Research
Publications / Talks / Awards / Patents

研究分野 / Research Interests

• 線形方程式に対する数値解法
  • Krylov部分空間法： [4] [5] [6] [7] [16] [20] [32] / (2)
  • Krylov部分空間法の前処理： [1] [2] [8] [10] [49] / {1} {2}
  • シフト/ブロック線形方程式に対する数値解法： [9] [11] [13] [14] [21] [27] [37] [40] [47] [54] / {8} / (8) (22)
  • 応用： [24] [31] / {6} / (6) (12)


• 固有値問題に対する数値解法
  • 周回積分型固有値解法の基盤アルゴリズム： [12] [25] [29] [61] / {10} / (1) (24)
  • 周回積分型固有値解法の解析・改良： [17] [18] [19] [22] [23] [26] [35] [53] / {9} / (3) (5) (17)
  • 精度保証計算： [43] [60] / (15) (25)
  • 無限次元固有値問題の解法： [58] / (26)
  • 応用： [33] [38] [41] / {3} {4} {11} {22} / (4) (10) (11) (13)


• 機械学習・ディープニューラルネットワーク
  • 行列分解型ディープニューラルネットワーク計算法： [28] [30] / {5} {20} / (7)
  • 次元削減法・特徴量選択・異常検知： [42] [44] [46] [48] [52] [55] [57] [68] / {13} {17} {18} {23} {25} / (16) (21) (23)
  • 分散データ統合解析： [45] [50] [51] [56] [59] [62] [63] [64] [65] [66] [67] / {14} {19} {21} {24} {26} {29} {31} {32} {33} / (14) (18) (19) (20) (28) (29) (30) (31) (32) (33) (34)


• その他
  • 一般内積における行列の直交化： [39] / (9)
  • Extended Krylov部分空間の直交基底生成法： [15]
  • 対称行列束の同時帯化： [36]
  • 行列関数計算： [3]
  • 低ランクテンソル補完： {27} {30}
  • テンソルネットワーク計算： [34] / {12}
  • 輝度伝達行列推定法： {15} {16}

Publications

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書籍 / Book

1. KS情報科学専門書『データサイエンスはじめの一歩』（分担執筆）
   佐久間淳, 國廣昇（編著）
   講談社, 2024.
2. シリーズ応用数理 【6】巻『数値線形代数の数理とＨＰＣ』（分担執筆）
   日本応用数理学会 監修・櫻井 鉄也・松尾 宇泰・片桐 孝洋 編
   共立出版, 2018.

学術論文 / Journal Papers

1. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Xiucai Ye,
   Numerical Analysis for Data Relationship,
   In: Ikeda, K., et al. (eds) Advanced Mathematical Science for Mobility Society, Springer, Singapore, pp 61-77, 2024. [abstract]
   
   

   [abstract] In recent years, a vast amount of data has been accumulated across various fields in industry and academia, and with the rise of artificial intelligence and machine learning technologies, knowledge discovery and high-precision predictions through such data have been demanded. However, real-world data is diverse, including network data that represent relationships, data with multiple modalities or views, data that is distributed across multiple institutions and requires a certain level of information confidentiality.

   
   


2. Hiromi Yamashiro, Kazumasa Omote, Akira Imakura, Tetsuya Sakurai,
   Toward the Application of Differential Privacy to Data Collaboration,
   IEEE Access, Vol.12, 63292-63301, 2024. [abstract]
   
   

   [abstract] Federated Learning, a model-sharing method, and Data Collaboration, a non-model-sharing method, are recognized as data analysis methods for distributed data. In Federated Learning, clients send only the parameters of a machine learning model to the central server. In Data Collaboration, clients send data that has undergone irreversibly transformed through dimensionality reduction to the central server. Both methods are designed with privacy concerns, but privacy is not guaranteed. Differential Privacy, a theoretical and quantitative privacy criterion, has been applied to Federated Learning to achieve rigorous privacy preservation. In this paper, we introduce a novel method using PCA (Principal Component Analysis) that finds low-rank approximation of a matrix preserving the variance, aiming to apply Differential Privacy to Data Collaboration. Experimental evaluation using the proposed method show that differentially-private Data Collaboration achieves comparable performance to differentially-private Federated Learning.

   
   


3. Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
   Collaborative causal inference on distributed data,
   Expert Systems with Applications, Vol.244, 123024, 2024. [abstract]
   
   

   [abstract] In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects. In this study, we propose a data collaboration quasi-experiment (DC-QE) that resolves the lack of both subjects and covariates, reducing random errors and biases in the estimation. Our method involves constructing dimensionality-reduced intermediate representations from private data from local parties, sharing intermediate representations instead of private data for privacy preservation, estimating propensity scores from the shared intermediate representations, and finally, estimating the treatment effects from propensity scores. Through numerical experiments on both artificial and real-world data, we confirm that our method leads to better estimation results than individual analyses. While dimensionality reduction loses some information in the private data and causes performance degradation, we observe that sharing intermediate representations with many parties to resolve the lack of subjects and covariates sufficiently improves performance to overcome the degradation caused by dimensionality reduction. Although external validity is not necessarily guaranteed, our results suggest that DC-QE is a promising method. With the widespread use of our method, intermediate representations can be published as open data to help researchers find causalities and accumulate a knowledge base.

   
   


4. Go Uchitachimoto, Noriyoshi Sukegawa, Masayuki Kojima, Rina Kagawa, Takashi Oyama, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
   Data collaboration analysis in predicting diabetes from a small amount of health checkup data,
   Scientific Reports, 13, 11820, 2023. [abstract]
   
   

   [abstract] Recent studies showed that machine learning models such as gradient-boosting decision tree (GBDT) can predict diabetes with high accuracy from big data. In this study, we asked whether highly accurate prediction of diabetes is possible even from small data by expanding the amount of data through data collaboration (DC) analysis, a modern framework for integrating and analyzing data accumulated at multiple institutions while ensuring confidentiality. To this end, we focused on data from two institutions: health checkup data of 1502 citizens accumulated in Tsukuba City and health history data of 1399 patients collected at the University of Tsukuba Hospital. When using only the health checkup data, the ROC-AUC and Recall for logistic regression (LR) were 0.858 ± 0.014 and 0.970 ± 0.019, respectively, while those for GBDT were 0.856 ± 0.014 and 0.983 ± 0.016, respectively. When using also the health history data through DC analysis, these values for LR improved to 0.875 ± 0.013 and 0.993 ± 0.009, respectively, while those for GBDT deteriorated because of the low compatibility with a method used for confidential data sharing (although DC analysis brought improvements). Even in a situation where health checkup data of only 324 citizens are available, the ROC-AUC and Recall for LR were 0.767 ± 0.025 and 0.867 ± 0.04, respectively, thanks to DC analysis, indicating an 11% and 12% improvement. Thus, we concluded that the answer to the above question was “Yes” for LR but “No” for GBDT for the data set tested in this study.

   
   


5. Anna Bogdanova, Akira Imakura, Tetsuya Sakurai,
   DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning,
   Human-Centric Intelligent Systems, Vo. 3, 197--210, 2023. [abstract]
   
   

   [abstract] Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend of distributed machine learning designed to limit access to training data for privacy concerns. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explaining party may have a biased view of background data or a partial view of the feature space. As a result, explanations obtained from different participants of distributed machine learning might not be consistent with one another, undermining trust in the product. This paper presents an Explainable Data Collaboration Framework based on a model-agnostic additive feature attribution algorithm (KernelSHAP) and Data Collaboration method of privacy-preserving distributed machine learning. In particular, we present three algorithms for different scenarios of explainability in Data Collaboration and verify their consistency with experiments on open-access datasets. Our results demonstrated a significant (by at least a factor of 1.75) decrease in feature attribution discrepancies among the users of distributed machine learning. The proposed method improves consistency among explanations obtained from different participants, which can enhance trust in the product and enable ethical application in various industries.

   
   


6. Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai,
   Another use of SMOTE for interpretable data collaboration analysis,
   Expert Systems with Applications, Vol.228, 120385, 2023. [abstract]
   
   

   [abstract] Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data. To construct the collaboration representations, each institution generates and shares a shareable anchor dataset and centralizes its intermediate representation. Although, random anchor dataset functions well for DC analysis in general, using an anchor dataset whose distribution is close to that of the raw dataset is expected to improve the recognition performance, particularly for the interpretable DC analysis. Based on an extension of the synthetic minority over-sampling technique (SMOTE), this study proposes an anchor data construction technique to improve the recognition performance without increasing the risk of data leakage. Numerical results demonstrate the efficiency of the proposed SMOTE-based method over the existing anchor data constructions for artificial and real-world datasets. Specifically, the proposed method achieves 6, 4, and 36 percentage point performance improvements regarding NMI, ACC and essential feature selection, respectively, over existing methods for an income dataset. The proposed method provides another use of SMOTE not for imbalanced data classifications but for a key technology of privacy-preserving integrated analysis.

   
   


7. Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe,
   Non-readily identifiable data collaboration analysis for multiple datasets including personal information,
   Information Fusion, Vol.98, 101826, 2023. [abstract]
   
   

   [abstract] Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has attracted considerable research attention. Data confidentiality and cross-institutional communication are critical for the construction of a prediction model using datasets of multiple medical institutions. In such cases, data collaboration (DC) analysis by sharing dimensionality-reduced intermediate representations without iterative cross-institutional communications may be appropriate. Identifiability of the shared data is essential when analyzing data including personal information. In this study, the identifiability of the DC analysis is investigated. The results reveal that the shared intermediate representations are readily identifiable to the original data for supervised learning. This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information. The proposed method solves identifiability concerns based on a random sample permutation, the concept of interpretable DC analysis, and usage of functions that cannot be reconstructed. In numerical experiments on medical datasets, the proposed method exhibits non-readily identifiability while maintaining a high recognition performance of the conventional DC analysis. The proposed method exhibits a nine percentage point improvement regarding the recognition performance over the local analysis that uses only local dataset for a hospital dataset.

   
   


8. Akira Imakura, Tetsuya Sakurai,
   Complex moment-based eigensolver coupled with two Krylov subspaces,
   Journal of Computational and Applied Mathematics, Volume 432, 2023, 115283. [abstract]
   
   

   [abstract] Complex moment-based eigensolvers have been well studied for solving interior eigenvalue problems because of their high parallel efficiency. Recently, as a time-efficient complex moment-based eigensolver, the block SS?CAA method was proposed that is based on the communication-avoiding Arnoldi procedure regarding the high-order complex moments. These complex moment-based eigensolvers, including the block SS-CAA method, usually use with a subspace iteration technique for improving the accuracy. In this paper, we aim to improve the convergence behavior of the block SS?CAA method using a block Arnoldi iteration instead of the subspace iteration. The proposed method is based on a special subspace coupled with two Krylov subspaces: one is for the high-order complex moments, and the other is for the iteration technique. Numerical experiments indicate that the proposed method has a higher convergence rate than the block SS?CAA method and the FEAST eigensolver.

