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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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Fast and robust cross-validation-based scoring rule inference for spatial statistics

arXiv

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arXiv 2024年

作者： Ólafsdóttir, Helga Kristín Rootzén, Holger Bolin, David Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Gothenburg Sweden Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Scoring rules are aimed at evaluation of the quality of predictions, but can also be used for estimation of parameters in statistical models. We propose estimating parameters of multivariate spatial models by maximising the average leave-one-out cross-validation score. This method, LOOS, thus optimises predictions instead of maximising the likelihood. The method allows for fast computations for Gaussian models with sparse precision matrices, such as spatial Markov models. It also makes it possible to tailor the estimator’s robustness to outliers and their sensitivity to spatial variations of uncertainty through the choice of the scoring rule which is used in the maximisation. The effects of the choice of scoring rule which is used in LOOS are studied by simulation in terms of computation time, statistical efficiency, and robustness. Various popular scoring rules and a new scoring rule, the root score, are compared to maximum likelihood estimation. The results confirmed that for spatial Markov models the computation time for LOOS was much smaller than for maximum likelihood estimation. Furthermore, the standard deviations of parameter estimates were smaller for maximum likelihood estimation, although the differences often were small. The simulations also confirmed that the usage of a robust scoring rule results in robust LOOS estimates and that the robustness provides better predictive quality for spatial data with outliers. Finally, the new inference method was applied to ERA5 temperature reanalysis data for the contiguous United States and the average July temperature for the years 1940 to 2023, and this showed that the LOOS estimator provided parameter estimates that were more than a hundred times faster to compute compared to maximum-likelihood estimation, and resulted in a model with better predictive performance. © 2024, CC BY.

关键词： Expectation maximization algorithm

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Sensors-Based Ambient Assistant Living via E-Monitoring technology

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computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： Classification algorithm human action recognition motion sensors machine learning Masi entropy

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Optimization of Performance Metrics of Charge Trapping Synaptic Device for Neuromorphic Applications

Optimization of Performance Metrics of Charge Trapping Synap...

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IEEE Electron Devices technology and Manufacturing Conference (EDTM)

作者： Md. Hasan Raza Ansari Nazek El-Atab SAMA Labs Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

This work validates the synaptic behaviors (long-term potentiation (LTP) and depression (LTD)) of a junctionless transistor (JL) through the simulator. The synaptic transistor is an essential component for implementing artificial neural networks (ANN), which are called hardware neural networks (HNNs). This analysis shows optimization of nonlinearity and dynamic range of conductance values of LTP and LTD and is used for implementing the ANN with the MNIST dataset. The device achieves linear conductance (0.1) value and a higher dynamic range (~10 5 ) by optimizing the gate voltage. These results indicate that the JL device achieves 88.1 % image recognition accuracy.

关键词： Neuromorphic engineering Artificial neural networks Voltage Dynamic range Hardware Behavioral sciences Transistors

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Profit aware Resource Allocation in Fog Computing: A Stackelberg Game Approach

Profit aware Resource Allocation in Fog Computing: A Stackel...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Doad, Akshita Gupta, Nitin Obaidat, Mohammad S. Jadon, Kuldeep Singh Rawat, Piyush Hsiao, Kuie-Fang National Institute of Technology Department of Computer Science and Engineering HP Hamirpur India Distinguished Professor at the King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Amity University UP Noida201301 India University of Texas-Permain Basin OdessaTX United States

ISBN: (纸本)9798350336092

Recently, Internet of Things (IoT) based smart systems have led to more latency-sensitive and bandwidth hungry IoT applications. Fog computing as an extension of Cloud computing fulfills those requirements more effectively. However, a key challenge for the fog computing is multiple resources allocation between heterogeneous Data Service Operators (DSOs) and End Users (EUs). The DSOs want to increase their revenues, however, due to lack of efficient resource allocation mechanisms, they often end up compromising Quality of Service (QoS) and violating Service Level Agreements (SLAs). It is very important for them to make a balance among user's expectations, expenses of providers and penalties for not providing services within the deadline. Hence, a profit-aware resource allocation policy using Stackelberg game is proposed in the work where DSO acts as leaders and EU as followers. The DSOs being the leaders, set the prices first, send it to the followers, and then the followers can compute their best demand for resources accordingly. Next, Stackelberg equilibrium for each type of resource is obtained to find the best resource demand strategy for an EU. The simulation results demonstrate the superiority of the proposed algorithm. © 2023 IEEE.

关键词： Resource allocation

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A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies 17

A-LIOn - Alignment Learning through Inconsistency negatives ...

