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Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics

Communications on Applied Mathematics and Computation 2022年第4期4卷 1191-1228页

作者： Hendrik Ranocha Lisandro Dalcin Matteo Parsani David I.Ketcheson Applied Mathematics Münster University of MünsterMunsterGermany Computer Electrical and Mathematical Science and Engineering Division(CEMSE) Extreme Computing Research Center(ECRC)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Computer Electrical and Mathematical Science and Engineering Division(CEMSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We develop error-control based time integration algorithms for compressible fluid dynam-ics(CFD)applications and show that they are efficient and robust in both the accuracy-limited and stability-limited *** on discontinuous spectral element semidis-cretizations,we design new controllers for existing methods and for some new embedded Runge-Kutta *** demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in *** compare a wide range of error-control-based methods,along with the common approach in which step size con-trol is based on the Courant-Friedrichs-Lewy(CFL)*** optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances,while additionally providing control of the temporal error at tighter *** numerical examples include challenging industrial CFD applications.

关键词： Explicit Runge-Kutta methods Step size control Compressible Euler equations Compressible Navier-Stokes equations hp-adaptive spatial discretizations

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Throughput-Maximized Network Deployment in a Multi-NIB-Aided ISAC Network

Throughput-Maximized Network Deployment in a Multi-NIB-Aided...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Park, Ki-Hong Alouini, Mohamed-Slim Chen, Yunfei King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350370218

In this paper, we consider the deployment problem of integrated sensing and communication (ISAC) network in sparsely populated area where the distributions of broadband users and Internet-of-things (IoT) devices are disjoint and therefore traditional network infrastructure can be under-utilized. Network-in-a-box which is lightweight, portable, and flexible to support different type of networks is the emerging technology for either one-off network or under-served network environments. We formulate the total network throughput maximization problem to optimally deploy multiple NIBs enabling an ISAC network supporting both conventional broadband data and sensed IoT data modeled by short packet communication (SPC) framework. Under the delay and minimum throughput requirements of sensing data for IoT devices, we will propose the suboptimal iterative solution to find location of NIBs, association between NIB and user, and coding rate in SPC framework and verify the feasibility by simulation. © 2023 IEEE.

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A Balloon-Based UAV-Aided Non-Terrestrial Sectorized Network for Post Disaster Cellular Coverage: A Dynamic Environment Perspective 7

A Balloon-Based UAV-Aided Non-Terrestrial Sectorized Network...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Dabiri, Mohammad Taghi Hasna, Mazen Althunibat, Saud Qaraqe, Khalid Alouini, Mohamed-Slim Qatar University Department of Electrical Engineering Doha Qatar Al-Hussein Bin Talal University Department of Communications Engineering Jordan Hamad Bin Khalifa University Electrical Engineering Program College of Science and Engineering Qatar King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9798350367027

With the advancements in low-earth orbit (LEO) constellations, modern aerial platforms (APs), sector antennas, and device technologies, it is now becoming feasible to provide aerial platform-based cellular network (APCN) as an efficient and easy-To-deploy solution for post-disaster internet recovery. Similar to the terrestrial networks (TNs), sector access antennas have a huge potential for their usage in APs, such as tethered balloons, to provide a wide coverage and high-speed internet connectivity in disaster areas. However, in this paper, we show that many significant challenges remain in their commercial deployments which have received less attention so far. For instance, as the main cause of other challenges, by providing realistic 3D network simulations, we show that sectorized antenna-based APCNs suffer from inherent motions and vibrations of floating AP topologies, which causes moving and dynamic holes in the coverage and thus, confirms that the design of these APCNs is much more complicated than fixed TNs. To this end, by providing an easy-To-read brief review of existing aerospace post-disaster solutions, we show the superiority of APCNs which can bring several key benefits over the existing topologies, and then technically discuss its various challenges and open problems that need to be addressed before they can be deployed at a large scale. © 2024 IEEE.

关键词： Tropics

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KenCoh: A Ranked-Based Canonical Coherence 1

arXiv

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arXiv 2024年

作者： Talento, Mara Sherlin D.P. Roy, Sarbojit Ombao, Hernando Statistics Program Computer Electrical and Mathematical Science & Engineering King Abdullah University of Science and Technology Saudi Arabia

In this paper, we consider the problem of characterizing a robust global dependence between two brain regions where each region may contain several voxels or channels. This work is driven by experiments to investigate the dependence between two cortical regions and to identify differences in brain networks between brain states, e.g., alert and drowsy states. The most common approach to explore dependence between two groups of variables (or signals) is via canonical correlation analysis (CCA). However, it is limited to only capturing linear associations and is sensitive to outlier observations. These limitations are crucial because brain network connectivity is likely to be more complex than linear and that brain signals may exhibit heavy-tailed properties. To overcome these limitations, we develop a robust method, Kendall canonical coherence (KenCoh), for learning monotonic connectivity structure among neuronal signals filtered at given frequency bands. Furthermore, we propose the KenCoh-based permutation test to investigate the differences in brain network connectivity between two different states. Our simulation study demonstrates that KenCoh is competitive to the traditional variance-covariance estimator and outperforms the later when the underlying distributions are heavy-tailed. We apply our method to EEG recordings from a virtual-reality driving experiment. Our proposed method led to further insights on the differences of frontal-parietal cross-dependence network when the subject is alert and when the subject is drowsy and that left-parietal channel drives this dependence at the beta-band. © 2024, CC BY.