   
   


9. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
   Verified eigenvalue and eigenvector computations using complex moments and the Rayleigh-Ritz procedure for generalized Hermitian eigenvalue problems,
   Journal of Computational and Applied Mathematics, Volume 424, 114994, 2023. [abstract]
   
   

   [abstract] We propose a verified computation method for eigenvalues in a region and the corresponding eigenvectors of generalized Hermitian eigenvalue problems. The proposed method uses complex moments to extract the eigencomponents of interest from a random matrix and uses the Rayleigh?Ritz procedure to project a given eigenvalue problem into a reduced eigenvalue problem. The complex moment is given by contour integral and approximated using numerical quadrature. We split the error in the complex moment into the truncation error of the quadrature and rounding errors and evaluate each. This idea for error evaluation inherits our previous Hankel matrix approach, whereas the proposed method enables verification of eigenvectors and requires half the number of quadrature points for the previous approach to reduce the truncation error to the same order. Moreover, the Rayleigh?Ritz procedure approach forms a transformation matrix that enables verification of the eigenvectors. Numerical experiments show that the proposed method is faster than previous methods while maintaining verification performance and works even for nearly singular matrix pencils and in the presence of multiple and nearly multiple eigenvalues.

   
   


10. 【日本医療情報学会 第6回学術論文賞】 Akira Imakura, Ryoya Tsunoda, Rina Kagawa, Kunihiro Yamagata, Tetsuya Sakurai,
    DC-COX: data collaboration Cox proportional hazards model for privacy-preserving survival analysis on multiple parties,
    Journal of Biomedical Informatics, Volume 137, 104264, 2023. [abstract]
    
    

    [abstract] The demand for the privacy-preserving survival analysis of medical data integrated from multiple institutions or countries has been increased. However, sharing the original medical data is difficult because of privacy concerns, and even if it could be achieved, we have to pay huge costs for cross-institutional or cross-border communications. To tackle these difficulties of privacy-preserving survival analysis on multiple parties, this study proposes a novel data collaboration Cox proportional hazards (DC-COX) model based on a data collaboration framework for horizontally and vertically partitioned data. By integrating dimensionality-reduced intermediate representations instead of the original data, DC-COX obtains a privacy-preserving survival analysis without iterative cross-institutional communications or huge computational costs. DC-COX enables each local party to obtain an approximation of the maximum likelihood model parameter, the corresponding statistic, such as the p-value, and survival curves for subgroups. Based on a bootstrap technique, we introduce a dimensionality reduction method to improve the efficiency of DC-COX. Numerical experiments demonstrate that DC-COX can compute a model parameter and the corresponding statistics with higher performance than the local party analysis. Particularly, DC-COX demonstrates outstanding performance in essential feature selection based on the p-value compared with the existing methods including the federated learning-based method.

    
    


11. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Complex moment-based methods for differential eigenvalue problems,
    Numerical Algorithms, Volume 92, 693-721, 2023. [abstract]
    
    

    [abstract] This paper considers computing partial eigenpairs of differential eigenvalue problems (DEPs) such that eigenvalues are in a certain region on the complex plane. Recently, based on a “solve-then-discretize” paradigm, an operator analogue of the FEAST method has been proposed for DEPs without discretization of the coefficient operators. Compared to conventional “discretize-then-solve” approaches that discretize the operators and solve the resulting matrix problem, the operator analogue of FEAST exhibits much higher accuracy; however, it involves solving a large number of ordinary differential equations (ODEs). In this paper, to reduce the computational costs, we propose operation analogues of Sakurai?Sugiura-type complex moment-based eigensolvers for DEPs using higher-order complex moments and analyze the error bound of the proposed methods. We show that the number of ODEs to be solved can be reduced by a factor of the degree of complex moments without degrading accuracy, which is verified by numerical results. Numerical results demonstrate that the proposed methods are over five times faster compared with the operator analogue of FEAST for several DEPs while maintaining almost the same high accuracy. This study is expected to promote the “solve-then-discretize” paradigm for solving DEPs and contribute to faster and more accurate solutions in real-world applications.

    
    


12. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    LSEC: Large-scale spectral ensemble clustering,
    Intelligent Data Analysis, Vol. 27, No. 1, pp. 59-77, 2023. [abstract]
    
    

    [abstract] A fundamental problem in machine learning is ensemble clustering, that is, combining multiple base clusterings to obtain improved clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks owing to efficiency bottlenecks. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to balance efficiency and effectiveness. In LSEC, a large-scale spectral clustering-based efficient ensemble generation framework is designed to generate various base clusterings with low computational complexity. Thereafter, all the base clusterings are combined using a bipartite graph partition-based consensus function to obtain improved consensus clustering results. The LSEC method achieves a lower computational complexity than most existing ensemble clustering methods. Experiments conducted on ten large-scale datasets demonstrate the efficiency and effectiveness of the LSEC method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li-Hongmin/MyPaperWithCode.

    
    


13. Akihiro Mizoguchi, Akira Imakura, Tetsuya Sakurai,
    Application of data collaboration analysis to distributed data with misaligned features,
    Informatics in Medicine Unlocked, Vol.32, 2022, 101013. [abstract]
    
    

    [abstract] The types of metabolites measured in metabolomics studies vary depending on many factors, including differences in methods. Centralizing the distributed raw data is also often difficult due to confidentiality issues. These difficulties prevent the integrated analysis of metabolomic data from multiple studies. In this study, we extend the data collaboration analysis, an integrated data analysis method, by sharing dimensionality-reduced intermediate representations instead of the raw data to allow it to be applied to distributed data where the samples are completely different, and features are partially common. We then evaluated the improvement in performance using non-common features in the data collaboration analysis. To perform this evaluation, we created the four artificial datasets and the two datasets generated from metabolomics public data where samples are completely different and features are partially common. For each of these datasets, we compared the classification performance including area under the curve in the receiver operating characteristic curve (ROC-AUC) with the following three cases: (i) a case where only local data were used for training, (ii) the data collaboration analysis with only the common features of the distributed datasets, and (iii) the data collaboration analysis with all the features including non-common features. In most cases, the data collaboration analysis using all features demonstrated better results compared to the data collaboration analysis only using common features (by 1.3-4.8 points ROC-AUC for each dataset on average) or that trained on only one of the datasets (by 1.8-2.9 points ROC-AUC for each dataset on average). It was confirmed that the data collaboration analysis could integrate and analyze distributed data where samples are completely different and features are partially common, which can improve the classification accuracy in machine learning without sharing the raw data.

    
    


14. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Divide-and-conquer based Large-Scale Spectral Clustering,
    Neurocomputing, Vol. 501, pp.664-678, 2022. [abstract]
    
    

    [abstract] Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness. In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities. Then clustering results can be computed quickly through a bipartite graph partition process. The proposed method achieves the lower computational complexity than most existing large-scale spectral clustering methods. Experimental results on ten large-scale datasets have demonstrated the efficiency and effectiveness of the proposed method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li-Hongmin/MyPaperWithCode.

    
    


15. Kensuke Aihara, Akira Imakura, Keiichi Morikuni,
    Cross-interactive residual smoothing for global and block Lanczos-type solvers for linear systems with multiple right-hand sides,
    SIAM Journal on Matrix Analysis and Applications, Vol. 43, No. 3, pp. 1308--1330, 2022. [abstract]
    
    

    [abstract] Global and block Krylov subspace methods are efficient iterative solvers for large sparse linear systems with multiple right-hand sides. However, global or block Lanczos-type solvers often exhibit large oscillations in the residual norms and may have a large residual gap relating to the loss of attainable accuracy of the approximations. Conventional residual smoothing schemes suppress these oscillations but cannot improve the attainable accuracy, whereas a recent residual smoothing scheme enables the improvement of the attainable accuracy for single right-hand-side Lanczos-type solvers. The underlying concept of this scheme is that the primary and smoothed sequences of the approximations and residuals influence one another, thereby avoiding the severe propagation of rounding errors. In the present study, we extend this cross-interactive residual smoothing to the case of solving linear systems with multiple right-hand sides. The resulting smoothed methods can reduce the residual gap with a low additional cost compared to their original counterparts. We demonstrate the effectiveness of the proposed approach through rounding error analysis and numerical experiments.

    
    


16. Sarah Huber, Yasunori Futamura, Martin Galgon, Akira Imakura, Bruno Lang, Tetsuya Sakurai,
    Flexible subspace iteration with moments for an effective contour integration-based eigensolver,
    Numerical Linear Algebra with Applications, Vol. 29, Issue 6, e2447, 2022. [abstract]
    
    

    [abstract] Contour integration schemes are a valuable tool for the solution of difficult interior eigenvalue problems. However, the solution of many large linear systems with multiple right hand sides may prove a prohibitive computational expense. The number of right hand sides, and thus, computational cost may be reduced if the projected subspace is created using multiple moments. In this work, we explore heuristics for the choice and application of moments with respect to various other important parameters in a contour integration scheme. We provide evidence for the expected performance, accuracy, and robustness of various schemes, showing that good heuristic choices can provide a scheme featuring good properties in all three of these measures.

    
    


17. Meng Huang, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma,
    Journal of Biomedical Informatics, Vol. 128, 2022, 104049. [abstract]
    
    

    [abstract] Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and kidney chromophobe (KICH). Gene signature identification plays an important role in the precise classification of RCC subtypes and personalized treatment. However, most of the existing gene selection methods focus on statically selecting the same informative genes for each subtype, and fail to consider the heterogeneity of patients which causes pattern differences in each subtype. In this work, to explore different informative gene subsets for each subtype, we propose a novel gene selection method, named sequential reinforcement active feature learning (SRAFL), which dynamically acquire the different genes in each sample to identify the different gene signatures for each subtype. The proposed SRAFL method combines the cancer subtype classifier with the reinforcement learning (RL) agent, which sequentially select the active genes in each sample from three mixed RCC subtypes in a cost-sensitive manner. Moreover, the module-based gene filtering is run before gene selection to filter the redundant genes. We mainly evaluate the proposed SRAFL method based on mRNA and long non-coding RNA (lncRNA) expression profiles of RCC datasets from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that the proposed method can automatically identify different gene signatures for different subtypes to accurately classify RCC subtypes. More importantly, we here for the first time show the proposed SRAFL method can consider the heterogeneity of samples to select different gene signatures for different RCC subtypes, which shows more potential for the precision-based RCC care in the future.

    
    


18. Akira Imakura, Hiroaki Inaba, Yukihiko Okada, Tetsuya Sakurai,
    Interpretable collaborative data analysis on distributed data,
    Expert Systems with Applications, Vol. 177, 114891, 2021. [abstract]
    
    

    [abstract] This paper proposes an interpretable non-model sharing collaborative data analysis method as a federated learning system, which is an emerging technology for analyzing distributed data. Analyzing distributed data is essential in many applications, such as medicine, finance, and manufacturing, due to privacy and confidentiality concerns. In addition, interpretability of the obtained model plays an important role in the practical applications of federated learning systems. By centralizing intermediate representations, which are individually constructed by each party, the proposed method obtains an interpretable model, achieving collaborative analysis without revealing the individual data and learning models distributed between local parties. Numerical experiments indicate that the proposed method achieves better recognition performance than individual analysis and comparable performance to centralized analysis for both artificial and real-world problems.

    
    


19. Yuta Takahashi, Han-ten Chang, Akie Nakai, Rina Kagawa, Hiroyasu Ando, Akira Imakura, Yukihiko Okada, Hideo Tsurushima, Kenji Suzuki, Tetsuya Sakurai,
    Decentralized learning with virtual patients for medical diagnosis of diabetes,
    SN Computer Science, Vol. 2, Issue 4, Article 239, 2021. [abstract]
    
    

    [abstract] Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance.

    
    


20. Akira Imakura,
    An improvement of multigrid methods using multiple grids on each layer for parallel computing,
    Journal of Mathematical Research with Applications, Vol.41, No.1, pp.87-98, 2021. [abstract]
    
    

    [abstract] Multigrid methods are widely used and well studied for linear solvers and preconditioners of Krylov subspace methods. The multigrid method is one of the most powerful approaches for solving large scale linear systems; however, it may show low parallel efficiency on coarse grids. There are several kinds of research on this issue. In this paper, we intend to overcome this difficulty by proposing a novel multigrid algorithm that has multiple grids on each layer. Numerical results indicate that the proposed method shows a better convergence rate compared with the existing multigrid method.

