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arXiv 2021年

作者： Ye, Guanhua Yin, Hongzhi Chen, Tong Chen, Hongxu Cui, Lizhen Zhang, Xiangliang School of Information Technology & Electric Engineering The University of Queensland Australia School of Computer Science University of Technology Sydney Australia School of Software Shandong University China Division of Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Saudi Arabia

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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EXPLICIT RUNGE–KUTTA METHODS THAT ALLEVIATE ORDER REDUCTION

arXiv

引用

arXiv 2023年

作者： Biswas, Abhijit Ketcheson, David I. Roberts, Steven Seibold, Benjamin Shirokoff, David Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Mathematics Temple University PhiladelphiaPA19122 United States Department of Mathematical Sciences New Jersey Institute of Technology NewarkNJ07102 United States

Explicit Runge–Kutta (RK) methods are susceptible to a reduction in the observed order of convergence when applied to initial-boundary value problem with time-dependent boundary conditions. We study conditions on explicit RK methods that guarantee high-order convergence for linear problems;we refer to these conditions as weak stage order conditions. We prove a general relationship between the method’s order, weak stage order, and number of stages. We derive explicit RK methods with high weak stage order and demonstrate, through numerical tests, that they avoid the order reduction phenomenon up to any order for linear problems and up to order three for nonlinear *** Codes 65L04, 65L20, 65M12 Copyright © 2023, The Authors. All rights reserved.

关键词： Runge Kutta methods

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Mapping the Diffusion Tensor in Microstructured Perovskites

arXiv

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arXiv 2022年

作者： Brenes, Roberto deQuilettes, Dane W. Swartwout, Richard Alsalloum, abdullah Y. Bakr, Osman M. Bulović, Vladimir Research Laboratory of Electronics Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Understanding energy transport in semiconductors is critical for design of electronic and optoelectronic devices. Semiconductor material properties such as charge carrier mobility or diffusion length are commonly measured in bulk crystals and determined using models that describe transport behavior in homogeneous media, where structural boundary effects are minimal. However, most emerging semiconductors exhibit nano and microscale heterogeneity. Therefore, experimental techniques with high spatial resolution paired with models that capture anisotropy and domain boundary behavior are needed. We develop a diffusion tensor-based framework to analyze experimental photoluminescence (PL) diffusion maps accounting for material nano and microstructure. Specifically, we quantify both carrier transport and recombination in single crystal and polycrystalline lead halide perovskites by globally fitting diffusion maps, with spatial, temporal, and PL intensity data. We reveal a 29% difference in principal diffusion coefficients and alignment between electronically coupled grains for CH3NH3PbI3 polycrystalline films. This framework allows for understanding and optimizing anisotropic energy transport in heterogeneous materials. © 2022, CC BY.

关键词： Single crystals

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Regulating surface potential maximizes voltage in all-perovskite tandems (vol 613, pg 676, 2022)

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NATURE 2023年第7973期620卷 E15-E15页

作者： Chen, Hao Maxwell, Aidan Li, Chongwen Teale, Sam Chen, Bin Zhu, Tong Ugur, Esma Harrison, George Grater, Luke Wang, Junke Wang, Zaiwei Zeng, Lewei Park, So Min Chen, Lei Serles, Peter Awni, Rasha Abbas Subedi, Biwas Zheng, Xiaopeng Xiao, Chuanxiao Podraza, Nikolas J. Filleter, Tobin Liu, Cheng Yang, Yi Luther, Joseph M. De Wolf, Stefaan Kanatzidis, Mercouri G. Yan, Yanfa Sargent, Edward H. The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Physics and Astronomy and Wright Center for Photovoltaics Innovation and Commercialization The University of Toledo Toledo OH USA Department of Chemistry Northwestern University Evanston IL USA Department of Electrical and Computer Engineering Northwestern University Evanston IL USA KAUST Solar Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Department of Mechanical and Industrial Engineering University of Toronto Toronto Ontario Canada National Renewable Energy Laboratory Golden CO USA

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A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

arXiv

引用

arXiv 2023年

作者： Alshahrani, Norah M. Kiah, M.L. Mat Zaidan, B.B. Alamoodi, A.H. Saif, Abdu Department of Computer System & Technology Faculty of Computer Science & IT University of Malaya Lembah Pantai Kuala Lumpur50603 Malaysia Department of Computer Science Applied Collage King Khalid University P.O. Box 62569 Abha Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges, and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development. © 2023, CC BY.

关键词： Smart contract

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DTiGEMS+: Drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques

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Journal of Cheminformatics 2020年第1期12卷 1-17页

作者： Thafar, Maha A. Olayan, Rawan S. Ashoor, Haitham Albaradei, Somayah Bajic, Vladimir B. Gao, Xin Gojobori, Takashi Essack, Magbubah Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Collage of Computers and Information Technology Taif University Taif Saudi Arabia Jackson Laboratory for Genomic Medicine Farmington CT United States Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Biological and Environmental Sciences and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison. © 2020 The Author(s).

