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An adaptive solution strategy for Richards' equation

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arXiv 2023年

作者： Stokke, Jakob S. Mitra, Koondanibha Storvik, Erlend Both, Jakub W. Radu, Florin A. Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway Computational Mathematics Group Hasselt University Belgium Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway

Flow in variably saturated porous media is typically modelled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative solver for Richards’ equation. The solver relies on an adaptive switching algorithm, based on rigorously derived a posteriori indicators, between two linearization methods: L-scheme and Newton. Although a combined L-scheme/Newton strategy was introduced previously in [1], here, for the first time we propose a reliable and robust criteria for switching between these schemes. The performance of the solver, which can be in principle applied to any spatial discretization and linearization methods, is illustrated through several numerical *** Codes 65M12, 65J08, 65M50, 76Sxx © 2023, CC BY.

关键词： Nonlinear equations

AKM-FCCI: Secure Authentication and Key Management Mechanism for Fog Computing-Based IoT-Driven Critical Infrastructure

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AKM-FCCI: Secure Authentication and Key Management Mechanism...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Vijay Karnatak Neha Tripathi Amit Kumar Mishra Mohammad Wazid Ashok Kumar Das Mohsen Guizani Sachin Shetty Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.

关键词： Threat modeling Wireless communication Performance evaluation Authentication Organizations Critical infrastructure Safety

EM-PAD: An Effective Mechanism for Phishing Attack Detection

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EM-PAD: An Effective Mechanism for Phishing Attack Detection

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Aakash Saksham Mittal Mohammad Wazid Amit Kumar Mishra Ashok Kumar Das Sachin Shetty Mohsen Guizani Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

关键词： Wireless communication Measurement Accuracy Phishing computational modeling Banking Benchmark testing

IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

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arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation 9th

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Exploring Graph-Based Neural Networks for Automatic Brain Tu...

9th International Symposium on From Data Models and Back, DataMod 2020

作者： Saueressig, Camillo Berkley, Adam Kang, Elliot Munbodh, Reshma Singh, Ritambhara Department of Computer Science Brown University Providence United States Center for Computational Molecular Biology Brown University Providence United States Department of Radiation Oncology Brown Alpert Medical School Providence United States

ISBN: (纸本)9783030706494

Manual evaluation of medical images, such as MRI scans of brain tumors, requires years of training, is time-consuming, and is often subject to inter-annotator variation. The automatic segmentation of medical images is a long-standing challenge that seeks to alleviate these issues, with great potential benefits for physicians and patients. In the past few years, variations of Convolutional Neural Networks (CNNs) have established themselves as the state-of-the-art methodology for this task. Recently, Graph-based Neural Networks (GNNs) have gained considerable attention in the deep learning community. GNNs exploit the structural information present in graphical data by aggregating information over connected nodes, allowing them to effectively capture relation information between data elements. In this project, we propose a GNN-based approach to brain tumor segmentation. We represent 3D MRI scans of the brain as a graph, where different regions in the images are represented by nodes and edges connect adjacent regions. We apply several variations of GNNs for the automatic segmentation of brain tumors from MRI scans. Our results show GNNs give reasonable performance on the task and allow for realistic modeling of the data. Furthermore, they are far less computationally expensive and time-consuming to train than state-of-the-art segmentation models. Lastly, we assign Shapley value-based contribution scores to input MRI features to learn what features are relevant for a particular segmentation, generating interesting insights into explaining the predictions of the proposed model. © 2021, Springer Nature Switzerland AG.

关键词： Graph neural networks

Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents

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arXiv 2023年

作者： Giannakakis, Emmanouil Khajehabdollahi, Sina Levina, Anna Department of Computer Science University of Tübingen Tübingen Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany

The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem. We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve. Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents. We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity. Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve. Moreover, we show that co-evolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task. © 2023, CC BY.

关键词： Structural optimization

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

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Journals of Gerontology - Series A Biological sciences and Medical sciences 2023年第4期78卷 718-726页

作者： Liu, Xiaoli DuMontier, Clark Hu, Pan Liu, Chao Yeung, Wesley Mao, Zhi Ho, Vanda Thoral, Patrick J. Kuo, Po-Chih Hu, Jie Li, Deyu Cao, Desen Mark, Roger G. Zhou, FeiHu Zhang, Zhengbo Celi, Leo Anthony Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Center for Artificial Intelligence in Medicine General Hospital of PLA Beijing China New England Geriatric Research Education and Clinical Center VA Boston Healthcare System Boston MA United States Division of Aging Brigham and Women's Hospital Boston MA United States Department of Anesthesiology 920 Hospital of Joint Logistic Support Force of Chinese PLA Kunming Yunnan China Department of Critical Care Medicine First Medical Center General Hospital of PLA Beijing China Department of Medicine National University Hospital Singapore Division of Geriatric Medicine Department of Medicine National University Hospital Singapore Department of Intensive Care Medicine Amsterdam Netherlands Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering General Hospital of PLA Beijing China Elderly Center General Hospital of PLA Beijing China Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively;discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively;and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making. © The Author(s) 2022. Published by Oxford University Press on behalf

关键词： International multicenter Interpretable models Machine learning Mortality Multiple organ dysfunction syndrome

Semantic Units: Organizing knowledge graphs into semantically meaningful units of representation