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17th International Workshop on Ontology Matching, OM 2022

作者： Alghamdi, Sarah M. Zhapa-Camach, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia King Abdul-Aziz University Faculty of Computing and Information Technology Rabigh25732 Saudi Arabia

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Ecological effects of micro/nanoplastics on plant-associated food webs

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Trends in Plant science 2025年第5期30卷 526-538页

作者： Ilyas, Muhammad Duarte, Carlos M. Xu, Elvis Genbo Xu, Guorui Yang, Jie State Key Laboratory of Plant Diversity and Specialty Crops Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Yunnan Menglun 666303 China University of Chinese Academy of Sciences Beijing 100049 China Marine Science Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Biology University of Southern Denmark Odense 5230 Denmark

Micro/nanoplastics (MNPs) contamination is a potential threat to global biodiversity and ecosystem functions, with unclear ecological impacts on aboveground (AG) and belowground (BG) food webs in terrestrial ecosystems. Here, we discuss the uptake, ingestion, bioaccumulation, and ecotoxicological effects of MNPs in plants and associated AG-BG biota at various trophic levels. We propose key pathways for MNPs transfer between the AG-BG food webs and elaborate their impact on terrestrial ecosystem multifunctionality. We conclude that MNPs are bioaccumulated in most studied plants and associated AG-BG biota and can be transferred along AG-BG food webs, which may profoundly impact ecosystem functioning. However, most pathways are still untested. Future research on MNPs should focus on the interactions within AG-BG food webs in terrestrial ecosystems. © 2024 Elsevier Ltd

关键词： aboveground–belowground interactions bioaccumulation ecological multifunctionality ecotoxicity plastic pollution trophic transfer

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Alkyl-thiophene-alkyl linkers to construct double-cable conjugated polymers for single-component organic solar cells

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Chinese Chemical Letters 2024年第1期35卷 542-546页

作者： Wenbin Lai Safakath Karuthedath Chengyi Xiao Lei Meng Frédéric Laquai Weiwei Li Yongfang Li Beijing National Laboratory for Molecular Sciences Key Laboratory of Organic SolidsInstitute of ChemistryChinese Academy of SciencesBeijing 100190China Beijing Advanced Innovation Center for Soft Matter Science and Engineering&State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical TechnologyBeijing 100029China King Abdullah University of Science and Technology(KAUST) KAUST Solar Center(KSC)Physical Sciences and Engineering Division(PSE)Material Science and Engineering Program(MSE)Thuwal 23955-6900Kingdom of Saudi Arabia University of Chinese Academy of Sciences Beijing 100049China Institute of Materials Research Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

In this work,semirigid linkers of the alkyl-thiophene-alkyl structure are developed to construct double-cable *** alkyl units,propyl(C3H6),hexyl(C6H12),and dodecyl(C12H24),are applied as semirigid linkers,yielding three double-cable polymers:PBC6-T,PBC12-T,and PBC24-T,***12-T which uses C6H12-thiophene-C6H12 linkers is found to exhibit the best device efficiency of 5.56%,while PBC6-T and PBC24-T with shorter or longer linkers yield device efficiencies of only 2.65%and 1.09%in single-component organic solar cells(SCOSCs).Further studies reveal that PBC12-T exhibits higher crystallinity and improved charge transport,resulting in better *** work provides an approach to construct double-cable conjugated polymers with long alkyl linkers,and it shows the importance of the linker length for the photovoltaic performance of SCOSCs.

关键词： Double-cable conjugated polymer Single-component organic solar cell Crystallinity Semirigid linkers Alkyl-thiophene-alkyl

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Attributional life cycle assessment of recycling and disposal strategies for construction and demolition waste

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Cleaner Environmental Systems 2025年 18卷

作者： Kanwal, Qudsia Xu, Guochang Gari, Ziad Alhusainan, Ream S. Al-Ghamdi, Sami G. Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia KAUST Climate and Livability Initiative King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Material Cycles Division National Institute for Environmental Studies Onogawa 16-2 Tsukuba305-8506 Japan

The management of Construction and Demolition Waste (CDW) poses significant environmental challenges in Saudi Arabia, exacerbated by rapid urbanization and the Giga Projects under Vision 2030. This study presents a Life Cycle Assessment (LCA) of CDW management practices across seventeen municipalities, providing the first region-specific evaluation of environmental impacts using comprehensive data. By integrating primary and secondary data from local sources, Ecoinvent 3.91, and GaBi Sphera, the study evaluates key CDW disposal methods: sanitary landfilling, incineration, and recycling. The findings reveal that advanced recycling infrastructure (e.g., compactor trucks, recycling facilities) significantly reduces non-renewable energy use, Global Warming Potential (GWP), and downstream emissions, particularly in high CDW-generating regions like Riyadh and Jeddah. Notably, recycling achieves a GWP reduction of up to 75 million kg CO2-eq annually for key materials like mixed soil and concrete. Furthermore, the study contributes to global CDW management literature by presenting a replicable LCA framework that can be applied to other rapidly urbanizing economies facing similar waste management challenges. This framework offers valuable insights for policymakers, urban planners, and researchers seeking to develop sustainable CDW management strategies aligned with circular economy principles and international sustainability goals. © 2025 The Authors

关键词： Project management

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