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Quantifying gender imbalance in East Asian academia: Research career and citation practice

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arXiv 2023年

作者： Nakajima, Kazuki Liu, Ruodan Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce in the world, have been the main geographical regions for research on gender imbalance in academia. However, the academia in East Asia, which accounts for a substantial fraction of research, may be exposed to strong gender imbalance because Asia has been facing persistent and stronger gender imbalance in society at large than Europe and North America. Here we use publication data between 1950 and 2020 to analyze gender imbalance in academia in China, Japan, and South Korea in terms of the number of researchers, their career, and citation practice. We found that, compared to the average of the other countries, gender imbalance is larger in these three East Asian countries in terms of the number of researchers and their citation practice and additionally in Japan in terms of research career. Moreover, we found that Japan has been exposed to the larger gender imbalance than China and South Korea in terms of research career and citation practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Geographical regions

Fully precessing higher-mode surrogate model of effective-one-body waveforms

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Physical Review D 2024年第12期110卷 124038-124038页

作者： Bhooshan Gadre Michael Pürrer Scott E. Field Serguei Ossokine Vijay Varma Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany Institute for Gravitational and Subatomic Physics (GRASP) Utrecht University Princetonplein 1 3584 CC Utrecht The Netherlands Department of Physics East Hall University of Rhode Island Kingston Rhode Island 02881 USA URI Center for Computational Research Tyler Hall University of Rhode Island Kingston Rhode Island 02881 USA Department of Mathematics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA

We present a surrogate model of SEOBNRv4PHM, a fully precessing time-domain effective-one-body waveform model including subdominant modes. We follow an approach similar to that used to build recent numerical relativity surrogate models. Our surrogate is 5000 M in duration, covers mass ratios up to 1∶20 and dimensionless spin magnitudes up to 0.8. Validating the surrogate against an independent test set, we find that the median mismatch error is less than 10−3, which is typically smaller than the modeling error of SEOBNRv4PHM itself. At high total mass, a few percent of configurations can exceed mismatches of 10−2 if they are highly precessing and exceed a mass ratio of 1∶4. This surrogate is nearly 2 orders of magnitude faster than the underlying time-domain SEOBNRv4PHM model and can be evaluated in ∼50 ms. Bayesian inference analyses with SEOBNRv4PHM are typically very computationally demanding and can take from weeks to months to complete. The 2 order of magnitude speedup attained by our surrogate model enables practical parameter estimation analyses with this waveform family. This is crucial because Bayesian inference allows us to recover the masses and spins of binary black hole mergers given a model of the emitted gravitational waveform along with a description of the noise.

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Optimal design for kernel interpolation: Applications to uncertainty quantification

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arXiv 2021年

作者： Narayan, Akil Yan, Liang Zhou, Tao Department of Mathematics Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States School of Mathematics Southeast University Nanjing China LSEC Institute of Computational Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

The paper is concerned with classic kernel interpolation methods, in addition to approximation methods that are augmented by gradient measurements. To apply kernel interpolation using radial basis functions (RBFs) in a stable way, we propose a type of quasi-optimal interpolation points, searching from a large set of candidate points, using a procedure similar to designing Fekete points or power function maximizing points that use pivot from a Cholesky decomposition. The proposed quasi-optimal points results in smaller condition number, and thus mitigates the instability of the interpolation procedure when the number of points becomes large. Applications to parametric uncertainty quantification are presented, and it is shown that the proposed interpolation method can outperform sparse grid methods in many interesting cases. We also demonstrate the new procedure can be applied to constructing gradient-enhanced Gaussian process emulators. © 2021, CC BY.

关键词： Radial basis function networks

Development of multifactor forecasting model based on fuzzy time series 35

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Development of multifactor forecasting model based on fuzzy ...

35th International Conference on Information Technologies, InfoTech 2021

作者： Demidova, Liliya Anatolievna Stepanov, Maksim Anatolievich Institute of Information Technologies Russian Technological University - MIREA Department of Corporate Information Systems Vernadsky Avenue78 Moscow119454 Russia Ryazan State Radio Engineering University Named after V.F. Utkin Faculty of Computing Engineering Department of Computational and Applied Mathematics Gagarin Str. 59/1 Ryazan390005 Russia

ISBN: (纸本)9781665403245

The problem of developing a multifactor forecasting model using the tools of fuzzy set theory has been considered. It is proposed to represent each of the analyzed factors, which have the same source of occurrence, but different trajectories of evolution, in the form of a fuzzy time series. The groups of fuzzy logical dependencies on the basis of fuzzy time series for each trajectory have been created. The multifactor forecasting model is developed with the use of these groups of fuzzy logical dependencies, taking into account the operations of union and intersection. Optimal parameter values of the forecasting model, providing a minimum value of the average forecasting error rate, have been determined using a genetic algorithm. An example which confirms the effectiveness of the proposed approach to the development of a multifactor forecasting model has been given. © 2021 IEEE.

