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arXiv 2021年

作者： Occorsio, Donatella Russo, Maria Grazia Themistoclakis, Woula Department of Mathematics Computer Science and Economics University of Basilicata Via dell'Ateneo Lucano 10 Potenza85100 Italy C.N.R. National Research Council of Italy IAC Institute for Applied Computing "Mauro Picone" Via P. Castellino 111 Napoli80131 Italy

A product quadrature rule, based on the filtered de la Vallée Poussin polynomial approximation, is proposed for evaluating the finite Hilbert transform in [−1, 1]. Convergence results are stated in weighted uniform norm for functions belonging to suitable Besov type subspaces. Several numerical tests are provided, also comparing the rule with other formulas known in literature. © 2021, CC BY.

关键词： Banach spaces

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Some remarks on filtered polynomial interpolation at Chebyshev nodes

arXiv

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arXiv 2021年

作者： Occorsio, Donatella Themistoclakis, Woula Department of Mathematics Computer Science and Economics University of Basilicata Via dell’Ateneo Lucano 10 Potenza85100 Italy C.N.R. National Research Council of Italy IAC Institute for Applied Computing "Mauro Picone" Via P. Castellino 111 Napoli80131 Italy

The present paper concerns filtered de la Vallée Poussin (VP) interpolation at the Chebyshev nodes of the four kinds. This approximation model is interesting for applications because it combines the advantages of the classical Lagrange polynomial approximation (interpolation and polynomial preserving) with the ones of filtered approximation (uniform boundedness of the Lebesgue constants and reduction of the Gibbs phenomenon). Here we focus on some additional features that are useful in the applications of filtered VP interpolation. In particular, we analyze the simultaneous approximation provided by the derivatives of the VP interpolation polynomials. Moreover, we state the uniform boundedness of VP approximation operators in some Sobolev and Hölder–Zygmund spaces where several integro–differential models are uniquely and stably solvable. © 2021, CC BY-NC-ND.

关键词： Interpolation

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parGeMSLR: A Parallel Multilevel Schur Complement Low-Rank Preconditioning and Solution Package for General Sparse Matrices

arXiv

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arXiv 2022年

作者： Xu, Tianshi Kalantzis, Vassilis Li, Ruipeng Xi, Yuanzhe Dillon, Geoffrey Saad, Yousef Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Thomas J. Watson Research Center IBM Research Yorktown HeightsNY10598 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory P.O. Box 808 L-561 LivermoreCA94551 United States Department of Mathematics Emory University AtlantaGA30322 United States Department of Mathematics University of South Carolina ColumbiaSC29208 United States

This paper discusses parGeMSLR, a C++/MPI software library for the solution of sparse systems of linear algebraic equations via preconditioned Krylov subspace methods in distributed-memory computing environments. The preconditioner implemented in parGeMSLR is based on algebraic domain decomposition and partitions the symmetrized adjacency graph recursively into several non-overlapping partitions via a p-way vertex separator, where p is an integer multiple of the total number of MPI processes. From a numerical perspective, parGeMSLR builds a Schur complement approximate inverse preconditioner as the sum between the matrix inverse of the interface coupling matrix and a low-rank correction term. To reduce the cost associated with the computation of the approximate inverse matrices, parGeMSLR exploits a multilevel partitioning of the algebraic domain. The parGeMSLR library is implemented on top of the Message Passing Interface and can solve both real and complex linear systems. Furthermore, parGeMSLR can take advantage of hybrid computing environments with in-node access to one or more Graphics Processing Units. Finally, the parallel efficiency (weak and strong scaling) of parGeMSLR is demonstrated on a few model problems arising from discretizations of 3D Partial Differential Equations. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation

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Scientific reports 2025年第1期15卷 18423页

作者： Lalitha Krishnasamy Siva C Rajesh Kumar Dhanaraj Mahmoud Ahmad Al-Khasawneh Taher Al-Shehari Nasser A Alsadhan Shitharth Selvarajan Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India. Department of Information Technology Nandha Engineering College Erode Tamil Nadu India. Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India. School of Computing Skyline University College University City Sharjah 1797 Sharjah UAE. Applied Science Research Center Applied Science Private University Amman Jordan. Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia. Computer Science Department College of Computer and Information Sciences King Saud University 12372 Riyadh Saudi Arabia. Department of Computer Science Kebri Dehar University 250 Kebri Dehar Ethiopia. ShitharthS@kdu.edu.et. Department of Computer Science and Engineering Chennai Institute of Technology Chennai India. ShitharthS@kdu.edu.et. Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India. ShitharthS@kdu.edu.et. School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds UK. ShitharthS@kdu.edu.et.

Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion management enables drivers to bypass heavily trafficked areas and reducing pollutant emissions. However, properly forecasting congestion spread remains challenging due to complex, dynamic, and non-linear nature of traffic patterns. The advent of Internet of Things (IoT) devices has introduced valuable datasets that can support the development of intelligent and sustainable transportation for modern cities. This work presents a Deep Learning (DL) approach of Reinforcement Learning (RL) based Bidirectional Long Short-Term Memory (BiLSTM) with Adaptive Secretary Bird Optimizer (ASBO) for traffic congestion prediction. The experimentation is evaluated on Traffic Prediction Dataset and achieved better Mean Square Error (MSE) and Mean Absolute Error (MAE) with results of 0.015 and 0.133 respectively. Compared to the existing algorithms like RL, Deep Q Learning (DQL), LSTM and BiLSTM, the RL - BiLSTM with ASBO outperformed with the parameters MSE, RMSE, R2, MAE and MAPE with 37%, 27.44%, 26%, 33.52% and 35.8% respectively. The better performance demonstrates that RL- BiLSTM with ASBO is well-suited to predict congestion patterns in road networks.

关键词： Adaptive secretary bird optimization Reinforcement learning Smart cities Traffic congestion

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Data Driven Surrogate Signal Extraction for Dynamic PET Using Selective PCA

Data Driven Surrogate Signal Extraction for Dynamic PET Usin...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Alexander C. Whitehead Kuan-Hao Su Elise C. Emond Ander Biguri Maria Machado Joanna C. Porter Helen Garthwaite Scott D. Wollenweber Jamie R. McClelland Kris Thielemans Institute of Nuclear Medicine University College London London UK Centre for Medical Image Computing University College London London UK Department of Computer Science University College London London UK Molecular Imaging and Computed Tomography Engineering GE Healthcare Waukesha USA Institute of Nuclear Medicine University College London UK Snke OS Munich Germany Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK Centre for Respiratory Medicine University College London London UK

Respiratory motion correction is beneficial in PET. Methods of motion correction include gated reconstruction, where the acquisition is binned, based on a respiratory trace. To acquire these respiratory traces, an external device, like the Real Time Position Management System, or a data driven method, such as PCA, can be used. Data driven methods have the advantage that they are non-invasive, and can be performed post-acquisition. However, data driven methods have the disadvantage that they are adversely affected by the tracer kinetics of a dynamic acquisition. This work seeks to evaluate several adaptions of the PCA method, through which it can be used with dynamic data. The methods explored in this work include, using a moving window (similar to the KRG method of Schleyer et al. (PMB 2014)), extrapolation of the principal component from later time points to earlier time points, as well as a method to select and combine multiple respiratory components. The respiratory traces acquired, were evaluated on 21 patients, by calculating their correlation with a Real Time Position Management System surrogate signal. The results indicate that all methods produce better surrogate signals than when applying static PCA to dynamic data. Extrapolating a late principal component, produced more promising results than using a moving window, and selecting and combining components held benefits for all methods.

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SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials

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Chemometrics and Intelligent Laboratory Systems 2025年 263卷

作者： Muhamad Akrom Harun Al Azies Wise Herowati Totok Sutojo Supriadi Rustad Hermawan Kresno Dipojono Hideaki Kasai Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia Department of Applied Physics Osaka University Suita Osaka 565-0871 Japan