    
    


21. Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai,
    An oversampling framework for imbalanced classification based on Laplacian eigenmaps,
    Neurocomputing, Vol. 399, 107-116, 2020. [abstract]
    
    

    [abstract] Imbalanced classification is a challenging problem in machine learning and data mining. Oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE), generate synthetic data to achieve data balance for imbalanced classification. However, such kind of oversampling methods generates unnecessary noise when the data are not well separated. On the other hand, there are many applications with inadequate training data and vast testing data, making the imbalanced classification much more challenging. In this paper, we propose a novel oversampling framework to achieve the following two objectives. (1) Improving the classification results of the SMOTE based oversampling methods; (2) Making the SMOTE based oversampling methods applicable when the training data are inadequate. The proposed framework utilizes the Laplacian eigenmaps to find an optimal dimensional space, where the data are well separated and the generation of noise by SMOTE based oversampling methods can be avoided. The construction of graph Laplacian not only explores the useful information from the unlabeled testing data to facilitate imbalanced learning, but also makes the learning process incremental. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed framework.

    
    


22. Xian-Ming Gu, Ting-Zhu Huang, Bruno Carpentieri, Akira Imakura, Ke Zhang, Lei Du,
    Efficient variants of the CMRH method for solving a sequence of multi-shifted non-Hermitian linear systems simultaneously,
    Journal of Computational and Applied Mathematics, Vol. 375, 112788, 2020. [abstract]
    
    

    [abstract] Multi-shifted linear systems with non-Hermitian coefficient matrices arise in numerical solutions of time-dependent partial/fractional differential equations (PDEs/FDEs), in control theory, PageRank problems, and other research fields. We derive efficient variants of the restarted Changing Minimal Residual method based on the cost-effective Hessenberg procedure (CMRH) for this problem class. Then, we introduce a flexible variant of the algorithm that allows to use variable preconditioning at each iteration to further accelerate the convergence of shifted CMRH. We analyse the performance of the new class of methods in the numerical solution of PDEs and FDEs, also against other multi-shifted Krylov subspace methods.

    
    


23. Momo Matsuda, Keiichi Morikuni, Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
    Multiclass spectral feature scaling method for dimensionality reduction,
    Intelligent Data Analysis, Vol. 24, No. 6, pp. 1273-1287, 2020. [abstract]
    
    

    [abstract] Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that are used to modify the features. Specifically, we reformulate the Laplacian eigenproblem of the spectral clustering as an eigenproblem of a linear matrix pencil whose eigenvector has the scaling factors. Numerical experiments show that the proposed method outperforms well-established supervised dimensionality reduction methods for toy problems with more samples than features and real-world problems with more features than samples.

    
    


24. Akira Imakura, Tetsuya Sakurai,
    Data Collaboration Analysis Framework Using Centralization of Individual Intermediate Representations for Distributed Data Sets,
    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 6, Issue 2, 04020018, 2020. [abstract]
    
    

    [abstract] This paper proposes a data collaboration analysis framework for distributed data sets. The proposed framework involves centralized machine learning while the original data sets and models remain distributed over a number of institutions. Recently, data has become larger and more distributed with decreasing costs of data collection. Centralizing distributed data sets and analyzing them as one data set can allow for novel insights and attainment of higher prediction performance than that of analyzing distributed data sets individually. However, it is generally difficult to centralize the original data sets because of a large data size or privacy concerns. This paper proposes a data collaboration analysis framework that does not involve sharing the original data sets to circumvent these difficulties. The proposed framework only centralizes intermediate representations constructed individually rather than the original data set. The proposed framework does not use privacy-preserving computations or model centralization. In addition, this paper proposes a practical algorithm within the framework. Numerical experiments reveal that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.

    
    


25. Yasunori Futamura, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Spectral anomaly detection in large graphs using a complex moment-based eigenvalue solver,
    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 6, Issue 2, 04020010, 2020. [abstract]
    
    

    [abstract] Detecting anomalies is an important and challenging task for many applications. In recent years, spectral methods have been proposed to detect anomalous subgraphs embedded into a background graph using eigenvectors corresponding to some of the largest positive eigenvalues of the graph’s modularity matrix. The spectral methods use the standard Lanczos-type eigenvalue solver to compute these exterior eigenpairs. However, eigenvectors with interior eigenvalues could also indicate the existence of anomalous subgraphs. In this study, we propose an efficient method using a complex moment-based eigenvalue solver, which can efficiently search anomalous subgraphs related to eigenvectors with both exterior and interior eigenvalues. Experimental results show the potential of the proposed method.

    
    


26. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Verified partial eigenvalue computations using contour integrals for Hermitian generalized eigenproblems,
    Journal of Computational and Applied Mathematics, Vol. 369, 112543, 2020. [abstract]
    
    

    [abstract] We propose a verified computation method for partial eigenvalues of a Hermitian generalized eigenproblem. The block Sakurai-Sugiura Hankel method, a contour integral-type eigensolver, can reduce a given eigenproblem into a generalized eigenproblem of block Hankel matrices whose entries consist of complex moments. In this study, we evaluate all errors in computing the complex moments. We derive a truncation error bound of the quadrature. Then, we take numerical errors of the quadrature into account and rigorously enclose the entries of the block Hankel matrices. Each quadrature point gives rise to a linear system, and its structure enables us to develop an efficient technique to verify the approximate solution. Numerical experiments show that the proposed method outperforms a standard method and infer that the proposed method is potentially efficient in parallel.

    
    


27. Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis,
    International Journal of Machine Learning and Computing (IJMLC), Vol.10, No.4, pp.527-533, 2020. [abstract]
    
    

    [abstract] Novelty detection represents the detection of anomalous data based on a training set consisting of only the normal data. In this study, we propose a new probabilistic approach for novelty detection to effectively detect anomalous data, particularly for the case of multimodal training dataset. Our method is inspired by the Least-Squares Probabilistic Classifier (LSPC), which is an efficient multi-class classification method. Numerical experimental results based on multimodal datasets show that the proposed method outperforms the related methods.

    
    


28. Andreas Alvermann, Achim Basermann, Hans-Joachim Bungartz, Christian Carbogno, Dominik Ernst, Holger Fehske, Yasunori Futamura, Martin Galgon, Georg Hager, Sarah Huber, Thomas Huckle, Akihiro Ida, Akira Imakura, Masatoshi Kawai, Simone Kocher, Moritz Kreutzer, Pavel Kus, Bruno Lang, Hermann Lederer, Valeriy Manin, Andreas Marek, Kengo Nakajima, Lydia Nemec, Karsten Reuter, Michael Rippl, Melven Rohrig-Zollner, Tetsuya Sakurai, Matthias Scheffler, Christoph Scheurer, Faisal Shahzad, Danilo Simoes Brambila, Jonas Thies, Gerhard Wellein,
    Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects,
    Japan Journal of Industrial and Applied Mathematics, Vol.36, Issue 2, pp. 699-717, 2019. [abstract]
    
    

    [abstract] We first briefly report on the status and recent achievements of the ELPA-AEO (Eigen value Solvers for Petaflop Applications-Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained.

    
    


29. Akira Imakura,
    Minimal residual-like condition with collinearity for shifted Krylov subspace methods,
    Japan Journal of Industrial and Applied Mathematics, Vol.36, Issue 2, pp. 643-661, 2019. [abstract]
    
    

    [abstract] In this paper, we consider shifted Krylov subspace methods for solving shifted linear systems. In such methods, the collinearity of the residual vectors plays a very important role. The minimal residual-like condition with collinearity for the shifted Krylov subspace methods was first proposed for the restarted shifted GMRES method by Frommer and Glassner in 1998, and it has been used for several shifted Krylov subspace methods, such as the shifted BiCGSTAB(l) and shifted IDR(s) methods. In this paper, we propose a novel minimal residual-like condition with collinearity for shifted Krylov subspace methods. Numerical experiments indicate that the proposed condition shows a better convergence behavior than the traditional condition.

    
    


30. Akira Imakura, Yusaku Yamamoto,
    Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product,
    Japan Journal of Industrial and Applied Mathematics, Vol.36, Issue 2, pp. 619-641, 2019. [abstract]
    
    

    [abstract] The modified Gram-Schmidt (MGS) orthogonalization is one of the most well-used algorithms for computing the thin QR factorization. MGS can be straightforwardly extended to a non-standard inner product with respect to a symmetric positive definite matrix A. For the thin QR factorization of an m×n matrix with the non-standard inner product, a naive implementation of MGS requires 2n matrix-vector multiplications (MV) with respect to A. In this paper, we propose n-MV implementations: a high accuracy (HA) type and a high performance type, of MGS. We also provide error bounds of the HA-type implementation. Numerical experiments and analysis indicate that the proposed implementations have competitive advantages over the naive implementation in terms of both computational cost and accuracy.

    
    


31. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Toshiyuki Imamura,
    Scalable Eigen-Analysis Engine for Large-Scale Eigenvalue Problems,
    In: Sato M. (eds) Advanced Software Technologies for Post-Peta Scale Computing, Springer, Singapore, pp. 37-57, 2019. [abstract]
    
    

    [abstract] Our project aims to develop a massively parallel Eigen-Supercomputing Engine for post-petascale systems. Our Eigen-Engines are based on newly designed algorithms that are suited to the hierarchical architecture in post-petascale systems and show very good performance on petascale systems including K computer. In this paper, we introduce our Eigen-Supercomputing Engines: z-Pares and EigenExa and their performance.

    
    


32. 関川 悠太, 二村 保徳, 今倉 暁, 櫻井 鉄也,
    複数右辺ベクトルを持つシフト線形方程式に対する多項式前処理の有効性の検証,
    日本応用数理学会論文誌, Vol. 29, No. 1, pp. 141-164, 2019. [概要]
    
    

    [概要] 複数右辺ベクトルをもつシフト線形方程式の解法として，シフト不変性を利用したBlock Krylov部分空間法に対する多項式前処理について考える． 右辺ベクトルが1本の場合，一般に多項式前処理は，計算時間の観点からは必ずしも有効とはいえない． 本論文では，多項式前処理が多くの右辺ベクトルをもつシフト線形方程式に対して，計算時間の観点から有効となることを示す．

    
    


33. Lei Du, Akira Imakura, Tetsuya Sakurai,
    Simultaneous Band Reduction of Two Symmetric Matrices,
    Computers and Mathematics with Applications, Vol. 77, Issue 8, pp. 2207-2220, 2019. [abstract]
    
    

    [abstract] In this paper, we consider simultaneous band reduction of two dense symmetric matrices by congruent transformations. The ideas of simultaneous tridiagonalization are generalized to propose an efficient algorithm for the simultaneous band reduction. In contrast to the algorithms of simultaneous tridiagonalization which are mainly based on matrix-vector operations, the proposed algorithm of simultaneous band reduction has the advantage that matrix-matrix operations can be fully used to achieve better performance on modern computer architecture. Numerical results are presented to illustrate the effectiveness of our proposed algorithm.

