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Franklin Open 2025年 10卷

作者： Salihu, Nasiru Kumam, Poom Salisu, Sani Khammahawong, Konrawut Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Sciences Modibbo Adama University Yola 652105 Nigeria KMUTT-Fixed Point Research Laboratory Room SCL 802 Science Laboratory Building Department of Mathematics Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Natural and Applied Sciences Sule Lamido University Kafin Hausa Jigawa Nigeria Applied Mathematics for Science and Engineering Research Unit (AMSERU) Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand

Hybrid nonlinear conjugate gradient (CG) methods are known to be efficient and have less memory requirement for solving optimization problems in Euclidean spaces. The formulations strategically switch between two or more CG parameters to enhance their general structures. In this work, some hybrid Riemannian CG methods are presented, which are sufficiently descent irrespective of the line search technique employed and converges globally using scaled version of the nonexpensive condition. Finally, the numerical experiments are performed to confirm the strength of the schemes by considering some Riemannian optimization problems. The numerical results obtained when compared to some classical Riemannian CG methods indicated the efficiency of the proposed hybrid Riemannian CG methods. © 2025 The Authors

关键词： Global convergence Retraction Riemannian conjugate gradient method Riemannian optimization Riemannian wolfe conditions Vector transport

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Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures

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Computer Modeling in engineering & Sciences 2021年第5期127卷 771-800页

作者： Muhammad Riaz Harish Garg Hafiz Muhammad Athar Farid Ronnason Chinram Department of Mathematics University of the PunjabLahore54590Pakistan School of Mathematics Thapar Institute of Engineering and Technology(Deemed University)Patiala147004India Algebra and Applications Research Unit Division of Computational ScienceFaculty of SciencePrince of Songkla UniversityHat YaiSongkhla90110Thailand Centre of Excellence in Mathematics Bangkok10400Thailand

This paper aims to introduce the novel concept of the bipolar picture fuzzy set(BPFS)as a hybrid structure of bipolar fuzzy set(BFS)and picture fuzzy set(PFS).BPFS is a new kind of fuzzy sets to deal with bipolarity(both positive and negative aspects)to each membership degree(belonging-ness),neutral membership(not decided),and non-membership degree(refusal).In this article,some basic properties of bipolar picture fuzzy sets(BPFSs)and their fundamental operations are *** score function,accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy numbers(BPFNs).Additionally,the concept of new distance measures of BPFSs is presented to discuss geometrical properties of *** the context of BPFSs,certain aggregation operators(AOs)named as“bipolar picture fuzzy weighted geometric(BPFWG)operator,bipolar picture fuzzy ordered weighted geometric(BPFOWG)operator and bipolar picture fuzzy hybrid geometric(BPFHG)operator”are defined for information aggregation of *** on the proposed AOs,a new multicriteria decision-making(MCDM)approach is proposed to address uncertain real-life ***,a practical application of proposed methodology is also illustrated to discuss its feasibility and applicability.

关键词： Bipolar picture fuzzy set aggregation operators distance measures pattern recognition MCDM

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JTAV: Jointly learning social media content representation by fusing textual, acoustic, and visual features 27

JTAV: Jointly learning social media content representation b...

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27th International Conference on computational Linguistics, COLING 2018

作者： Liang, Hongru Wang, Haozheng Wang, Jun You, Shaodi Sun, Zhe Wei, Jin-Mao Yang, Zhenglu CCCE Nankai University Tianjin China College of Mathematics and Statistics Science Ludong University China Data61-CSIRO Australian National University Canberra Australia RIKEN Head Office for Information Systems and Cybersecurity Computational Engineering Applications Unit Japan

ISBN: (纸本)9781948087506

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin. © 2018 COLING 2018 - 27th International Conference on computational Linguistics, Proceedings. All rights reserved.