关键词： Statistics

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources 41

Contrastive Learning for Clinical Outcome Prediction with Pa...

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41st International Conference on Machine Learning, ICML 2024

作者： Xia, Meng Wilson, Jonathan Goldstein, Benjamin Henao, Ricardo Department of Electrical and Computer Engineering Duke University Durham United States Department of Biostatistics and Bioinformatics Duke University Durham United States King Abdullah University of Science and Technology Thuwal Saudi Arabia

The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and the growing availability of large EHR datasets. Existing models generally assume access to the same data sources during both training and inference stages. However, this assumption is often challenged by the fact that real-world clinical datasets originate from various data sources (with distinct sets of covariates), which though can be available for training (in a research or retrospective setting), are more realistically only partially available (a subset of such sets) for inference when deployed. So motivated, we introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS), that trains encoders to capture information across different data sources and then leverages them to build classifiers restricting access to a single data source. This approach can be used with existing cross-sectional or longitudinal outcome classification models. We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Low-rank variational Bayes correction to the Laplace method

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3032-3056页

作者： Janet Van Niekerk Håvard Rue Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

Approximate inference methods like the Laplace method, Laplace approximations and variational methods, amongst others, are popular methods when exact inference is not feasible due to the complexity of the model or the abundance of data. In this paper we propose a hybrid approximate method called Low-Rank Variational Bayes correction (VBC), that uses the Laplace method and subsequently a Variational Bayes correction in a lower dimension, to the joint posterior mean. The cost is essentially that of the Laplace method which ensures scalability of the method, in both model complexity and data size. Models with fixed and unknown hyperparameters are considered, for simulated and real examples, for small and large data sets.

关键词： information processing rule INLA Laplace LGM variational Bayes

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Deterministic and Nondeterministic Decision Trees for Recognition of Properties of Decision Rule Systems 11th

Deterministic and Nondeterministic Decision Trees for Recogn...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Durdymyradov, Kerven Moshkov, Mikhail Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Jeddah 23955-6900 Saudi Arabia

ISBN: (纸本)9783031927430

This paper considers various problems of recognizing the properties of decision rule systems. Deterministic and nondeterministic decision trees are used as algorithms for solving them. It is proved that the minimum depth of a deterministic decision tree solving the problem is bounded from above by the square of the minimum depth of a nondeterministic decision tree. Note that a nondeterministic decision tree can be considered as a representation of a system of decision rules that are true for the problem under consideration and cover all possible inputs. The results obtained may be of interest to the rough set theory, in which both decision rule systems and decision trees are intensively studied. In particular, they make one think about the possibilities of transforming decision rule systems into decision trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision rule system Deterministic decision tree Nondeterministic decision tree

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Optically Controlled Charge Trapping Memory Based on Spin Coated Hafnium Diselenide Flakes

Optically Controlled Charge Trapping Memory Based on Spin Co...

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IEEE Conference on Nanotechnology

作者： Bashayr Alqahtani Nazek El Atab King Abdullah University of Science and Technology Program of Electrical and Computer Engineering Computer Electrical Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

This paper demonstrates the seamless fabrication of optoelectronic memory by integrating HfSe 2 as a charge-trapping layer in a MOS memory structure. Through a spin coating technique, solution-processable HfSe 2 flakes with average thicknesses of 2 nm were deposited between the tunneling and blocking oxide layers. The charge-trapping material distribution and thickness were explored by Atomic Force Microscopy and X-ray Diffraction Spectroscopy. The electrical characterization of MOS memory revealed a memory window of 5.5 Volts under ±16 Volts biasing. Furthermore, the memory endurance exceeds 10 4 electrical programming and erasing cycles. The retention test performed at room temperature showed that the memory device is expected to lose only 10% of the stored charges after 10 years. Under light stimuli (405nm wavelength and output power ~ 20 mW) with electrical readout voltage, the MOS memory showed an increase in the memory window from 5.5 Volts to 6.5 Volts.

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A Super-Resolution Electromagnetic Inversion Scheme for Brain Stroke Detection

A Super-Resolution Electromagnetic Inversion Scheme for Brai...

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European Conference on Antennas and Propagation, EuCAP

作者： Lingqi Gao Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

A super-resolution electromagnetic inversion scheme is proposed for brain stroke detection. The scheme operates in two stages: (i) A quantitative Gauss-Newton method with Tikhonov regularization and frequency-hopping is employed to get a low-resolution inversion result. (ii) A U-Net-based super-resolution method is used to significantly enhance the spatial resolution of the inversion result from (i), The proposed scheme is demonstrated through several numerical examples, highlighting its effectiveness in accurately reconstructing the brain's permittivity and conductivity profiles, thereby contributing to advancements in stroke diagnosis.

关键词： Neuroimaging Location awareness Superresolution Europe Conductivity Classification algorithms Electromagnetics Permittivity Spatial resolution Image reconstruction

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