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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Quantum Error Mitigation for Sampling Algorithms

arXiv

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arXiv 2025年

作者： Liu, Kecheng Cai, Zhenyu Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing100871 China Department of Materials University of Oxford OxfordOX1 3PH United Kingdom

Recent experimental breakthroughs have signalled the imminent arrival of the early fault-tolerant era. However, for a considerable period in the foreseeable future, relying solely on quantum error correction for full error suppression will remain extremely challenging due to its substantial hardware overhead. Additional help from quantum error mitigation (QEM) is essential for bridging this gap towards achieving quantum advantage. The application of QEM has so far been restricted to expectation value estimation, leaving its extension to sampling-based algorithms – which is expected to play a pivotal role in the early fault-tolerant era – an unresolved challenge. In this work, we present a framework for applying any QEM techniques to obtain the error-mitigated output distribution, showing that this incurs no greater cost than estimating a single observable. We also devised a way to sample from this distribution and constructed an explicit scheme for applying any QEM methods to quantum phase estimation, which can be generalised to other sampling algorithms. Numerical experiments were conducted to validate the efficacy of these methods. We believe our methods significantly broaden the scope of QEM, extending its applicability to most algorithms of practical interest and forming a crucial step towards realising quantum advantage. Copyright © 2025, The Authors. All rights reserved.

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Workload Failure Prediction for Data centers

arXiv

引用

arXiv 2023年

作者： Li, Jie Wang, Rui Ali, Ghazanfar Dang, Tommy Sill, Alan Chen, Yong Department of Computer Science Texas Tech University Lubbock United States High-Performance Computing Center Texas Tech University Lubbock United States

Failed workloads that consumed significant computational resources in time and space affect the efficiency of data centers significantly and thus limit the amount of scientific work that can be achieved. While the computational power has increased significantly over the years, detection and prediction of workload failures have lagged far behind and will become increasingly critical as the system scale and complexity further increase. In this study, we analyze workload traces collected from a production cluster and train machine learning models on a large amount of data sets to predict workload failures. Our prediction models consist of a queue-time model that estimates the probability of workload failures before execution and a runtime model that predicts failures at runtime. Evaluation results show that the queue-time model and runtime model can predict workload failures with a maximum precision score of 90.61% and 97.75%, respectively. By integrating the runtime model with the job scheduler, it helps reduce CPU time, memory usage by up to 16.7% and 14.53%, respectively. © 2023, CC BY-NC-SA.

关键词： Big data

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Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

arXiv

引用

arXiv 2022年

作者： Cheng, Ri Sun, Yuqi Yan, Bo Tan, Weimin Ma, Chenxi School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multiview images captured from the same scene. One solution is to apply image or video super-resolution (SR) methods to reconstruct HR results from the low-resolution (LR) input view. However, these methods cannot handle large-angle transformations between views and leverage information in all multi-view images. To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view. Specifically, the proposed Geometry-Aware Reference Synthesis module in MVSRnet uses geometry information and all multi-view LR images to synthesize pixel-aligned HR reference images. Then, the proposed Dynamic High-Frequency Search network fully exploits the high-frequency textural details in reference images for SR. Extensive experiments on several benchmarks show that our method significantly improves over the state-of-the-art approaches. © 2022, CC BY.

关键词： Geometry

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Approaching Mega-Scale Quantum Detector Tomography using High-Performance computing

Approaching Mega-Scale Quantum Detector Tomography using Hig...

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Schapeler, Timon Lass, Michael Plessl, Christian Bartley, Tim J. Institute for Photonic Quantum Systems Paderborn University Warburger Str. 100 Paderborn33098 Germany Paderborn Center for Parallel Computing Paderborn University Warburger Str. 100 Paderborn33098 Germany Department of Computer Science Paderborn University Warburger Str. 100 Paderborn33098 Germany

Quantum characterization tools must keep up with the ever-increasing size of detector arrays for quantum applications. We show quantum detector tomography performed on a supercomputer which reconstructs the POVMs up t... 详细信息

ISBN: (纸本)9781957171272

关键词： Beam splitters Detector arrays Photonic quantum computing Quantum detectors Superconducting nanowire single photon detectors Variable optical attenuators

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Enhancing the Performance of AlphaFold Through Modified Storage Method and Optimization of HHblits on TSUBAME3.0 Supercomputer

Enhancing the Performance of AlphaFold Through Modified Stor...

引用

computer science, computer Engineering, & Applied computing (CSCE), Congress in

作者： Hayato Fujita Akihiro Nomura Toshio Endo Masakazu Sekijima Department of Computer Science Tokyo Institute of Technology Yokohama Japan Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Yokohama Japan

Knowledge of the three-dimensional structures of proteins is vital for understanding their functions. This knowledge can also serve as a basis for understanding the various functions of the human body. The three-dimensional structures of proteins have been experimentally ascertained from the known amino acid sequence of proteins. To reduce the time and cost of experiments, computer-based methods such as AlphaFold have been proposed. AlphaFold uses existing tools such as HHblits to obtain multiple sequence alignments (MSAs) from huge G protein (sequence) databases, such as BFD. However, HHblits requires a long execution time, as it has to perform many I/O operations. The execution time of HHblits differs significantly depending on the type of storage used to store the protein (sequence) database. Notably, the execution time of HHblits when using TSUBAME3.0 Lustre storage area with default settings differs significantly from that when storage with stripe settings is used. Therefore, we modified the storage method of the protein (sequence) database that can be selected on TSUBAME3.0 and measured the execution time of HHblits based on the storage method selected. Further-more, we profiled the various bottlenecks of HHblits, and based on the results, tuned the number of parallel processes, modified the database arrangement, and optimized sorting. Furthermore, we made the tool execution asynchronous due to the data dependency between the MSA acquisition tools of AlphaFold. Consequently, we succeeded in shortening the execution time by half, on average, when predicting a three-dimensional structure from a single amino acid sequence on TSUBAME3.0. The modifications proposed in this study to accelerate the process have already been pull requested at the following URLs: HHblits (https://***/soedinglab/hh-suite/pull/307) and AlphaFold (https://***/deepmind/alphafold/pull/399).

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Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

arXiv

引用

arXiv 2022年

作者： Jiang, Xuhao Tan, Weimin Cheng, Ri Zhou, Shili Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods. © 2022, CC BY.

关键词： Textures

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction

arXiv

引用

arXiv 2023年

作者： Sun, Kai Zhang, Richong Mensah, Samuel Aletras, Nikolaos Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023, CC BY.

关键词： Sentiment analysis

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Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

arXiv

引用

arXiv 2023年

作者： Alaql, Abeer Abdullah Alqurashi, Fahad Mehmood, Rashid Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah21589 Saudi Arabia

The current aggressive capitalist approaches have affected social, economic, and planet sustainability. Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of financial crises (the 1970s, 2008, FTX, etc.) reveals significant weaknesses and vulnerabilities in the foundations of modern economies. Another significant issue currently is people quitting their jobs in large numbers (Great Attrition). Moreover, many organizations have a diverse workforce comprising multiple generations (e.g., Generations X, Y, Z, alpha) posing new challenges. Transformative approaches in economics and labour markets are needed to protect our societies, economies, and planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multigenerational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorised them into 5 macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues, and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualisation methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of AI-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour

关键词： Data Analytics

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

引用

14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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