关键词： Time series

Tightness and equivalence of semidefinite relaxations for mimo detection

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arXiv 2021年

作者： Jiang, Ruichen Liu, Ya-Feng Bao, Chenglong Jiang, Bo Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Yau Mathematical Sciences Center Tsinghua University Beijing100084 China School of Mathematical Sciences Key Laboratory for NSLSCS of Jiangsu Province Nanjing Normal University Nanjing210023 China

The multiple-input multiple-output (MIMO) detection problem, a fundamental problem in modern digital communications, is to detect a vector of transmitted symbols from the noisy outputs of a fading MIMO channel. The maximum likelihood detector can be formulated as a complex least-squares problem with discrete variables, which is NP-hard in general. Various semidefinite relaxation (SDR) methods have been proposed in the literature to solve the problem due to their polynomial-time worst-case complexity and good detection error rate performance. In this paper, we consider two popular classes of SDR-based detectors and study the conditions under which the SDRs are tight and the relationship between different SDR models. For the enhanced complex and real SDRs proposed recently by Lu et al., we refine their analysis and derive the necessary and sufficient condition for the complex SDR to be tight, as well as a necessary condition for the real SDR to be tight. In contrast, we also show that another SDR proposed by Mobasher et al. is not tight with high probability under mild conditions. Moreover, we establish a general theorem that shows the equivalence between two subsets of positive semidefinite matrices in different dimensions by exploiting a special "separable" structure in the constraints. Our theorem recovers two existing equivalence results of SDRs defined in different settings and has the potential to find other applications due to its *** Codes 90C22, 90C20, 90C46, 90C27 Copyright © 2021, The Authors. All rights reserved.

关键词： Polynomial approximation

Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures

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Physical Review A 2024年第6期110卷 062418-062418页

作者： Myeongjin Shin Seungwoo Lee Junseo Lee Mingyu Lee Donghwa Ji Hyeonjun Yeo Kabgyun Jeong School of Computing KAIST Daejeon 34141 South Korea Quantum AI Team Norma Inc. Seoul 04799 South Korea Department of Computer Science and Engineering Seoul National University Seoul 08826 South Korea College of Liberal Studies Seoul National University Seoul 08826 South Korea Department of Physics and Astronomy Seoul National University Seoul 08826 South Korea Research Institute of Mathematics Seoul National University Seoul 08826 South Korea School of Computational Sciences Korea Institute for Advanced Study Seoul 02455 South Korea

The estimation of quantum entropies and distance measures, such as von Neumann entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the Bures distance, has been a key area of research in quantum information science. In our study, we introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Estimation algorithms for these quantities are generally tied to the size of the Hilbert space of the quantum state to be estimated. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities. We provide an in-depth discussion of the physical scenarios and limitations in which our algorithm is applicable, as well as the learnability of the proposed quantum neural network.

关键词： Hilbert spaces

User grouping and reflective beamforming for IRS-aided URLLC

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arXiv 2021年

作者： Xie, Hailiang Xu, Jie Liu, Ya-Feng Liu, Liang Ng, Derrick Wing Kwan School of Information Engineering Guangdong University of Technology Guangzhou510006 China Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales Australia

This paper studies an intelligent reflecting surface (IRS)-aided downlink ultra-reliable and low-latency communication (URLLC) system, in which an IRS is dedicatedly deployed to assist a base station (BS) to send individual short-packet messages to multiple users. To enhance the URLLC performance, the users are divided into different groups and the messages for users in each group are encoded into a single codeword. By considering the time division multiple access (TDMA) protocol among different groups, our objective is to minimize the total latency for all users subject to their individual reliability requirements, via jointly optimizing the user grouping and block-length allocation at the BS together with the reflective beamforming at the IRS. We solve the latency minimization problem via the alternating optimization, in which the blocklengths and the reflective beamforming are optimized by using the techniques of successive convex approximation (SCA) and semi-definite relaxation (SDR), while the user grouping is updated by K-means and greedy-based methods. Numerical results show that the proposed designs can significantly reduce the communication latency, as compared to various benchmark schemes, which unveil the importance of user grouping and reflective beamforming optimization for exploiting the joint encoding gain and enhancing the worst-case user performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Beamforming

New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization

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arXiv 2020年

作者： Zhao, Ting Liu, Hongwei Liu, Zexian School of Mathematics and Statistics Xidian University Xi’an710126 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering computing AMSS Chinese Academy of Sciences Beijing100190 China

In this paper, two new subspace minimization conjugate gradient methods based on p−regularization models are proposed, where a special scaled norm in p−regularization model is analyzed. Different choices for special scaled norm lead to different solutions to the p−regularized subproblem. Based on the analyses of the solutions in a two-dimensional subspace, we derive new directions satisfying the sufficient descent condition. With a modified nonmonotone line search, we establish the global convergence of the proposed methods under mild assumptions. R−linear convergence of the proposed methods are also analyzed. Numerical results show that, for the CUTEr library, the proposed methods are superior to four conjugate gradient methods, which were proposed by Hager and Zhang (SIAM J Optim 16(1):170-192, 2005), Dai and Kou (SIAM J Optim 23(1):296-320, 2013), Liu and Liu (J Optim Theory Appl 180(3):879-906, 2019) and Li et al. (Comput Appl Math 38(1): 2019), respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Conjugate gradient method

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

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Large-Scale Materials Modeling at Quantum Accuracy: Ab Initi...

Supercomputing Conference

作者： Sambit Das Bikash Kanungo Vishal Subramanian Gourab Panigrahi Phani Motamarri David Rogers Paul M. Zimmerman Vikram Gavini Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Materials Science & Engineering University of Michigan Ann Arbor MI USA Department of Computational and Data Sciences Indian Institute of Science Bangalore Karnataka India Scientific Computing Group Oak Ridge National Laboratory Oak Ridge TN USA Department of Chemistry University of Michigan Ann Arbor MI USA

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

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