    
    


34. Takahiro Yano, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Performance evaluation of the Sakurai-Sugiura method with a block Krylov subspace linear solver for large dense Hermitian-definite generalized eigenvalue problems,
    JSIAM Letters, Vol. 10, pp. 77-80, 2018. [abstract]
    
    

    [abstract] Contour-integral based eigensolvers have been proposed for efficiently exploiting the performance of massively parallel computational environments. In the algorithms of these methods, inner linear systems need to be solved and its calculation time becomes the most time-consuming part for large-scale problems. In this paper, we consider applying a contour-integral based method to a large dense problem in conjunction with a block Krylov subspace method as an inner linear solver. Comparison of parallel performance with the contour-integral based method with a direct linear solver and a ScaLAPACK's eigensolver is shown using matrices from a practical application.

    
    


35. Haruka Yamada, Akira Imakura, Tetsuya Sakurai,
    Cost-efficient cutoff method for tensor renormalization group with randomized singular value decomposition,
    JSIAM Letters, Vol. 10, pp. 61-64, 2018. [abstract]
    
    

    [abstract] Tensor renormalization group (TRG) is a coarse-graining algorithm for approximating the partition function using a tensor network in the field of elementary particle physics. Although the computational cost of TRG can be reduced using a randomized singular value decomposition, its computation time is still large. In this paper, we propose a cost-efficient cutoff method for calculating TRG by truncating small tensor elements. Numerical experiments showed that the proposed method is faster than the conventional one without degrading accuracy.

    
    


36. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Shigeru Tsukamoto, Tomoya Ono,
    Contour integral method for obtaining the self-energy matrices of electrodes in electron transport calculations,
    PHYSICAL REVIEW B, 97, 195449, 2018. [abstract]
    
    

    [abstract] We propose an efficient computational method for evaluating the self-energy matrices of electrodes to study ballistic electron transport properties in nanoscale systems. To reduce the high computational cost incurred in large systems, a contour integral eigensolver based on the Sakurai-Sugiura method combined with the shifted biconjugate gradient method is developed to solve an exponential-type eigenvalue problem for complex wave vectors. A remarkable feature of the proposed algorithm is that the numerical procedure is very similar to that of conventional band structure calculations. We implement the developed method in the framework of the real-space higher-order finite-difference scheme with nonlocal pseudopotentials. Numerical tests for a wide variety of materials validate the robustness, accuracy, and efficiency of the proposed method. As an illustration of the method, we present the electron transport property of the freestanding silicene with the line defect originating from the reversed buckled phases.

    
    


37. Akira Imakura, Tomohiro Sogabe, Shao-Liang Zhang,
    A Look-Back-Type Restart for the Restarted Krylov Subspace Methods for Solving Non-Hermitian Linear Systems,
    Japan Journal of Industrial and Applied Mathematics, Vol. 35, Issue 2, pp. 835-859, 2018. [abstract]
    
    

    [abstract] In this paper, we investigate the restarted Krylov subspace methods, as typified by the GMRES(m) method and the FOM(m) method, for solving non-Hermitian linear systems. We have recently focused on the restart of the GMRES(m) method and proposed the extension of the GMRES(m) method based on the error equations. The main purpose of this paper is to apply the extension to other restarted Krylov subspace methods, and propose a specific restart technique for the restarted Krylov subspace method. The specific restart technique is named as the Look-Back-type restart, and is based on an implicit residual polynomial reconstruction via the initial guess. The comparison analysis based on the residual polynomials and some numerical experiments indicate that the Look-Back-type restart achieves more efficient convergence results than the traditional restarted Krylov subspace methods.

    
    


38. Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Hirotada Okawa, Akira Harada, Kohsuke Sumiyoshi, Shoichi Yamada, Hideo Matsufuru, Akira Imakura,
    Simulations of Core-collapse Supernovae in Spatial Axisymmetry with Full Boltzmann Neutrino Transport,
    The Astrophysical Journal, Vol 854, No 2, 13pp. 2018. [abstract]
    
    

    [abstract] We present the first results of our spatially axisymmetric core-collapse supernova simulations with full Boltzmann neutrino transport, which amount to a time-dependent five-dimensional (two in space and three in momentum space) problem. Special relativistic effects are fully taken into account with a two-energy-grid technique. We performed two simulations for a progenitor of 11.2 M, employing different nuclear equations of state (EOSs): Lattimer and Swesty's EOS with the incompressibility of K = 220 MeV (LS EOS) and Furusawa's EOS based on the relativistic mean field theory with the TM1 parameter set (FS EOS). In the LS EOS, the shock wave reaches ~700 km at 300 ms after bounce and is still expanding, whereas in the FS EOS it stalled at ~200 km and has started to recede by the same time. This seems to be due to more vigorous turbulent motions in the former during the entire postbounce phase, which leads to higher neutrino-heating efficiency in the neutrino-driven convection. We also look into the neutrino distributions in momentum space, which is the advantage of the Boltzmann transport over other approximate methods. We find nonaxisymmetric angular distributions with respect to the local radial direction, which also generate off-diagonal components of the Eddington tensor. We find that the rθ component reaches ~10% of the dominant rr component and, more importantly, it dictates the evolution of lateral neutrino fluxes, dominating over the θθ component, in the semitransparent region. These data will be useful to further test and possibly improve the prescriptions used in the approximate methods.

    
    


39. Ryosuke Arai, Akira Imakura, Tetsuya Sakurai,
    An improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization for deep neural networks,
    International Journal of Machine Learning and Computing (IJMLC), Vol. 8, No. 3, pp. 191-197, 2018. [abstract]
    
    

    [abstract] Backpropagation (BP) has been widely used as a de-facto standard algorithm to compute weights for deep neural networks (DNNs). The BP method is based on a stochastic gradient descent method using the derivatives of an objective function. As another approach, an alternating optimization method using linear and nonlinear semi-nonnegative matrix factorizations (semi-NMFs) has been proposed recently for computing weight matrices of fully-connected DNNs without bias vectors and regularization. In this paper, we proposed an improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization. Experimental results indicate that the proposed method shows higher recognition performance than the nonlinear semi-NMF based method and competitive advantages to the conventional BP method.

    
    


40. Akira Imakura, Tetsuya Sakurai,
    Block SS--CAA: A complex moment-based parallel nonlinear eigensolver using the block communication-avoiding Arnoldi procedure,
    Parallel Computing, Vol. 74, pp. 34-48, 2018. [abstract]
    
    

    [abstract] Complex moment-based parallel eigensolvers have been actively studied owing to their high parallel efficiency. In this paper, we propose a block SS-CAA method, which is a complex moment-based parallel nonlinear eigensolver that makes use of the block communication-avoiding Arnoldi procedure. Numerical experiments indicate that the proposed method has higher performance compared with traditional complex moment-based nonlinear eigensolvers, i.e., the block SS-Hankel and Beyn methods.

    
    


41. Akira Imakura, Yuto Inoue, Tetsuya Sakurai, Yasunori Futamura,
    Parallel implementation of the nonlinear semi-NMF based alternating optimization method for deep neural networks,
    Neural Processing Letters, Vol 47, Issue 3, pp. 815-827, 2018. [abstract]
    
    

    [abstract] For computing weights of deep neural networks (DNNs), the backpropagation (BP) method has been widely used as a de-facto standard algorithm. Since the BP method is based on a stochastic gradient descent method using derivatives of objective functions, the BP method has some difficulties finding appropriate parameters such as learning rate. As another approach for computing weight matrices, we recently proposed an alternating optimization method using linear and nonlinear semi-nonnegative matrix factorizations (semi-NMFs). In this paper, we propose a parallel implementation of the nonlinear semi-NMF based method. The experimental results show that our nonlinear semi-NMF based method and its parallel implementation have competitive advantages to the conventional DNNs with the BP method.

    
    


42. Yasunori Futamura, Takahiro Yano, Akira Imakura, Tetsuya Sakurai,
    A real-valued block conjugate gradient type method for solving complex symmetric linear systems with multiple right-hand sides,
    Applications of Mathematics, Vol. 62, Issue 4, pp. 333-355, 2017. [abstract]
    
    

    [abstract] We consider solving complex symmetric linear systems with multiple right-hand sides. We assume that the coefficient matrix has indefinite real part and positive definite imaginary part. We propose a new block conjugate gradient type method based on the Schur complement of a certain 2-by-2 real block form. The algorithm of the proposed method consists of building blocks that involve only real arithmetic with real symmetric matrices of the original size. We also present the convergence property of the proposed method and an efficient algorithmic implementation. In numerical experiments, we compare our method to a complex-valued direct solver, and a preconditioned and nonpreconditioned block Krylov method that uses complex arithmetic.

    
    


43. Hongjia Chen, Akira Imakura, Tetsuya Sakurai,
    Improving backward stability of Sakurai-Sugiura method with balancing technique in polynomial eigenvalue problem,
    Applications of Mathematics, Vol. 62, Issue 4, pp. 357-375, 2017. [abstract]
    
    

    [abstract] One of the most efficient methods for solving the polynomial eigenvalue problem (PEP) is the Sakurai-Sugiura method with Rayleigh-Ritz projection (SS-RR), which finds the eigenvalues contained in a certain domain using the contour integral. The SS-RR method converts the original PEP to a small projected PEP using the Rayleigh-Ritz projection. However, the SS-RR method suffers from backward instability when the norms of the coefficient matrices of the projected PEP vary widely. To improve the backward stability of the SS-RR method, we combine it with a balancing technique for solving a small projected PEP. We then analyze the backward stability of the SS-RR method. Several numerical examples demonstrate that the SS-RR method with the balancing technique reduces the backward error of eigenpairs of PEP.

    
    


44. Akira Imakura, Tetsuya Sakurai,
    Block Krylov-type complex moment-based eigensolvers for solving generalized eigenvalue problems,
    Numerical Algorithms, Volume 75, Issue 2, pp 413-433, 2017. [abstract]
    
    

    [abstract] Complex moment-based eigensolvers for solving interior eigenvalue problems have been studied because of their high parallel efficiency. Recently, we proposed the block Arnoldi-type complex moment-based eigensolver without a low-rank approximation. A low-rank approximation plays a very important role in reducing computational cost and stabilizing accuracy in complex moment-based eigensolvers. In this paper, we develop the method and propose block Krylov-type complex moment-based eigensolvers with a low-rank approximation. Numerical experiments indicate that the proposed methods have higher performance than the block SS--RR method, which is one of the most typical complex moment-based eigensolvers.

    
    


45. Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Kohsuke Sumiyoshi, Shoichi Yamada, Hideo Matsufuru, Akira Imakura,
    Three-dimensional Boltzmann-Hydro code for core-collapse in massive stars II. The Implementation of moving-mesh for neutron star kicks,
    The Astrophysical Journal Supplement, Volume 229, Number 2, 2017, 14pp. [abstract]
    
    

    [abstract] We present a newly developed moving-mesh technique for the multi-dimensional Boltzmann-Hydro code for the simulation of core-collapse supernovae (CCSNe). What makes this technique different from others is the fact that it treats not only hydrodynamics but also neutrino transfer in the language of the 3 + 1 formalism of general relativity (GR), making use of the shift vector to specify the time evolution of the coordinate system. This means that the transport part of our code is essentially general relativistic, although in this paper it is applied only to the moving curvilinear coordinates in the flat Minknowski spacetime, since the gravity part is still Newtonian. The numerical aspect of the implementation is also described in detail. Employing the axisymmetric two-dimensional version of the code, we conduct two test computations: oscillations and runaways of proto-neutron star (PNS). We show that our new method works fine, tracking the motions of PNS correctly. We believe that this is a major advancement toward the realistic simulation of CCSNe.

