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U-index: A new Universal metric as unique indicator of researcher's contributions to academic knowledge

Scientific African 2022年 16卷

作者： Asaolu, Olumuyiwa S. Jaiyeola, Temitope G. Usikalu, Mojisola R. Gayawan, Ezra Atolani, Olubunmi Adeyemi, Oluyomi S. Department of Systems Engineering University of Lagos Lagos State Akoka 100213 Nigeria Department of Mathematics Faculty of Science Obafemi Awolowo University Osun State Ile-Ife 220282 Nigeria Department of Physics Covenant University Ogun State Ota 112212 Nigeria Department of Statistics Federal University of Technology Ondo State Akure 340252 Nigeria Department of Chemistry University of Ilorin Kwara State Ilorin 240003 Nigeria Department of Biochemistry Landmark University Kwara State Omu-Aran 251101 Nigeria

Quantification and ranking of scholars’ output via a consideration of the citations of one's works has been of global interest within the last two decades, after criticisms of the approach that uses the Journal Impact Factor as a proxy. Various metrics exist but are deemed flawed due to their susceptibility to manipulations and citation preponderance variability across different fields of study. Hence, we present the Universal Index (U-index or U) towards resolving the controversy surrounding the indication of an entity's contributions to academic knowledge. U-index is the average weighted ratio of citation rates per annum of one's research works relative to the Impact Factor of the respective publication outlets. Some attributes and benefits of this new metric are established herein. This work demonstrates the viability of U-index from synthesis as well as from an analytical survey of the publication portfolio of 32 researchers. An evaluation of Kendall tau coefficient shows that for a mix of scholars from diverse fields, ranking these researchers based on U-index is distinct from, and preferred to ratings that use h-index. A download link is provided to the Universal Index Calculator freeware, developed to analyze a scholar's profile generated from indexing databases such as Google Scholar. © 2022 The Authors

关键词： Citations H-index Impact Factor U-index Universal metric

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Antenna characterization for the HIRAX experiment

arXiv

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arXiv 2022年

作者： Kuhn, Emily R. Saliwanchik, Benjamin R.B. Bandura, Kevin Bianco, Michele Chiang, H. Cynthia Crichton, Devin Deng, Meiling Gaddam, Sindhu Gerodias, Kit Gumba, Austin Harris, Maile Moodley, Kavilan Mugundhan, V. Newburgh, Laura Peterson, Jeffrey Pieters, Elizabeth Polish, Anna R. Refregier, Alexandre Sampath, Ajith Santos, Mario G. Sengate, Onkabetse Sievers, Jonathan Smith, Ema Tyndall, Will Walters, Anthony Weltman, Amanda Wulf, Dallas Department of Physics Yale University New HavenCT United States Instrumentation Division Brookhaven National Laboratory UptonNY United States Department of Computer Science and Electrical Engineering Center for GravitationalWaves and Cosmology West Virginia University MorgantownWV United States Laboratoire d'Astrophysique Ecole Polytechnique Federale de Lausanne EPFL Observatoire de Sauverny Versoix Switzerland Department of Physics McGill University MontrealQC Canada Institute for Particle Physics and Astrophysics ETH Zurich Zurich Switzerland Perimeter Institute for Theoretical Physics WaterlooON Canada Dominion Radio Astrophysical Observatory KaledenBC Canada School of Mathematics Statistics and Computer Science UKZN Saudi Arabia Astrophysics Research Centre UKZN Saudi Arabia Dept of Physics Carnegie Mellon University PittsburghPA United States Department of Physics and Astronomy University of Western Cape Cape Town7535 South Africa School of Chemistry and Physics UKZN Saudi Arabia Department of Mathematics and Applied Mathematics University of Cape Town Saudi Arabia

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) aims to improve constraints on the dark energy equation of state through measurements of large-scale structure at high redshift (0.8 700MHz. Noise temperature measurements of the HIRAX feeds were performed in a custom apparatus built at Yale. In this system, identical loads, one cryogenic and the other at room temperature, are used to take a differential (Y-factor) measurement from which the noise of the system is inferred. Several measurement sets have been conducted using the system, involving CHIME feeds as well as four of the HIRAX active feeds. These measurements give the first noise temperature measurements of the HIRAX feed, revealing a ∼60K noise temperature (relative to 30K target) with 40K peak- to-peak frequency-dependent features, and provide the first demonstration of feed repeatability. Both findings inform current and future feed designs. © 2022, CC BY.

关键词： Equations of state

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Publisher's Note: “Reacting and non-reacting, three-dimensional shear layers with spanwise stretching” [Phys. Fluids 34, 123602 (2022)]

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Physics of Fluids 2023年第1期35卷

作者： Jonathan L. Palafoutas William A. Sirignano 1Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08540 USA 2Department of Mechanical and Aerospace Engineering University of California Irvine Irvine California 92697 USA

This article was published online on 2 December 2022 with errors throughout the paper. All the derivative terms had the wrong denominator; the denominators were

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INFORMATION GEOMETRY OF ORTHOGONAL INITIALIZATIONS AND TRAINING 8

INFORMATION GEOMETRY OF ORTHOGONAL INITIALIZATIONS AND TRAIN...