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arXiv 2023年

作者： Vogt, Lars Kuhn, Tobias Hoehndorf, Robert TIB Leibniz Information Centre for Science and Technology Welfengarten 1B Hanover30167 Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands Computational Bioscience Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Background: Knowledge graphs and ontologies are becoming increasingly important as technical solutions for Findable, Accessible, Interoperable, and Reusable data and metadata (FAIR Guiding Principles). We discuss four challenges that impede the use of FAIR knowledge graphs. Results: Semantic units have the potential to solve the challenges by structuring a knowledge graph into identifiable and semantically meaningful subgraphs. Each semantic unit is represented by its own resource, instantiates a corresponding semantic unit class, and can be implemented as a FAIR Digital Object and a nanopublication in RDF/OWL and property graphs. We distinguish statement and compound units as basic categories of semantic units. Statement units represent smallest, independent propositions that are semantically meaningful for a human reader. They consist of one or more triples and mathematically partition a knowledge graph. We distinguish assertional, contingent (prototypical), and universal statement units as basic types of statement units and propose representational schemes and formal semantics for them (including for absence statements, negations, and cardinality restrictions) that do not involve blank nodes and that translate back to OWL. Compound units, on the other hand, represent semantically meaningful collections of semantic units and we distinguish various types of compound units, representing different levels of representational granularity, different types of granularity trees, and different frames of reference. Conclusions: Semantic units support making statements about statements, can be used for graph-alignment, subgraph-matching, knowledge graph profiling, and for managing access restrictions to sensitive data. Organizing the graph into semantic units supports the separation of ontological, diagnostic (i.e., referential), and discursive information, and it also supports the differentiation of multiple frames of reference. © 2023, CC BY.

关键词： Graphic methods

Pistachio genomes provide insights into nut tree domestication and ZW sex chromosome evolution

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Plant Communications 2023年第3期4卷 80-98页

作者： Salih Kafkas Xiaokai Ma Xingtan Zhang Hayat Topc¸u Rafael Navajas-Pérez Ching Man Wai Haibao Tang Xuming Xu Mortaza Khodaeiaminjan Murat Güney Aibibula Paizila Harun Karcı Xiaodan Zhang Jing Lin Han Lin Roberto de la Herrán Carmelo Ruiz Rejón Jerson Alexander García-Zea Francisca Robles Coral del Val Muñoz Agnes Hotz-Wagenblatt Xiangjia Jack Min HakanÖzkan Elmira Ziya Motalebipour Hatice Gozel Nergiz C¸oban Nesibe Ebru Kafkas Andrej Kilian HuaXing Huang Xuanrui Lv Kunpeng Liu Qilin Hu Ewelina Jacygrad William Palmer Richard Michelmore Ray Ming Department of Horticulture Faculty of AgricultureUniversity of C¸ukurovaAdana 01330Turkey Center for Genomics and Biotechnology Haixia Institute of Science and TechnologySchool of Future TechnologyFujian Agriculture and Forestry UniversityFuzhouChina Key Laboratory of Orchid Conservation and Utilization of National Forestry and Grassland Administration Fujian Agriculture and Forestry UniversityFuzhouChina Departamento de Genética Facultad de CienciasCampus de Fuentenueva s/n18071 GranadaSpain Department of Plant Biology University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamen 361102China Department of Computer Science University of GranadaGranadaSpain Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI Institute) 18014 GranadaSpain German Cancer Research Center Omics IT and Data Management Core FacilityHeidelbergGermany Department of Biological Sciences Youngstown State UniversityYoungstownOH 44555USA Department of Field Crops Faculty of AgricultureUniversity of C¸ukurovaAdana 01330Turkey Pistachio Research Institute SxahinbeyGaziantep 27060Turkey Diversity Arrays Technology University of CanberraCanberraACTAustralia Genome Center University of California Davis451 Health Sciences DriveDavisCA 95616USA

Pistachio is a nut crop domesticated in the Fertile Crescent and a dioecious species with ZW sex *** sequenced the genomes of Pistacia vera cultivar(cv.)Siirt,the female parent,and *** ***,the male *** chromosome-level reference genomes of pistachio were generated,and Z and W chromosomes were *** ZW chromosomes originated from an autosome following the first inversion,which occurred approximately 8.18 *** inversion events in the W chromosome led to the formation of a 12.7-Mb(22.8%of the W chromosome)non-recombining *** W-specific sequences contain several genes of interest that may have played a pivotal role in sex determination and contributed to the initiation and evolution of a ZW sex chromosome system in *** W-specific genes,including defA,defA-like,DYT1,two PTEN1,and two tandem duplications of six VPS13A paralogs,are strong candidates for sex determination or *** history analysis of resequenced genomes suggest that cultivated pistachio underwent severe domestication bottlenecks approximately 7640 years ago,dating the domestication event close to the archeological record of pistachio domestication in *** identified 390,211,and 290 potential selective sweeps in 3 cultivar subgroups that underlie agronomic traits such as nut development and quality,grafting success,flowering time shift,and drought *** findings have improved our understanding of the genomic basis of sex determination/differentiation and horticulturally important traits and will accelerate the improvement of pistachio cultivars and rootstocks.

关键词： Pistacia vera pistachio sequencing reference genome sex chromosome domestication