educational institutions Symmetric matrices Heuristic algorithms Algorithm design and analysis Vectors

DE/RM-MEDA: A new hybrid multi-objective generator

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arXiv 2019年

作者： Rmalano, Abel Saâ J. Guanjun, D.U. Tong, Guoxiang Xiong, Naixue Shanghai Key Laboratory of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK74464 United States

Under the condition of Karush-Kuhn-Tucker, the Pareto Set (PS) in the decision area of an m-objective optimization problem is a piecewise continuous (m-1)-D manifold. For illustrate the degree of convergence of the population, we employed the ratio of the sum of the first (m-1) largest eigenvalue of the population's covariance matrix of the sum of all eigenvalue. Based on this property, this paper proposes a new algorithm, called DE/RMMEDA, which mix differential evolutionary (DE) and the estimation of distribution algorithm (EDA) to generate and adaptively adjusts the number of new solutions by the ratio. The proposed algorithm is experimented on nine tec09 problems. The comparison results between DE/RM-MEDA and the others algorithms, called NSGA-II-DE and RM-MEDA, show that the proposed algorithm perform better in terms of convergence and diversity metric. Copyright © 2019, The Authors. All rights reserved.

关键词： Multiobjective optimization

Meta Structure Search for Link Weight Prediction in Heterogeneous Graphs

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Meta Structure Search for Link Weight Prediction in Heteroge...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoou Zhang Yang Gao Yang Liu Yujia Zhu Peng Zhang Chuan Zhou Qingyun Liu Hongyang Chen Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University Chinese Academy of Sciences Academy of Mathematics and Systems Science Cyberspace Institute of Advanced Technology Guangzhou University Zhejiang Lab Research Center for Graph Computing

Recently link weight prediction has attracted an increasing research interest due to its merits in quantifying the strength between nodes within a graph. Nonetheless, current link weight prediction methods focus solely on graph topology, disregarding node feature information embedded in graphs. In real-world applications, we often collect heterogeneous graph data where multiple types of nodes linked by multiple types of edges are available for analysis, and it is essential and challenging to quantify the proximity of different types of nodes. To solve this challenge, we present a new model for Heterogeneous Graph Link Weight Prediction (HLWP for short). In HLWP, message passing in heterogeneous graph neural networks is described as a meta structure, which can be effectively designed by Differentiable Neural Architecture Search (DARTS) algorithms. Thus, HLWP can enhance the message passing in heterogeneous graphs by DARTS. In addition, HLWP employs a perturbation-based algorithm to enhance stability and precision. Through empirical experiments conducted on three real-world datasets, we demonstrate that HLWP achieves accurate predictions of link weights. Our results highlight the superiority of HLWP over existing methods for link weight prediction and baseline GNN models in terms of accurately predicting link weights within heterogeneous graphs.

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Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

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arXiv 2021年

作者： Guo, Yue Dietrich, Felix Bertalan, Tom Doncevic, Danimir T. Dahmen, Manuel Kevrekidis, Ioannis G. Li, Qianxiao Department of Mathematics National University of Singapore 117543 Singapore Institut für Informatik TU München Boltzmannstr. 3 Garching b. München85748 Germany Department of Chemical and Biomolecular Engineering Whiting School of Engineering Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Forschungszentrum Jülich GmbH Jülich52425 Germany RWTH Aachen University Aachen52062 Germany Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Whiting School of Engineering Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Department of Mathematics National University of Singapore Singapore117543 Singapore Institute of High Performance Computing A*STAR 138632 Singapore

MSC Codes 65L06, 68T07, 65L05We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of general algorithm structure with a data-driven adaptation to specific classes of tasks. This represents a departure from the classical approaches in numerical analysis, which typically do not feature such learning-based adaptations. As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ordinary differential equations (ODEs), based on the Runge-Kutta (RK) integrator architecture. We show that we can learn high-order integrators for targeted families of differential equations without the need for computing integrator coefficients by hand. Moreover, we demonstrate that in certain cases we can obtain superior performance to classical RK methods. This can be attributed to certain properties of the ODE families being identified and exploited by the approach. Overall, this work demonstrates an effective learning-based approach to the design of algorithms for the numerical solution of differential equations. This can be readily extended to other numerical tasks. © 2021, CC BY.

关键词： Ordinary differential equations

Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System

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Cloud-Powered Neural Networks for Sustainable Fishing Practi...

Sustainable Expert Systems (ICSES), International Conference on

作者： M. Xavier Suresh Prathik A V. Arun R. Venugopal T. R. GaneshBabu M. Muthulekshmi Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns; fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges.