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8th International Conference on Learning Representations, ICLR 2020

作者： Sokól, Piotr Aleksander Park, Il Memming Department of Neurobiology and Behavior Institutes for Advanced Computing Science and AI-driven Discovery and Innovation Stony Brook University Stony BrookNY11733 United States Department of Applied Mathematics and Statistics and Electrical and Computer Engineering Institutes for Advanced Computing Science and AI-driven Discovery and Innovation Stony Brook University Stony BrookNY11733 United States

Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It gave rise to a prescriptive theory for initializing feed-forward neural networks with orthogonal weights, which ensures that both the forward propagated activations and the backpropagated gradients are near 2 isometries and as a consequence training is orders of magnitude faster. Despite strong empirical performance, the mechanisms by which critical initializations confer an advantage in the optimization of deep neural networks are poorly understood. Here we show a novel connection between the maximum curvature of the optimization landscape (gradient smoothness) as measured by the Fisher information matrix (FIM) and the spectral radius of the input-output Jacobian, which partially explains why more isometric networks can train much faster. Furthermore, given that orthogonal weights are necessary to ensure that gradient norms are approximately preserved at initialization, we experimentally investigate the benefits of maintaining orthogonality throughout training, and we conclude that manifold optimization of weights performs well regardless of the smoothness of the gradients. Moreover, we observe a surprising yet robust behavior of highly isometric initializations - even though such networks have a lower FIM condition number at initialization, and therefore by analogy to convex functions should be easier to optimize, experimentally they prove to be much harder to train with stochastic gradient descent. We conjecture the FIM condition number plays a non-trivial role in the optimization. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Gradient methods

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An advanced algorithm for discrimination prevention in data mining

An advanced algorithm for discrimination prevention in data ...

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Shuoyu Wang Jie Ren Hao Fang Jun Pan Xin Hu Tingyun Zhao College of Information Science and Technology Tibet University Lhasa China Department of Computer Science and Technology Lyuliang University Lvliang China College of Foreign Languages Liaoning University of Traditional Chinese Medicine Shenyang China School of Electronic and Information Engineering Beihang University Beijing China School of Mathematics and Statistics Yangtze Normal University Chongqing China School of Software and Big Data Changzhou College of Information Technology Changzhou China

ISBN: (纸本)9781665470544

Data mining has been effectively utilized in huge expanded application areas. Associations overall go with significant business choices in light of mining of data. Discrimination in navigation and preservation of protection of data are a couple of significant difficulties in data mining. Discrimination is of two sorts, Direct Discrimination and Indirect Discrimination. Discrimination prevention includes two significant angles disclosure of direct and additionally roundabout discrimination and data transformation as alteration of separating rules without influencing data quality. Existing strategies for discrimination prevention utilize one of the three methodologies, in particular, the pre-process, in-process or post-process approach. Tests for the exhibition assessment of all calculations by fluctuating these info boundaries, each in turn to review and break down impact of these boundaries.

关键词： Computer science Navigation Data integrity Computational modeling Optics Minimization Data models

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A generative approach to joint modeling of quantitative and qualitative responses

arXiv

引用

arXiv 2021年

作者： Kang, Xiaoning Kang, Lulu Chen, Wei Deng, Xinwei International Business College Institute of Supply Chain Analytics Dongbei University of Finance and Economics Dalian China Department of Applied Mathematics Illinois Institute of Technology Chicago United States Department of Mechanical Materials & Aerospace Engineering Illinois Institute of Technology Chicago United States Department of Statistics Virginia Tech BlacksburgVA24061 United States

In many scientific areas, data with quantitative and qualitative (QQ) responses are commonly encountered with a large number of predictors. By exploring the association between QQ responses, existing approaches often consider a joint model of QQ responses given the predictor variables. However, the dependency among predictive variables also provides useful information for modeling QQ responses. In this work, we propose a generative approach to model the joint distribution of the QQ responses and predictors. The proposed generative model provides efficient parameter estimation under a penalized likelihood framework. It achieves accurate classification for qualitative response and accurate prediction for quantitative response with efficient computation. Because of the generative approach framework, the asymptotic optimality of classification and prediction of the proposed method can be established under some regularity conditions. The performance of the proposed method is examined through simulations and real case studies in material science and genetics. © 2021, CC BY.

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Recursive residuals for linear mixed models

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Quality and Quantity 2019年第3期53卷 1263-1274页

作者： Bani-Mustafa, Ahmed Matawie, K.M. Finch, C.F. Al-Nasser, Amjad Ciavolino, Enrico Department of Mathematics Australian College of Kuwait Safat Kuwait School of Computing Engineering and Mathematics Western Sydney University Sydney NSW Australia School of Medical and Health Sciences Edith Cowan University Joondalup Australia Department of Statistics Faculty of Science Yarmouk University Irbid Jordan University of Salento Lecce Italy

This paper presents and extends the concept of recursive residuals and their estimation to an important class of statistical models, Linear Mixed Models (LMM). Recurrence formulae are developed and recursive residuals are defined. Recursive computable expressions are also developed for the model’s likelihood, together with its derivative and information matrix. The theoretical framework for developing recursive residuals and their estimation for LMM varies with the estimation method used, such as the fitting-of-constants or the Best Linear Unbiased Predictor method. These methods are illustrated through application to an LMM example drawn from a published study. Model fit is assessed through a graphical display of the developed recursive residuals and their Cumulative Sums. © 2018, Springer Nature B.V.