关键词： Recommender systems

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Ti interstitial flows giving rutile TiO2 reoxidation process enhancement in (001) surface

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Physical Review Materials 2019年第12期3卷 125801-125801页

作者： Tom Ichibha Anouar Benali Kenta Hongo Ryo Maezono School of Information Science JAIST 1-1 Asahidai Nomi Ishikawa 923-1292 Japan and Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan

We revisited ab initio evaluations of the barrier energies along the possible diffusion paths of the defects in rutile TiO2 by using a diffusion Monte Carlo method. We found that Ti interstitials hopping along the c axis are identified as the major diffusion directing to the (001) surface, contradicting any of the previous density functional theory studies. Our finding reasonably explains recent experiments reporting that the photocatalytic activity in the (001) surface is superior to that in the (110) surface: The faster Ti diffusion directing to the (001) surface leads to the better self-compensation ability and maintains its photocatalytic activity.

关键词： Self-diffusion Transition metal oxides Diffusion quantum Monte Carlo

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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Science China(Technological Sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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Generalized relation learning with semantic correlation awareness for link prediction

arXiv

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arXiv 2020年

作者： Zhang, Yao Zhang, Xu Wang, Jun Liang, Hongru Lei, Wenqiang Sun, Zhe Jatowt, Adam Yang, Zhenglu TKLNDST CS Nankai University China Ludong University China National University of Singapore Singapore Singapore Computational Engineering Applications Unit RIKEN Japan University of Innsbruck Austria

Developing link prediction models to automatically complete knowledge graphs has recently been the focus of significant research interest. The current methods for the link prediction task have two natural problems: 1) the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations. These two problems limit the training effectiveness and practical applications of the existing link prediction models. We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. GRL conducts a generalized relation learning, which is aware of semantic correlations between relations that serve as a bridge to connect semantically similar relations. After training with GRL, the closeness of semantically similar relations in vector space and the discrimination of dissimilar relations are improved. We perform comprehensive experiments on six benchmarks to demonstrate the superior capability of GRL in the link prediction task. In particular, GRL is found to enhance the existing link prediction models making them insensitive to unbalanced relation distributions and capable of learning unseen relations. © 2020, CC0.

关键词： Semantics

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Immuno-Metabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease comorbidity

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Biological Psychiatry Global Open Science 2025年

作者： Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina Ioannina Greece Department of Biological Applications and Technology University of Ioannina Ioannina Greece Department of Clinical Psychology University of Amsterdam Amsterdam The Netherlands Computational Science Lab Institute of Informatics University of Amsterdam Amsterdam The Netherlands Biomedical Engineering of the Department of Mechanical Engineering and Aeronautics University of Patras Patras Greece Department of Psychiatry Amsterdam UMC location Vrije Universiteit Amsterdam Boelelaan 1117 Amsterdam The Netherlands Amsterdam Public Health Mental Health Program Amsterdam The Netherlands Cardiology Geneva University Hospitals Geneva Switzerland Department of Medicine University of Geneva Geneva Switzerland Biomedical Research Institute Foundation for Research and Technology - Hellas (FORTH) Ioannina Greece Department of medical Psychology Amsterdam University Medical Centers Amsterdam The Netherlands

Background Major depressive disorder (MDD) and cardiovascular diseases (CVD) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immune-metabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods Using data from the Netherlands Study of Depression and Anxiety (NESDA: N=2,256, 66.3% female, mean age 41.75 ± 13.11 years), validated with the UK Biobank data (UKB, N=35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify (i) biomarkers, closely associated with current MDD, and (ii) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (NMR, Nightingale) and inflammatory markers were used as exposures within a Machine Learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVD, exploring longitudinal pathways through Causal Discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results Network analysis identified stable direct paths from MDD to CVD via TNF-α, tyrosine, and fatty acids, and indirect paths via acetate, HDL diameter, IL-6, alpha-1-acid glycoprotein (AGP), hs-CRP, and LDL triglycerides. Among these, acetate, tyrosine, AGP, and HDL diameter potentially mediate the MDD-CVD connection, as these were identified as key nodes within the network. UKB validation confirmed HDL diameter ( β = 0.004) and AGP ( β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and BMI. Conclusions These analyses identified biomarkers shared in MDD and CVD and may drive comorbid pathology risk.