    
    


46. Hongjia Chen, Yasuyuki Maeda, Akira Imakura, Tetsuya Sakurai, Francoise Tisseur,
    Improving the numerical stability of the Sakurai-Sugiura method for quadratic eigenvalue problems,
    JSIAM Letters, Vol. 9, 2017, pp.17-20. [abstract]
    
    

    [abstract] The Sakurai-Sugiura method with Rayleigh-Ritz projection (SS-RR method) finds the eigenvalues in a certain domain of the complex plane of large quadratic eigenvalue problems (QEPs). The SS-RR method can suffer from numerical instability when the coefficient matrices of the projected QEP vary widely in norm. To improve the numerical stability of the SS-RR method, we combine it with a numerically stable eigensolver for the small projected QEP. We analyze the backward stability of the proposed method and show, through numerical experiments, that it computes eigenpairs with backward errors that are smaller than those computed by the SS-RR method.

    
    


47. Akira Imakura, Lei Du, Tetsuya Sakurai,
    Relationships among contour integral-based methods for solving generalized eigenvalue problems,
    Japan Journal of Industrial and Applied Mathematics, Vol.33, Issue 3, 2016, pp.721-750. [abstract]
    
    

    [abstract] Recently, contour integral-based methods have been actively studied for solving interior eigenvalue problems that find all eigenvalues located in a certain region and their corresponding eigenvectors. In this paper, we reconsider the algorithms of the five typical contour integral-based eigensolvers from the viewpoint of projection methods, and then map the relationships among these methods. From the analysis, we conclude that all contour integral-based eigensolvers can be regarded as projection methods and can be categorized based on their subspace used, the type of projection and the problem to which they are applied implicitly.

    
    


48. 齋藤周作, 多田野寛人, 今倉暁,
    Shifted Block BiCGSTAB(l) 法の構築とその高精度化,
    日本応用数理学会論文誌, Vol.26, No.3, 2016, pp.318-352. [概要]
    
    

    [abstract] 本研究では, 複数右辺ベクトルを持つシフト線形方程式を高速に解く手法として，Shifted Block BiCGSTAB(l) 法を構築する． また，得られる近似解の精度について解析し，より高精度な手法を提案する． 数値実験により，提案法ではShifted BiCGSTAB(?)法を各右辺に適用する場合よりも多少精度は劣るものの，少ない計算時間で問題を解くことができた．

    
    


49. Akira Imakura, Ren-Cang Li, Shao-Liang Zhang,
    Locally Optimal and Heavy Ball GMRES Methods,
    Japan Journal of Industrial and Applied Mathematics, Vol. 33, Issue 2, 2016, pp. 471-499. [abstract]
    
    

    [abstract] The restarted GMRES (REGMRES) is one of the well used Krylov subspace methods for solving linear systems. However, the price to pay for the restart usually is slower speed of convergence. In this paper, we draw inspirations from the locally optimal CG and the heavy ball methods in optimization to propose two variants of the restarted GMRES that can overcome the slow convergence. Compared to various existing hybrid GMRES which are also designed to speed up REGMRES and which usually require eigen-region estimations, our variants preserve the appealing feature of GMRES and REGMRES?their simplicity. Numerical tests on real data are presented to demonstrate the superiority of the new methods over REGMRES and its variants.

    
    


50. Tetsuya Hasegawa, Akira Imakura, Tetsuya Sakurai,
    Recovering from accuracy deterioration in the contour integral-based eigensolver,
    JSIAM Letters, Vol. 8, 2016, pp.1-4. [abstract]
    
    

    [abstract] We consider a contour integral-based eigensolver that finds eigenvalues in a given domain and the corresponding eigenvectors of the generalized eigenvalue problem. In the contour integral-based eigensolver, quadrature points are placed in the complex plane in order to approximate the contour integral. When eigenvalues exist near a quadrature point, the accuracy of other eigenvalues is deteriorated. We herein propose a method by which to recover the accuracy of the eigenpairs when eigenvalues exist near a quadrature point. A numerical experiment is conducted in order to verify that the proposed method is efficient.

    
    


51. Akira Imakura, Lei Du, Tetsuya Sakurai,
    Error bounds of Rayleigh--Ritz type contour integral-based eigensolver for solving generalized eigenvalue problems,
    Numerical Algorithms, Vol. 71, Issue 1, 2016, pp.103-120. [abstract]
    
    

    [abstract] We investigate contour integral-based eigensolvers for computing all eigenvalues located in a certain region and their corresponding eigenvectors. In this paper, we focus on a Rayleigh--Ritz type method and analyze its error bounds. From the results of our analysis, we conclude that the Rayleigh--Ritz type contour integral-based eigensolver with sufficient subspace size can achieve high accuracy for target eigenpairs even if some eigenvalues exist outside but near the region.

    
    


52. Yasuyuki Maeda, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Filter analysis for the stochastic estimation of eigenvalue counts,
    JSIAM Letters, Vol. 7, 2015, pp.53-56. [abstract]
    
    

    [abstract] To estimate the number of eigenvalues of a Hermitian matrix that are located in a given interval, existing methods include polynomial filtering and rational filtering. Both filtering approaches are based on stochastic approximations for matrix trace. In this paper, we analyze a rational filtering method that is based on polynomial filtering in which the solutions to the linear systems are approximated by a Krylov subspace method. Our analysis and numerical experiments indicate that the rational filtering method is effective when the eigenvalues of a given matrix are sparsely distributed in the target interval.

    
    


53. Lijiong Su, Akira Imakura, Hiroto Tadano and Tetsuya Sakurai,
    Improving the convergence behaviour of the BiCGSTAB method by applying D-norm minimization,
    JSIAM Letters, Vol. 7, 2015, pp.37-40. [abstract]
    
    

    [abstract] In this article, we deal with the iterative methods for solving unsymmetric linear systems, especially BiCGSTAB. The introduced parameter in BiCGSTAB at each iteration is selected to minimize the 2-norm of the residual vector. Here, we suggest another way to select the parameter by the idea of weighting used in Weighted GMRES. By our procedure, more importance is assigned to the larger entry of the residual vector so that faster convergence can be expected. In numerical experiments, it is shown that our procedure is efficient compared with the original BiCGSTAB.

    
    


54. Akira Imakura,
    An efficient algorithm to construct an orthonormal basis for the extended Krylov subspace,
    East Asian Journal on Applied Mathematics, Vol. 4, No. 3, 2014, pp.267-282. [abstract]
    
    

    [abstract] Subspace projection methods based on the Krylov subspace using powers of a matrix A have often been standard for solving large matrix computations in many areas of application. Recently, projection methods based on the extended Krylov subspace using powers of A and A^{-1} have attracted attention, particularly for functions of a matrix times a vector and matrix equations. In this article, we propose an efficient algorithm for constructing an orthonormal basis for the extended Krylov subspace. Numerical experiments indicate that this algorithm has less computational cost and approximately the same accuracy as the traditional algorithm.

    
    


55. Shusaku Saito, Hiroto Tadano, Akira Imakura,
    Development of the block BiCGSTAB(l) method for solving linear systems with multiple right hand sides,
    JSIAM Letters, Vol. 6, 2014, pp.65-68. [abstract]
    
    

    [abstract] In this paper, we derive the Block BiCGSTAB($\ell$) method which is developed by extending the BiCGSTAB($\ell$) method. We also propose some techniques to improve convergence properties by applying orthogonalization and to improve the accuracy of the approximate solutions by additional matrix multiplications. Some numerical experiments indicate that the performance of the Block BiCGSTAB($\ell$) method with those stabilization techniques can be higher than that of the Block BiCGSTAB method.

    
    


56. Hiroto Tadano, Youichi Ishikawa, Akira Imakura,
    Improvement of the accuracy of the approximate solution of the block BiCR method,
    JSIAM Letters, Vol.6, 2014, pp.61-64. [abstract]
    
    

    [abstract] Block Krylov subspace methods are efficient solvers for linear systems with multiple right-hand sides in terms of the number of iterations and computational time. As one of Block Krylov subspace methods, the Block BiCR method has been proposed by Zhang et al. in 2013. This method often shows a smooth convergence behavior compared with the Block BiCG method. However, the accuracy of the approximate solution generated by the Block BiCR method often deteriorates. In this paper we propose a modified Block BiCR method in order to improve the accuracy of the approximate solutions.

    
    


57. Akira Imakura, Lei Du, Tetsuya Sakurai,
    A block Arnoldi-type contour integral spectral projection method for solving generalized eigenvalue problems,
    Applied Mathematics Letters, Vol.32, 2014, pp.22-27. [abstract]
    
    

    [abstract] For generalized eigenvalue problems, we consider computing all eigenvalues located in a certain region and their corresponding eigenvectors. Recently, contour integral spectral projection methods have been proposed for solving such problems. In this study, from the analysis of the relationship between the contour integral spectral projection and the Krylov subspace, we conclude that the Rayleigh-Ritz-type of the contour integral spectral projection method is mathematically equivalent to the Arnoldi method with the projected vectors obtained from the contour integration. By this Arnoldi-based interpretation, we then propose a block Arnoldi-type contour integral spectral projection method for solving the eigenvalue problem.

    
    


58. Akira Imakura, Lei Du, Hiroto Tadano,
    A Weighted Block GMRES method for solving linear systems with multiple right-hand sides,
    JSIAM Letters, Vol.5, 2013, pp.65-68. [abstract]
    
    

    [abstract] We investigate the Block GMRES method for solving large and sparse linear systems with multiple right-hand sides. For solving linear systems with a single right-hand side, the Weighted GMRES method based on the weighted minimal residual condition has been proposed as an improvement of the GMRES method. In this paper, by applying the idea of the Weighted GMRES method to the Block GMRES method, we propose a Weighted Block GMRES method. The numerical experiments indicate that the Weighted Block GMRES(m) method has higher performance for efficient convergence than the Block GMRES(m) method.

    
    


59. 山崎 育朗, 今倉 暁, 多田野 寛人, 櫻井 鉄也,
    残差最小性に基づくKrylov部分空間反復法に対する疎行列用直接解法を用いた前処理のパラメータ推定,
    日本応用数理学会論文誌, Vol.23, No.3, 2013, pp.381-404. [概要]
    
    

    [概要] 非零要素数が多く, 絶対値の小さい要素を多く含む行列を係数行列にもつ連立一次方程式に適した方法として, 近似係数行列に対する疎行列用直接解法を用いた前処理が提案されている. 同前処理における近似係数行列はCutoffという方法で生成され, 前処理の性能はCutoffパラメータに強く依存する. 本論文では適切なCutoffパラメータの推定法を提案し, 数値実験において提案法の有効性を検証する.

    
    


60. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
    An efficient variant of the restarted shifted GMRES method for solving shifted linear systems,
    Journal of Mathematical Research with Applications, Vol.33, No.2, 2013, pp.127-141. [abstract]
    
    

    [abstract] We investigate the restart of the Restarted Shifted GMRES method for solving shifted linear systems. Recently the variant of the GMRES(m) method with the unfixed update has been proposed to improve the convergence of the GMRES(m) method for solving linear systems, and shown to have an efficient convergence property. In this paper, by applying the unfixed update to the Restarted Shifted GMRES method, we propose a variant of the Restarted Shifted GMRES method. We show a potentiality for efficient convergence within the variant by some numerical results.

    
    


61. Akira Imakura, Tetsuya Sakurai, Kohsuke Sumiyoshi, Hideo Matsufuru
    A parameter optimization technique for a weighted Jacobi-type preconditioner,
    JSIAM Letters, Vol.4, 2012, pp.41-44. [abstract]
    
    

    [abstract] The Jacobi preconditioner is well known as a preconditioner with high parallel efficiency to solve very large linear systems. However, the Jacobi preconditioner does not always show the great improvement of the convergence rate, because of the poor convergence property of the Jacobi method. In this paper, in order to improve the quality of the Jacobi preconditioner without loss its parallel efficiency, we introduce a weighted Jacobi-type preconditioner, and propose an optimization technique for the weight parameter. The numerical experiments indicate that the proposed preconditioner has higher quality and is more efficient than the traditional Jacobi preconditioner.