关键词： BLUP Fitting-of-constant Linear mixed model Recursive estimation Recursive residuals

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Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments

arXiv

引用

arXiv 2021年

作者： Li, Xiaoxu Yang, Xiaochen Ma, Zhanyu Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology China Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications China School of Mathematics and Statistics University of Glasgow United Kingdom Department of Statistical Science University College London United Kingdom

Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few-shot image classification is deep metric learning. These methods, by classifying unseen samples according to their distances to few seen samples in an embedding space learned by powerful deep neural networks, can avoid overfitting to few training images in few-shot image classification and have achieved the state-of-the-art performance. In this paper, we provide an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorize them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures. With this taxonomy, we identify the novelties of different methods and problems they face. We conclude this review with a discussion on current challenges and future trends in few-shot image classification. © 2021, CC BY.

关键词： Image classification

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Splitting Method for Stochastic Navier-Stokes Equations

arXiv

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arXiv 2025年

作者： Zhu, Jie Zhu, Yujun Ming, Ju Gunzburger, Max D. School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Scientific Computing Florida State University TallahasseeFL32304 United States

This paper investigates the two-dimensional stochastic steady-state Navier-Stokes(NS) equations with additive random noise. We introduce an innovative splitting method that decomposes the stochastic NS equations into a deterministic NS component and a stochastic equation. We rigorously analyze the proposed splitting method from the perspectives of equivalence, stability, existence and uniqueness of the solution. We also propose a modified splitting scheme, which simplified the stochastic equation by omitting its nonlinear terms. A detailed analysis of the solution properties for this modified approach is provided. Additionally, we discuss the statistical errors with both the original splitting format and the modified scheme. Our theoretical and numerical studies demonstrate that the equivalent splitting scheme exhibits significantly enhanced stability compared to the original stochastic NS equations, enabling more effective handling of nonlinear characteristics. Several numerical experiments were performed to compare the statistical errors of the splitting method and the modified splitting method. Notably, the deterministic NS equation in the splitting method does not require repeated solving, and the stochastic equation in the modified scheme is free of nonlinear terms. These features make the modified splitting method particularly advantageous for large-scale computations, as it significantly improves computational efficiency without compromising accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Navier Stokes equations

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An innovative hybrid method combining grey wolf and marine predator optimization techniques for global and constrained problem-solving

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Journal of Supercomputing 2025年第8期81卷 1-68页

作者： Biswas, Saptadeep Maiti, Binanda Singh, Gyan Mirjalili, Seyedali Simic, Vladimir Pamucar, Dragan Bera, Uttam Kumar Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Centre for Artificial Intelligence Research and Optimization Torrens University 90 Bowen Terrace Fortitude ValleyQLD4006 Australia Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seoul Seongbuk-gu02841 Korea Republic of Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Industrial Engineering Department Faculty of Engineering and Natural Sciences İstinye University Istanbul Sariyer34396 Turkey

The grey wolf optimizer (GWO) is a metaheuristic algorithm recognized for its effectiveness;however, it faces several limitations, such as a lack of diversity in its population, a tendency to prematurely converge on local optima, and insufficient convergence speed. To address these issues, we propose an innovative hybrid algorithm that combines the advantages of GWO with the marine predator algorithm (MPA), leading to the creation of the Hybrid Grey Wolf Marine Predator Algorithm (HGWMPA). By integrating the adaptive characteristics of MPA, this hybrid approach fosters a robust search mechanism that effectively balances exploration and exploitation. We performed comprehensive experimental assessments utilizing benchmark functions from the CEC competitions of 2014, 2017, 2020, and 2022. The findings indicate that the HGWMPA consistently surpasses numerous leading optimization methods, achieving an average rank of 1 across most benchmark functions. Specifically, HGWMPA secured top positions in 76.67% of functions in the CEC 2014 test suite, 70.00% in CEC 2017, 90.00% in CEC 2020, and 66.67% in CEC 2022, showcasing its robust performance across various benchmark scenarios. The experimental results reveal that HGWMPA excels in global exploration, local exploitation, convergence speed, and accuracy, achieving optimal or near-optimal solutions with minimal standard deviations. A detailed performance evaluation, employing the Wilcoxon rank-sum test and the MARCOS MCDM ranking technique, further confirms the competitive advantages of HGWMPA. The algorithm’s adaptability, characterized by the dynamic adjustment of parameters, enables an effective balance between exploration and exploitation, making it particularly suitable for a wide range of engineering design problems. Sensitivity analyses indicate that changes in population size, maximum iteration, and other parameter limits significantly influence the algorithm’s performance, providing valuable insights for enhancing the

关键词： Product design

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