关键词： MDD biomarkers CVD Machine Learning mediators

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Urban land use mapping with multi-spectral and SAR satellite data using neural networks

Urban land use mapping with multi-spectral and SAR satellite...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J. Heikkonen I. Kanellopoulos A. Varfis A. Steel K. Fullerton Laboratory of Computational Engineering Helsinki University of Technology Espoo Finland Environmental Mapping and Modelling Unit Space Applications Institute Italy Institute for Systems Informatics and Safety Joint Research Centre Commission of European Communities-Joint Research Center Varese Italy Environmental Mapping and Modelling Unit Space Applications Institute

Statistical, textural and Gabor features were extracted from integrated multitemporal multispectral TM data and ERS-1 SAR imagery for urban land use mapping. The computed features are first normalised using the SOM algorithm and then a decision tree algorithm is applied for feature selection. The classification procedure was carried out with a multilayer perceptron, trained with the resilient backpropagation algorithm. The authors' results demonstrate the potential of the proposed methodology.

关键词： Satellites Neural networks Feature extraction Data mining Filtering Remote sensing Urban areas Space technology Multilayer perceptrons Synthetic aperture radar

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All-electron quantum Monte Carlo with Jastrow single determinant Ansatz: Application to the sodium dimer

arXiv

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arXiv 2019年

作者： Nakano, Kousuke Maezono, Ryo Sorella, Sandro Via Bonomea 265 Trieste34136 Italy Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

In this work, we report potential energy surfaces (PESs) of the sodium dimer calculated by variational (VMC) and lattice regularized diffusion Monte Carlo (LRDMC). The VMC calculation is accurate for determining the equilibrium distance and the qualitative shape of the experimental PES. Remarkably, after the application of the LRDMC projection to this single determinant ansatz, namely the Jastrow Antisymmetrized Geminal Product (JAGP), chemical accuracy (∼1kcal/mol) is reached, and the obtained dissociation energy, equilibrium internuclear distance, and harmonic vibrational frequency are in very good agreement with the experimental ones. This outcome crucially depends on the quality of the optimization used to determine the best possible trial function within the chosen ansatz. The strategy adopted in this work is to minimize the variational energy by initializing the trial function with the DFT single determinant ansatz expanded exactly in the same atomic basis used for the corresponding VMC and LRDMC calculations. This atomic basis is ad-hoc reshaped for QMC calculations. Indeed, we multiply the standard Gaussian type atomic orbitals by a one-body Jastrow factor, satisfying in this way the electron-ion cusp conditions. This allows us to use a very small basis almost converged in the complete basis set limit, by reducing the computational effort as well as the statistical fluctuations on the total energy. In order to achieve these important advantages, we have defined a very efficient DFT algorithm in the mentioned basis, by estimating the corresponding matrix elements on a mesh, and by using a much finer mesh grid in the vicinity of nuclei. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum chemistry

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Speeding up the ab initio diffusion Monte Carlo by a smart lattice regularization

arXiv

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arXiv 2019年

One of the most significant drawbacks of the all-electron ab initio diffusion Monte Carlo (DMC) is that its computational cost drastically increases with the atomic number (Z), which typically scales with Z∼6. In this study, we introduce an algorithm based on a very efficient implementation of the Lattice Regularized Diffusion Monte Carlo (LRDMC), where the conventional time discretization is replaced by its lattice space counterpart. This scheme enables us to conveniently adopt a small lattice space in the vicinity of nuclei, and a large one in the valence region, by which a considerable speedup is achieved, especially for large atomic number Z. Indeed, the computational performances of our algorithm can be theoretically established by using the Thomas-Fermi model for heavy atoms, yielding an almost affordable scaling with the atomic number, i.e., Z∼5. This opens the way for efficient and accurate all-electron ab initio DMC in electronic structure calculations. Copyright © 2019, The Authors. All rights reserved.

关键词： Atoms

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