关键词： Cloud computing Technological innovation Biological system modeling Decision making Artificial neural networks Predictive models Fisheries Real-time systems Stakeholders Resource management

Fuzzy-Based Hybrid Approach for Security Impact Evaluation in Healthcare Web Applications

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Fuzzy-Based Hybrid Approach for Security Impact Evaluation i...

Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Jitendra Kumar Chaudhary A. Arthi S. Shalini C. Gunasundari Abhishek Sharma Dillip Narayan Sahu School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand Computer Science and Technology Karpagam College of Engineering Coimbatore Department of Mathematics J.N.N institute of Engineering No. 90 Ushaa Garden kannigaipair Chennai Department of Computer Science Engineering Roever Engineering College Perambalur Tamil Nadu National Institute of Advanced Manufacturing Technology (Formerly NIFFT) Department of MCA Gangadhar Meher University Odisha India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In order to connect patients, technology, and healthcare facilities, as well as to efficiently and carefully address the changing needs of medical ecosystems, the smart medical system is evolving into a medical policy service that makes use of wearables, online services, and mobile devices. This article examines some of the challenges that users must overcome in order to adopt intelligent medical technology more quickly and gain access to constant healthcare. The essay examines the integration of a fuzzy-based hybrid strategy to produce intelligently designed health solutions. The patient can access medical services, including monitoring, medication management, and emergency readiness, from anywhere at any time with the help of the intelligent healthcare management system. The adaptive neuro-fuzzy inference system (ANFIS) is a tool that this study suggests using to identify security concerns and evaluate them while developing online applications. First, the security risk concerns associated with developing web applications for healthcare have been identified in this paper. The ANFIS technique has since been used to assess these security issues. Additionally, a fuzzy regression model is suggested by this study.

关键词： Computational modeling Surveillance Ecosystems Medical services Mobile handsets Security Risk management

Reconfigurable intelligent surfaces-enabled vehicular networks: A physical layer security perspective

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arXiv 2020年

作者： Makarfi, Abubakar Rabie, Khaled M. Kaiwartya, Omprakash Adhikari, Kabita Li, Xingwang Quiroz-Castellanos, Marcela Kharel, Rupak Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom Department of Engineering Manchester Metropolitan University ManchesterM15 6BH United Kingdom School of Science and Technology Nottingham Trent University United Kingdom School of Engineering Newcastle University NewcastleNE1 7RU United Kingdom School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China Artificial Intelligence Research Center Universidad Veracruzana Mexico

This paper studies the physical layer security (PLS) of a vehicular network employing reconfigurable intelligent surfaces (RISs). RIS technologies are emerging as an important paradigm for the realisation of next-generation smart radio environments, where large numbers of small, low-cost and passive elements, reflect the incident signal with an adjustable phase shift without requiring a dedicated energy source. Inspired by the promising potential of RIS-based transmission, we investigate the PLS of two vehicular network system models: One with vehicle-to-vehicle communication with the source employing a RIS-based access point, and the other is in the form of a vehicular adhoc network (VANET), with a RIS-based relay deployed on a building;both models assume the presence of an eavesdropper. The performance of the proposed systems are evaluated in terms of the average secrecy capacity (ASC) and the secrecy outage probability (SOP). We present accurate analytical expressions for the two performance metrics and study the impact of various system parameters on the overall performance of the two considered system configurations. Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the ASC and SOP is affected by the location of the RIS-relay as well as the number of RIS cells. Moreover, upto an order magnitude gain could be achieved within certain regions when the number of RIS cells are doubled, clearly indicating the benefit of employing a RIS configuration. Copyright © 2020, The Authors. All rights reserved.

关键词： Intelligent systems

Nonuniform 3D finite difference elastic wave simulation on staggered grids

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arXiv 2020年

作者： Gao, Longfei Ghattas, Omar Keyes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Oden Institute for Computational Engineering and Sciences Jackson School of Geosciences Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Applied Mathematics and Computational Science Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We present an approach to simulate the 3D isotropic elastic wave propagation using nonuniform finite difference discretization on staggered grids. Specifically, we consider simulation domains composed of layers of uniform grids with different grid spacings, separated by nonconforming interfaces. We demonstrate that this layer-wise finite difference discretization has the potential to significantly reduce the simulation cost, compared to its fully uniform counterpart. Stability of such a discretization is achieved by using specially designed difference operators, which are variants of the standard difference operators with adaptations near boundaries or interfaces, and penalty terms, which are appended to the discretized wave system to weakly impose boundary or interface conditions. Combined with specially designed interpolation operators, the discretized wave system is shown to preserve the energy conserving property of the continuous elastic wave equation, and a fortiori ensure the stability of the simulation. Numerical examples are presented to demonstrate the efficacy of the proposed simulation approach. © 2020, CC BY.

关键词： Elastic waves