This investigation delves into the viability of the simplified molecular input line entry system (SMILES)-based machine learning (ML) approach as the sole input feature for predicting the corrosion inhibition efficiency (CIE) of pyridine-quinoline compounds to replace various quantum chemical properties (QCP). Employing the molecular access system (MACCS) fingerprint techniques simplifies the processing of molecular structures, enhancing data efficiency. The ML algorithm, notably the gradient boosting (GB) model, showcases superior predictive capabilities, as evidenced by R 2 and RMSE values of 0.92 and 0.07, respectively. This outcome is akin to predictions employing 20 QCP features, yielding R 2 and RMSE values of 0.90 and 0.08, respectively. The study substantiates SMILES as a robust single feature for accurate CIE prediction, revealing a moderate correlation between SMILES-represented structures and CIE values. This underscores the effectiveness of SMILES-based ML in assessing corrosion inhibition potential, thereby advancing predictive modeling in corrosion science. Integrating machine learning and SMILES notation presents an efficient approach for evaluating the corrosion inhibition capacity of diverse molecular structures.

关键词： Machine learning SMILES Quantum chemical property Corrosion inhibition Pyridine-quinoline

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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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Medical Image Analysis 2024年 97卷 103257页

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics AGH University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan FORXAI Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAlberta United States Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods. © 2024

关键词： Tissue

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Case-based cybersecurity incident resolution 31

Case-based cybersecurity incident resolution

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31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Colome, Marcelo Nunes, Raul Ceretta De Lima Silva, Luis Alvaro Graduate Program in Computer Science - PPGCC Applied Computing Department Federal University of Santa Maria Av. Roraima Campus UFSM Camobi Santa Maria RS Brazil

ISBN: (纸本)1891706489

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes to integrate Case-Based Reasoning techniques and IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Experimental results so far obtained with a Case-based Cybersecurity Incident Resolution System (CbCSecIRS) implemented show that information security knowledge can be retained in a reusable memory, so improving the resolution of new cybersecurity problems. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Intrusion detection

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Minimizing the ambiguities in medical devices regulations based on software requirement engineering techniques 2

Minimizing the ambiguities in medical devices regulations ba...

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2nd International Conference on Data science, E-Learning and Information Systems, DATA 2019

作者： Alsaadi, Mohmood Lisitsa, Alexei Qasaimeh, Malik Applied Computing Research Group Al-Maarif University College Anbar Iraq Department of Computer Science University of Liverpool Liverpool United Kingdom King Hussein Faculty of Computing Sciences Princess Sumaya University for Technology Jordan

ISBN: (纸本)9781450372848

Trusted medical software devices, must comply with one of the healthcare regulations such as Food and Drug Administration (FDA), EU Medical Device Regulation (MDR), and Health Insurance Portability and Accountability Act (HIPAA). Since these regulations are enacted by law legislators and written in a legal text, these regulations are typically written with ambiguities. However, people have a different interpretation for the legal text for example, software developers faced challenges in identifying and understanding the regulatory requirements that are related to the software development process. Moreover, ambiguous in regulatory requirements play a big role in software compliance with regulatory particularly, when the requirements are legal text. In this paper, we intend to minimize the ambiguities in EU MDR requirements based on requirement engineering techniques in order to make MDR requirements clear and precise for software developers. In our previous work, we extracted the requirements of MDR that would affect the software development life cycle (SDLC) directly or indirectly. Herein, we will identify the ambiguity types in the extracted requirements of MDR. Moreover, this paper will present a method to minimize the ambiguities in MDR requirements based on requirement engineering techniques (user story and use case diagram). Finally, this work will be evaluated by the critical-to-quality tree measurement technique. © 2019 Association for computing Machinery. ACM

关键词： Software design

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MāOri loanwords: A corpus of New Zealand english tweets 57

MāOri loanwords: A corpus of New Zealand english tweets

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57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Student Research Workshop, SRW 2019

作者： Trye, David Calude, Andreea S. Bravo-Marquez, Felipe Keegan, Te Taka Computing and Mathematical Sciences University of Waikato New Zealand School of General and Applied Linguistics University of Waikato New Zealand Department of Computer Science University of Chile & IMFD Chile

ISBN: (纸本)9781950737475

Maori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the Maori community. Motivated by the lack of linguistic resources for studying how Maori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected Maori words that are likely to be known by New Zealanders who do not speak Maori. Since over 30% of these words turned out to be irrelevant (e.g., mana is a popular gaming term, Moana is a character from a Disney movie), we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets. © 2019 Association for Computational Linguistics.

关键词： Computational linguistics

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