    
    


62. 今倉 暁, 楊 済栄, 曽我部 知広, 張 紹良,
    デフレーション型とLook-Back型のリスタートを併用したGMRES(m)法の収束特性,
    日本応用数理学会論文誌, Vol.22, No.3, 2012, pp.117-141. [概要]
    
    

    [概要] 非対称線形方程式のためのGMRES(m)法に対する改良法として，デフレーション型とLook-Back型のリスタートに着目する． 本論文では，残差多項式に基づく解析を通し，両リスタート技法が異なる数理的背景に基づきGMRES(m)法の収束性を改善することを示す． またその事実に基づき，両リスタート技法を併用するGMRES(m)法の改良法を提案し，数値実験からその収束特性を検証する．

    
    


63. 今倉 暁, 曽我部 知広, 張 紹良,
    非対称線形方程式のためのLook-Back GMRES(m)法,
    日本応用数理学会論文誌, Vol.22, No.1, 2012, pp.1-22. [概要]
    
    

    [概要] 近年我々は, 非対称線形方程式に対するGMRES(m)法のリスタート時の初期近似解の設定法に着目し, 誤差方程式の観点からGMRES(m)法の拡張法を提案した. 本論文では, 拡張したGMRES(m)法の残差多項式に対する解析を通し, GMRES(m)法の残差多項式を逐次再構築する``Look-Back戦略''に基づくLook-Back GMRES(m)法を提案する. また, Look-Back GMRES(m)法の有効性を数値実験から検証する.

    
    


64. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
    An efficient variant of the GMRES(m) method based on the error equations,
    East Asian Journal on Applied Mathematics, Vol. 2, No. 1, 2012, pp.19-32. [abstract]
    
    

    [abstract] The GMRES(m) method proposed by Saad and Schultz is one of the most successful Krylov subspace methods for solving nonsymmetric linear systems. In this paper, we investigate how to update the initial guess to make it converge faster, and in particular propose an efficient variant of the method that exploits an unfixed update. The mathematical background of the unfixed update variant is based on the error equations, and its potential for efficient convergence is explored in some numerical experiments.

    
    


65. 【平成22年度 日本応用数理学会論文賞】 今倉 暁, 曽我部 知広, 張 紹良,
    GMRES(m)法のリスタートについて,
    日本応用数理学会論文誌, Vol.19, No.4, 2009, pp.551-564. [概要]
    
    

    [概要] 1986年, SaadとSchultzによって, 非対称線形方程式に対する有効な解法として, GMRES法およびそのリスタート版であるGMRES(m)法が提案された. 本論文では, GMRES(m)法のリスタートに焦点をあて, リスタート時の初期近似解の設定に自由度を与えることによりGMRES(m)法を拡張することを目的とする. また, 本拡張の数理的背景を誤差方程式の観点から考察する. さらに, 提案法が有効となり得る可能性について数値実験を通して検証する.

    
    


66. 【平成23年度 日本応用数理学会論文賞】 則竹 渚宇, 今倉 暁, 山本 有作, 張 紹良,
    行列の指数関数に基づく連立線形常微分方程式の大粒度並列化解法とその評価,
    日本応用数理学会論文誌, Vol.19, No.3, 2009, pp.293-312. [概要] [BibTeX]
    
    

    [概要] 大規模連立線形常微分方程式の解法として, 解を行列の指数関数により解析的に表現し, クリロフ部分空間法を用いてそれを近似する手法が考えられる. この手法は大粒度の並列性を持つが, 実用化に当たっては, クリロフ部分空間の次元増加に伴う不安定性の解消, 要求精度に応じた次元数の適切な決定などが課題となる. 本論文では, これらに対する解決法を提案する. また, 本提案に基づく解法と陰的差分法を数値実験により比較し, 解の要求精度が高い場合には, 前者が高速となることを示す.

    
    
    
    

    日本語版： @article{imakura2009large title = {行列の指数関数に基づく連立線形常微分方程式の大粒度並列化解法とその評価}, author = {則竹渚宇 and 今倉暁 and 山本有作 and 張紹良}, journal = {日本応用数理学会論文誌}, volume = {19}, number = {3}, pages = {293--312}, year = {2009}, publisher = {一般社団法人 日本応用数理学会}, doi = {https://doi.org/10.11540/jsiamt.19.3_293}, } 英語版： @article{imakura2009large title = {A Large-Grained Parallel Solver for Linear Simultaneous Ordinary Differential Equations based on Matrix Exponential and its Evaluatio}, author = {Sho Noritak and Akira Imakura and Yusaku Yamamoto and Shao-Liang Zhang}, journal = {Transactions of the Japan Society for Industrial and Applied Mathematics}, volume = {19}, number = {3}, pages = {293--312}, year = {2009}, publisher = {The Japan Society for Industrial and Applied Mathematics}, doi = {https://doi.org/10.11540/jsiamt.19.3_293}, note = {(in Japanese)} }

    
    


67. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
    An implicit Wavelet sparse approximate inverse preconditioner using block finger pattern,
    Numerical Linear Algebra with Applications, Vol.16, Issue 11-12, 2009, pp.915-928. [abstract] [BibTeX]
    
    

    [abstract] Recently, an implicit wavelet sparse approximate inverse (IW-SPAI hereafter) preconditioner has been proposed by Hawkins and Chen for nonsymmetric linear systems. The preconditioning matrix of the IW-SPAI is characterized by a special sparse structure, the so-called finger pattern, which makes it possible to construct a good sparse approximate inverse. Since the IW-SPAI requires a number of QR factorizations with substantial costs to construct the preconditioner, there is a strong need to construct the IW-SPAI more efficiently. The target of this paper is to reduce the costs of the IW-SPAI using Haar basis. Under the strategy of classifying the QR factorizations into several groups and reducing the number of QR factorizations, in this paper, we introduce a block finger pattern for Haar basis in the place of the finger pattern as the nonzero pattern of the preconditioning matrix. From the block finger pattern, we propose an implicit wavelet sparse approximate inverse preconditioner using block finger pattern (BIW-SPAI hereafter). Numerical experiments indicate that the BIW-SPAI is often more efficient than the IW-SPAI.

    
    
    
    

    @article{imakura2009implicit, title = {An implicit wavelet sparse approximate inverse preconditioner using block finger pattern}, author = {Imakura, Akira and Sogabe, Tomohiro and Zhang, S-L}, journal = {Numerical Linear Algebra with Applications}, volume = {16}, number = {11-12}, pages = {915--928}, year = {2009}, publisher = {Wiley Online Library}, doi = {https://doi.org/10.1002/nla.657} }

    
    


68. 今倉 暁, 曽我部 知広, 張 紹良,
    Finger patternのブロック化による陰的wavelet近似逆行列前処理の高速化,
    日本応用数理学会論文誌, Vol.17, No.4, 2007, pp.523-542. [概要] [BibTeX]
    
    

    [概要] 近年, Krylov部分空間法の前処理として陰的wavelet近似逆行列前処理(IW-SPAI)が提案された. この前処理行列は,finger patternと呼ばれる非零構造を持ち, 多数の小規模な最小二乗問題を解くことにより構築される. 本論文ではIW-SPAIを改良することを目的とし, そのfinger patternをブロック化することにより, 最小二乗問題で用いられるQR分解の結果を再利用し, QR分解の回数を削減することで, 効率良く前処理行列が構築されることを示す. そして, 数値実験でその有効性を検証する.

    
    
    
    

    日本後版： @article{imakura2007efficient, title = {Finger patternのブロック化による陰的wavelet近似逆行列前処理の高速化}, author = {今倉暁 and 曽我部知広 and 張紹良}, journal = {日本応用数理学会論文誌}, volume = {17}, number = {4}, pages = {523--542}, year = {2007}, publisher = {一般社団法人 日本応用数理学会}, doi = {https://doi.org/10.11540/jsiamt.17.4_523} } 英語版： @article{imakura2007efficient, title = {An efficient implicit wavelet sparse approximate inverse preconditioner using blocked finger pattern}, author = {Akira Imakura and Tomohiro Sogabe and Shao-Liang Zhang}, journal = {Transactions of the Japan Society for Industrial and Applied Mathematics}, volume = {17}, number = {4}, pages = {523--542}, year = {2007}, publisher = {The Japan Society for Industrial and Applied Mathematics}, doi = {https://doi.org/10.11540/jsiamt.17.4_523}, note = {(in Japanese)} }

    
    

国際会議プロシーディング / Conference Proceedings (Peer reviewed)

1. Rina Kagawa, Akira Imakura, Masaki Matsubara,
   A privacy-preserving method to clarify the useful content of documents owned by multiple institutes,
   In: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14457.


2. Hiromi Yamashiro, Kazumasa Omote, Akira Imakura, Tetsuya Sakurai,
   A Study of the Privacy Perspective on Principal Component Analysis via a Realistic Attack Model,
   In: Proceedings of 2022 International Conference on Computational Intelligence and Security (CIS'2022), pp.376-380, 2022.


3. Takaya Yamazoe, Hiromi Yamashiro, Kazumasa Omote, Akira Imakura, Tetsuya Sakura,
   Image Data Recoverability against Data Collaboration and its Countermeasure,
   In: Su, C., Sakurai, K. (eds) Science of Cyber Security - SciSec 2022 Workshops. SciSec 2022. Communications in Computer and Information Science, vol 1680. pp.3-15, 2022.


4. Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota,
   Consistent MDT-Tucker: A Hankel Structure Constrained Tucker Decomposition in Delay Embedded Space,
   In: Proceedings of 2022 APSIPA Annual Summit and Conference. pp.137-142, 2022.


5. Anna Bogdanova, Akira Imakura, Tetsuya Sakurai, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe,
   Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration,
   In: Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022), PKAW/2022/03, 2022.


6. Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa, Akira Imakura, Hiroshi Nakamura, Kenjiro Taura, Tomohiro Kudoh, Toshihiro Hanawa, Yuji Sekiya, Hiroki Kobayashi, Shin Matsushima, Yohei Kuga, Ryo Nakamura, Renhe Jiang, Junya Kawase, Masatoshi Hanai, Hiroshi Miyazaki, Tsutomu Ishizaki, Daisuke Shimotoku, Daisuke Miyamoto, Kento Aida, Atsuko Takefusa, Takashi Kurimoto, Koji Sasayama, Naoya Kitagawa, Ikki Fujiwara, Yusuke Tanimura, Takayuki Aoki, Toshio Endo, Satoshi Ohshima, Keiichiro Fukazawa, Susumu Date, Toshihiro Uchibayashi,
   mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations,
   In: The 8th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2022), 2022


7. Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota,
   Fast Algorithm for Low-rank Tensor Completion in Delay-embedded Space,
   Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2058-2066, 2022.


8. Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
   Collaborative Novelty Detection for Distributed Data by a Probabilistic Method,
   In: The 13th Asian Conference on Machine Learning (ACML 2021), PMLR, Vol. 157, pp. 932-947, 2021.


9. Xiucai Ye, Chunhao Wang, Akira Imakura, Tetsuya Sakurai,
   Spectral Clustering Joint Deep Embedding Learning by Autoencoder,
   In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2021.