关键词： Bioinformatics Cheminformatics Drug repositioning Drug-target interaction Graph embedding Heterogenous network Machine learning Similarity integration Similarity-based

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Manifold optimization for high accuracy spatial location estimation using ultrasound waves

arXiv

引用

arXiv 2021年

作者： AlSharif, Mohammed H. Douik, Ahmed Ahmed, Mohanad Al-Naffouri, Tareq Y. Hassibi, Babak The Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia The Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States

This paper reports the design of a high-accuracy spatial location estimation method using ultrasound waves by exploiting the fixed geometry of the transmitters. Assuming an isosceles triangle antenna configuration, where three antennas are placed as the vertices of an isosceles triangle, the spatial location problem can be formulated as a non-convex optimization problem whose interior is shown to admit a Riemannian manifold structure. Our investigation of the geometry of the newly introduced manifold (i.e., the manifold of all isosceles triangles in R3) enables the design of highly efficient optimization algorithms. Simulations are presented to compare the performance of the proposed approach with popular methods from the literature. The results suggest that the proposed Riemannian-based methods outperform the state-of-the-art methods. Furthermore, the proposed Riemannian methods require much less computation time compared to popular generic non-convex approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Transmitters

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Gaps and Data Ambiguities in DNA Reference Libraries: A Limiting Factor for Molecular-Based Biodiversity Assessments Using Annelids as a Case Study

引用

Ecology and Evolution 2025年第6期15卷

作者： Marcos A. L. Teixeira Eva Aylagas John K. Pearman Susana Carvalho Biological and Environmental Sciences and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Cawthron Institute Nelson New Zealand Marine Science Program King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Regional DNA reference libraries are essential to improve the accuracy of molecular-based biodiversity assessments, species identification and conservation. However, these libraries are often incomplete, limiting the full potential of molecular tools. In this study, we evaluated the completeness of DNA barcode reference data for annelids from the Red Sea, Arabian Gulf and Gulf of Oman and examined its implications for biodiversity assessments. A database of 2291 worldwide annelid species and 3131–4047 Molecular Operational Taxonomic Units (MOTUs) was compiled from the Barcode of Life Data System (BOLD). Two regional checklists -OBIS (498 species) and Wehe & Fiege (892 species)—were cross-referenced against this database to identify coverage gaps and taxonomic inconsistencies. Only 24% and 23% of species in each checklist, respectively, had corresponding barcodes, and just three species were sampled from the region. Additionally, 43% of BOLD's Barcode Index Numbers (BINs) revealed taxonomic ambiguities. To further assess local annelid biodiversity, we analysed a metabarcoding dataset from 135 Autonomous Reef Monitoring Structures (ARMS) deployed on shallow reefs in the region. This yielded 5375 Amplicon Sequence Variants (ASVs), with 55% classified only to class or phylum level. Of the 1350 MOTUs identified, either none to just 14 species-level identifications were found depending on the taxonomic classification method and database, 10 of which appear to be cryptic species complexes. Based on proposed MOTU/species ratios, we estimate approximately 992 annelid species in the ARMS dataset, highlighting underexplored regional diversity. Manual inspection of clustered ASVs also revealed potential pseudogene artifacts, taxonomic misidentifications up to the class level and underestimation of species matches due to discordant MOTUs. These findings underscore the urgent need to expand regional reference libraries, apply integrative taxonomy and implement refined, user-defin

关键词： Annelida Arabian gulf ARMS coral reefs eDNA Gulf of Oman metabarcoding MtCOI-5P Red Sea

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Avionics of Aerial Robots

引用

Current Robotics Reports 2021年第2期2卷 113-124页

作者： Khamvilai, Thanakorn Dunham, Joel Feron, Eric Johnson, Eric N. Department of Aerospace Engineering Georgia Institute of Technology Atlanta USA Independent Researcher Nashua USA Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Aerospace Engineering Pennsylvania State University State College USA

This work provides an overview of avionic systems, which can be used as an entry point to learn about their architecture and components, and as a guide for studying the recent development of unmanned aerial systems avionics. The development trend of avionics for unmanned aerial systems tends toward the one used for a large aircraft both in the structure of the architecture and in design and implementation processes. However, due to the differences in the operational environment, challenges remain in specifying the proper requirements, and proving their safety and security. The paper reviews the functionalities and importance of avionic systems, especially in unmanned aerial systems. It also provides the historical background and the future trend of system development in terms of safety, security, and certification for the purpose of integrating these unmanned aerial systems into an urban airspace.

关键词：

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Vision-Language Models in Remote Sensing: Current Progress and Future Trends

arXiv

引用

arXiv 2023年

作者： Li, Xiang Wen, Congcong Hu, Yuan Yuan, Zhenghang Zhu, Xiao Xiang The King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates The Institute of Remote Sensing and Geographic Information Systems Peking University Beijing100871 China Data Science in Earth Observation Technical University of Munich Munich80333 Germany The Munich Center for Machine Learning Munich80333 Germany

The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions close to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond visual recognition of RS images, model semantic relationships, and generate natural language descriptions of the image. This makes them better suited for tasks requiring visual and textual understanding, such as image captioning, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting challenges, and identifying potential research opportunities. Specifically, we review the application of vision-language models in mainstream remote sensing tasks, including image captioning, text-based image generation, text-based image retrieval, visual question answering, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. We also summarize the commonly used remote sensing vision-language datasets, codebases, and online accessible resources. Finally, we delineate the limitations of current studies and suggest potential avenues for future advancements. This review aims to provide a comprehensive review of the current research pro

关键词： Semantics

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