10. Akira Imakura, Anna Bogdanova, Takaya Yamazoe, Kazumasa Omote, Tetsuya Sakurai,
    Accuracy and Privacy Evaluations of Collaborative Data Analysis,
    In: The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), 2021.


11. Takahiro Yano, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace,
    In: HPC Asia 2021: The International Conference on High Performance Computing in Asia-Pacific Region, pp. 83-89, 2021.


12. Christie L. Alappat, Andreas Alvermann, Achim Basermann, Holger Fehske, Yasunori Futamura, Martin Galgon, Georg Hager, Sarah Huber, Akira Imakura, Masatoshi Kawai, Moritz Kreutzer, Bruno Lang, Kengo Nakajima, Melven Rohrig-Zollner, Tetsuya Sakurai, Faisal Shahzad, Jonas Thies, Gerhard Wellein,
    ESSEX: Equipping Sparse Solvers For Exascale,
    In: Bungartz HJ., Reiz S., Uekermann B., Neumann P., Nagel W. (eds), Software for Exascale Computing - SPPEXA 2016-2019, Lecture Notes in Computational Science and Engineering, vol 136. Springer, Cham, pp 143--187, 2020.


13. Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
    Collaborative Data Analysis: Non-Model Sharing-Type Machine Learning for Distributed Data,
    In: Uehara H., Yamaguchi T., Bai Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science, vol 12280. Springer, Cham, pp. 14-29, 2021.


14. 【Best Student Paper Award】 Suhyeon Baek, Akira Imakura, Tetsuya Sakurai, Ichiro Kataoka,
    Accelerating the Backpropagation algorithm by Using the NMF-based method on Deep Neural Networks,
    In: Uehara H., Yamaguchi T., Bai Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science, vol 12280. Springer, Cham, pp. 1-13, 2021.


15. Anna Bogdanova, Akie Nakai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
    Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations,
    In: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI'20), 2021.


16. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Ensemble Learning for Spectral Clustering,
    In: 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy, pp. 1094-1099, 2020.


17. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Hubness-based Sampling Method for Nystrom Spectral Clustering,
    In: The 2020 International Joint Conference on Neural Networks (IJCNN 2020), Glasgow, United Kingdom, pp. 1-8, 2020.


18. 【Best Paper Finalist】 Naoya Chiba, Akira Imakura, Koichi Hashimoto,
    Fast ADMM l1 minimization by applying SMW formula and multi-row simultaneous estimation for Light Transport Matrix acquisition,
    In: the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), pp. 14--21, 2019.
    


19. 【Best Paper Finalist】 Naoya Chiba, Mingyu Li, Akira Imakura, Koichi Hashimoto,
    Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation,
    In: the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), pp. 7--13, 2019.
    


20. Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai,
    Distributed Collaborative Feature Selection Based on Intermediate Representation,
    In: the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), pp. 4142--4149, 2019.
    


21. Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction,
    In: the AAAI Conference on Artificial Intelligence, Vol. 33, pp. 3910-3918. 2019.
    


22. Haruka Yamada, Akira Imakura, Toshiyuki Imamura, Tetsuya Sakurai,
    Optimization of Reordering Procedures in HOTRG for Distributed Parallel Computing,
    In: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 957-966, 2018.
    


23. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Tomoya Ono,
    Efficient and Scalable Calculation of Complex Band Structure using Sakurai-Sugiura Method,
    In: SC'17 proceeding of the International Conference for High Performance Computing, Networking, Storage and Analysis, Article No. 40, 2017.
    


24. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Structure-preserving technique in the block SS--Hankel method for solving Hermitian generalized eigenvalue problems,
    In: Wyrzykowski R., Dongarra J., Deelman E., Karczewski K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science, vol 10777. Springer, Cham, pp.600-611, 2017.


25. Akira Imakura, Yasunori Futamura, Tetsuya, Sakurai,
    An error resilience strategy of a complex moment-based eigensolver,
    In: Sakurai T., Zhang SL., Imamura T., Yamamoto Y., Kuramashi Y., Hoshi T. (eds) Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing. EPASA 2015. Lecture Notes in Computational Science and Engineering, vol 117. Springer, Cham, pp.1-18, 2017.
    


26. Hiroto Tadano, Shusaku Saito, Akira Imakura,
    Accuracy improvement of the Shifted Block BiCGGR method for linear systems with multiple shifts and multiple right-hand sides,
    In: Sakurai T., Zhang SL., Imamura T., Yamamoto Y., Kuramashi Y., Hoshi T. (eds) Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing. EPASA 2015. Lecture Notes in Computational Science and Engineering, vol 117. Springer, Cham, pp.81-90, 2017.
    


27. Hiroya Suno, Yoshifumi Nakamura, Ken-Ichi Ishikawa, Yoshinobu Kuramashi, Yasunori Futamura, Akira Imakura, and Tetsuya Sakurai,
    Eigenspectrum Calculation of the O(a)-improved Wilson-Dirac Operator in Lattice QCD using the Sakurai-Sugiura Method,
    In: Sakurai T., Zhang SL., Imamura T., Yamamoto Y., Kuramashi Y., Hoshi T. (eds) Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing. EPASA 2015. Lecture Notes in Computational Science and Engineering, vol 117. Springer, Cham, pp.171-185, 2017.
    


28. Kohsuke Sumiyoshi, Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Hideo Matsufuru, Akira Imakura, Shoichi Yamada,
    Core-Collapse Supernovae Explored by Multi-D Boltzmann Hydrodynamic Simulations,
    In Proceedings of the 14th International Symposium on Nuclei in the Cosmos (NIC2016), 010606, 2017.


29. Tetsuya, Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura,
    Alternating optimization method based on nonnegative matrix factorizations for deep neural networks,
    In: A. Hirose, S. Ozawa, K. Doya, K. Ikeda, M. Lee, D. Liu. eds, Neural Information Processing, ICONIP 2016, Lecture Notes in Computer Science, Vol 9950, 2016, pp.354-362.
    


30. Hiroya Suno, Yoshifumi Nakamura, Ken-Ichi Ishikawa, Yoshinobu Kuramashi, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Eigenspectrum calculation of the non-Hermitian O(a)-improved Wilson-Dirac operator using the Sakurai-Sugiura method,
    In: The 33rd International Symposium on Lattice Field Theory (LATTICE2015), 2016, 026.


31. Yasunori Futamura, Shoji Hashimoto, Akira Imakura, Keitaro Nagata, Tetsuya Sakurai,
    A filtering technique for the temporally reduced matrix of the Wilson fermion determinant,
    In: The 32nd International Symposium on Lattice Field Theory (LATTICE2014), 2014, 049.


32. Akira Imakura, Tetsuya Sakurai, Kohsuke Sumiyoshi, Hideo Matsufuru,
    An auto-tuning technique of the weighed Jacobi-type iteration used for preconditioners of Krylov subspace methods,
    In: IEEE 6th International Symposium on Embedded Multicore SoCs (MCSoC-12), 2012, pp.183-190.
    


33. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
    A modification of implicit Wavelet sparse approximate inverse preconditioner based on a block finger pattern,
    In: Frontiers of Computational Science 2008, Y. Kaneda, M. Sasai and K. Tachibana (eds.), 2008, pp.271-278.
    

テクニカルレポート / Technical Reports

1. Akira Imakura, Tetsuya Sakurai,
   FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration,
   arXiv:2409.18356 [cs.LG], 2024.
   


2. Akihiro Mizoguchi, Anna Bogdanova, Akira Imakura, Tetsuya Sakurai,
   Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings,
   arXiv:2308.00280 [cs.LG], 2023.
   


3. 罇 涼稀，竹田 俊彦, 今倉 暁, 櫻井鉄也, 岡田幸彦,
   リレーションシップバンキング機能の向上を目的とした中小企業の資金ニーズ判別法とその活用の提案,
   筑波大学 社会工学コモンズ Discussion Paper Series, No.1385, 2023.


4. 罇 涼稀, 秦 涼太, 今倉 暁, 櫻井鉄也, 岡田幸彦,
   財務諸表データを用いた資金ニーズの見過ごしチェックAIの開発,
   筑波大学 社会工学コモンズ Discussion Paper Series, No.1383, 2022.


5. Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe,
   Non-readily identifiable data collaboration analysis for multiple datasets including personal information,
   arXiv:2208.14611 [cs.LG], 2022.
   


6. Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai,
   Another Use of SMOTE for Interpretable Data Collaboration Analysis,
   arXiv:2208.12458 [cs.LG], 2022.
   


7. Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
   Collaborative causal inference on distributed data,
   arXiv:2208.07898 [stat.ME], 2022.
   


8. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
   Complex moment-based methods for differential eigenvalue problems,
   arXiv:2205.00971 [math.NA], 2022.
   


9. Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa, Akira Imakura, Hiroshi Nakamura, Kenjiro Taura, Tomohiro Kudoh, Toshihiro Hanawa, Yuji Sekiya, Hiroki Kobayashi, Shin Matsushima, Yohei Kuga, Ryo Nakamura, Renhe Jiang, Junya Kawase, Masatoshi Hanai, Hiroshi Miyazaki, Tsutomu Ishizaki, Daisuke Shimotoku, Daisuke Miyamoto, Kento Aida, Atsuko Takefusa, Takashi Kurimoto, Koji Sasayama, Naoya Kitagawa, Ikki Fujiwara, Yusuke Tanimura, Takayuki Aoki, Toshio Endo, Satoshi Ohshima, Keiichiro Fukazawa, Susumu Date, Toshihiro Uchibayashi,
   mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations,
   arXiv:2203.14188 [cs.LG], 2022.
   


10. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Verified eigenvalue and eigenvector computations using complex moments and the Rayleigh-Ritz procedure for generalized Hermitian eigenvalue problems,
    arXiv:2110.01822 [math.NA], 2021.
    


11. Akira Imakura, Tetsuya Sakurai,
    Complex moment-based method with nonlinear transformation for computing large and sparse interior singular triplets,
    arXiv:2109.13655 [math.NA], 2021.
    


12. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    LSEC: Large-scale spectral ensemble clustering,
    arXiv:2106.09852 [cs.LG], 2021.
    


13. Kensuke Aihara, Akira Imakura, Keiichi Morikuni,
    Cross-interactive residual smoothing for global and block Lanczos-type solvers for linear systems with multiple right-hand sides,
    arXiv:2106.00284 [math.NA], 2021.
    


14. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Divide-and-conquer based Large-Scale Spectral Clustering,
    arXiv:2104.15042 [cs.LG], 2021.
    


15. Akira Imakura, Anna Bogdanova, Takaya Yamazoe, Kazumasa Omote, Tetsuya Sakurai,
    Accuracy and Privacy Evaluations of Collaborative Data Analysis,
    arXiv:2101.11144 [cs.LG], 2021.
    


16. Anna Bogdanova, Akie Nakai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
    Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations,
    arXiv:2011.06803 [cs.LG], 2020.
    


17. Akira Imakura, Hiroaki Inaba, Yukihiko Okada, Tetsuya Sakurai,
    Interpretable collaborative data analysis on distributed data,
    arXiv:2011.04437 [cs.LG], 2020.
    


18. Sarah Huber, Yasunori Futamura, Martin Galgon, Akira Imakura, Bruno Lang, Tetsuya Sakurai,
    Flexible subspace iteration with moments for an effective contour integration-based eigensolver,
    arXiv:2010.10162 [math.NA], 2020.
    


19. Momo Matsuda, Keiichi Morikuni, Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
    Multiclass spectral feature scaling method for dimensionality reduction,
    arXiv:1910.07174 [cs.LG], 2019.
    


20. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Verified partial eigenvalue computations for Hermitian generalized eigenproblems using contour integrals,
    arXiv:1904.06277 [math.NA], 2019.
    


21. Akira Imakura, Tetsuya Sakurai,
    Data collaboration analysis for distributed datasets,
    arXiv:1902.07535 [cs.LG], 2019.
    


22. Andreas Alvermann, Achim Basermann, Hans-Joachim Bungartz, Christian Carbogno, Dominik Ernst, Holger Fehske, Yasunori Futamura, Martin Galgon, Georg Hager, Sarah Huber, Thomas Huckle, Akihiro Ida, Akira Imakura, Masatoshi Kawai, Simone Kocher, Moritz Kreutzer, Pavel Kus, Bruno Lang, Hermann Lederer, Valeriy Manin, Andreas Marek, Kengo Nakajima, Lydia Nemec, Karsten Reuter, Michael Rippl, Melven Rohrig-Zollner, Tetsuya Sakurai, Matthias Scheffler, Christoph Scheurer, Faisal Shahzad, Danilo Simoes Brambila, Jonas Thies, Gerhard Wellein,
    Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects,
    arXiv:1806.01036 [physics.comp-ph], 2018.
    


23. Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Hirotada Okawa, Akira Harada, Kohsuke Sumiyoshi, Shoichi Yamada, Hideo Matsufuru, Akira Imakura,
    Simulations of core-collapse supernovae in spatial axisymmetry with full Boltzmann neutrino transport,
    arXiv:1702.01752 [astro-ph.HE], 2018.
    


24. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Shigeru Tsukamoto, Tomoya Ono,
    Contour integral method for obtaining the self-energy matrices of electrodes in electron transport calculations,
    arXiv:1709.09324 [cond-mat.mtrl-sci], 2017.
    


25. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Tomoya Ono,
    Efficient and Scalable Calculation of Complex Band Structure using Sakurai-Sugiura Method,
    arXiv:1709.09347 [cond-mat.mtrl-sci], 2017.
    


26. Akira Imakura, Yusaku Yamamoto,
    Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product,
    arXiv:1703.10440 [math.NA], 2017.


27. Xian-Ming Gu, Ting-Zhu Huang, Bruno Carpentieri, Akira Imakura, Ke Zhang, Lei Du,
    Variants of the CMRH method for solving multi-shifted non-Hermitian linear systems,
    arXiv:1611.00288 [math.NA], 2016.


28. Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura,
    Alternating optimization method based on nonnegative matrix factorizations for deep neural networks,
    arXiv:1605.04639 [cs.LG], 2016.


29. Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Kohsuke Sumiyoshi, Shoichi Yamada, Hideo Matsufuru, Akira Imakura,
    Three-dimensional Boltzmann-Hydro code for core-collapse in massive stars II. The Implementation of moving-mesh for neutron star kicks,
    arXiv:1605.00666 [astro-ph.HE], 2016.


30. Akira Imakura, Lei Du, Tetsuya Sakurai,
    A map of contour integral-based eigensolvers for solving generalized eigenvalue problems,
    arXiv:1510.02572 [math.NA], 2015.


31. Yasunori Futamura, Shoji Hashimoto, Akira Imakura, Keitaro Nagata, Tetsuya Sakurai,
    A filtering technique for the temporally reduced matrix of the Wilson fermion determinant,
    arXiv:1411.4262 [hep-lat], 2014.


32. Akira Imakura, Lei Du, Tetsuya Sakurai,
    Accuracy analysis on the Rayleigh-Ritz type of the contour integral based eigensolver for solving generalized eigenvalue problems,
    Technical Report of Department of Computer Science, University of Tsukuba (CS-TR), CS-TR-14-23.


33. Akira Imakura, Tomohiro Sogabe, Shao-Liang Zhang,
    A Look-Back-type restart for the restarted Krylov subspace methods to solve non-Hermitian linear systems,
    Technical Report of Department of Computer Science, University of Tsukuba (CS-TR), CS-TR-13-22.


34. Akira Imakura, Lei Du, Tetsuya Sakurai,
    A block Arnoldi-type contour integral spectral projection method for solving generalized eigenvalue problems,
    Technical Report of Department of Computer Science, University of Tsukuba (CS-TR), CS-TR-13-21.

解説論文

1. 今倉 暁,
   多機関分散データに対する生存時間分析,
   オペレーションズ・リサーチ, Vol. 69, No. 6, 2024, pp.303-209.


2. 櫻井 鉄也，今倉 暁,
   多機関分散データに対するデータコラボレーション解析,
   オペレーションズ・リサーチ, Vol. 69, No. 6, 2024, pp.283-289.


3. 今倉 暁,
   多機関分散データの安全な統合解析技術　～秘匿性と高度解析の両立～,
   月刊 車載テクノロジー（2023年8月号）, Vol.10, No.11, 2023, pp.67--73.


4. 和田 耕一, 佐久間 淳, 平田 祥人, 福地 一斗, 青砥 隆仁, 五十嵐 康彦, 今倉 暁, Vasilache Simona Mirela, 海野 広志, 遠藤 結城, 岡 瑞起, 川口 一画, 國廣 昇, 滝沢 穂高, 津川 翔, 三末 和男, 三谷 純,
   筑波大学における全学必修のデータサイエンス教育,
   オペレーションズ・リサーチ, Vol. 65, No. 11, 2020, pp.573-578.


5. 今倉 暁,
   行列計算を用いた機械学習法,
   オペレーションズ・リサーチ, Vol. 65, No. 6, 2020, pp.310-316.

講究録

1. 今倉 暁, 櫻井 鉄也,
   行列分解を基盤としたディープニューラルネットワーク計算法,
   京都大学数理解析研究所講究録, No.2167 「諸科学分野を結ぶ基礎学問としての数値解析学」, 2020.7.


2. 今倉 暁, 櫻井 鉄也,
   2つのKrylov部分空間による複素モーメント型固有値解法の改良,
   京都大学数理解析研究所講究録, No.2037 「現象解明に向けた数値解析学の新展開 II」, 2017.7, pp.21-31.


3. Lei Du, Akira Imakura, Tetsuya Sakurai,
   An Algorithm for Simultaneous Band Reduction of Two Dense Symmetric Matrices,
   京都大学数理解析研究所講究録, No.2005 「応用数理と計算科学における理論と応用の融合」, 2016.11, pp.21-31.


4. 今倉 暁, 杜 磊, 櫻井 鉄也,
   各種周回積分型固有値解法の関係性について,
   京都大学数理解析研究所講究録, No.1957 「新時代の科学技術を牽引する数値解析学」, 2015.7, pp.142-154.


5. 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
   重み付き定常反復型前処理のためのパラメータ最適化手法および超新星爆発計算における有効性,
   京都大学数理解析研究所講究録, No.1848 「次世代計算科学の基盤技術とその展開」, 2013.8, pp.15-24.


6. 今倉 暁, 曽我部 知広, 張 紹良,
   シフト称線形方程式に対するリスタート付きShifted Krylov部分空間法について,
   京都大学数理解析研究所講究録, No.1791 「科学技術計算における理論と応用の新展開」, 2012.4, pp.47-56.

学位論文 / Theses

• 博士論文, 名古屋大学 大学院工学研究科 計算理工学専攻, 2011.

  論文題目 ：	Restarted Krylov Subspace Methods and Wavelet-Based Preconditioners for Solving Large and Sparse Linear Systems --a Look-Back Strategy and a Blocking Technique--
  主査 ：	張 紹良 教授	（名古屋大学 大学院工学研究科）
  副査 ：	畔上 秀幸 教授 山本 章夫 教授 曽我部 知広 准教授 今堀 慎治 講師	（名古屋大学 大学院情報科学研究科） （名古屋大学 大学院工学研究科） （愛知県立大学 大学院情報科学研究科） （名古屋大学 大学院工学研究科）



• 修士論文, 名古屋大学 大学院工学研究科 計算理工学専攻, 2008.
  

  論文題目 ：	Block Algorithms of Wavelet Preconditioner for Solving Large Linear Systems
  主査 ：	張 紹良 教授	（名古屋大学 大学院工学研究科）
  副査 ：	安藤 秀樹 教授 山本 有作 准教授	（名古屋大学 大学院工学研究科） （名古屋大学 大学院工学研究科）



• 学士論文, 名古屋大学 工学部 物理工学科, 2006.
  

  論文題目 ：	ウェーブレットフィルタリングによる２次元一様等方性乱流における秩序渦度場の抽出
  指導教員 ：	金田 行雄 教授	（名古屋大学 大学院工学研究科）

Talks

---

国際会議 / International Conferences

1. Akira Imakura,
   Complex moment-based eigen solvers and their recent developments,
   2024 Dalian International Conference on Mathematics, Dalian University of Technology, China, September 24-28, 2024.


2. Kensuke Aihara, Akira Imakura, Keiichi Morikuni,
   Cross-interactive residual smoothing for reducing the residual gap of block Lanczos-type iterative methods,
   The 17th SIAM East Asian Section Conference (EASIAM2024), Macao SAR, June 28-July 1, 2024.


3. Akira Imakura, Tetsuya Sakurai, Yasunori Futamura,
   Recent progress in complex moment-based eigenvalue solvers ,
   SIAM Conference on Parallel Processing for Scientific Computing (PP24), Lord Baltimore Hotel, USA, March 5-8, 2024.


4. Tetsuya Sakurai, Akira Imakura, Yasunori Futamura,
   Distributed data analysis through data collaboration technique,
   SIAM Conference on Parallel Processing for Scientific Computing (PP24), Lord Baltimore Hotel, USA, March 5-8, 2024.


5. Rina Kagawa, Akira Imakura, Masaki Matsubara,
   A privacy-preserving method to clarify the useful content of documents owned by multiple institutes,
   The 25th International Conference on Asia-Pacific Digital Libraries (ICADL 2023), National Central Library, Taipei, Taiwan, December 4-7, 2023.


6. Akira Imakura, Ryoya Tsunoda, Rina Kagawa, Kunihiro Yamagata, Tetsuya Sakurai,
   Data Collaboration Cox Proportional Hazards Model for Privacy-preserving Survival Analysis,
   10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


7. Anna Bogdanova, Tetsuya Sakurai, Akira Imakura,
   Explainability and Fairness of Distributed Data Analysis,
   10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


8. Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
   Explainability and Fairness of Distributed Data Analysis,
   10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


9. Keiichi Morikuni, Akira Imakura,
   A projection method for singular eigenvalue problems of linear matrix pencils,
   10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


10. Kensuke Aihara, Akira Imakura, Keiichi Morikuni,
    Cross-interactive residual smoothing for block Lanczos-type methods for solving linear systems with multiple right-hand sides,
    10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


11. Mario Tsukassa Sato, Aiga Goto, Nanami Isoda, Ayu Kaise, Claus Aranha, Akira Imakura, Tetsuya Sakurai, Naomichi Fujiuchi, Naoya Fukuda,
    Prediction of Leaf Area Index of Tomato Plants by Image Processing and Deep Learning,
    10th International Congress on Industrial and Applied Mathematics (ICIAM2023). Waseda University, Tokyo, Japan, August 20-25, 2023.


12. Hiromi Yamashiro, Kazumasa Omote, Akira Imakura, Tetsuya Sakurai,
    A Study of the Privacy Perspective on Principal Component Analysis via a Realistic Attack Model,
    2022 International Conference on Computational Intelligence and Security (CIS'2022). Chengdu, China, March 31 - April 2, 2023.


13. Tetsuya Sakurai, Akira Imakura, Xiucai Ye, Anna Bogdanova, Yasunori Futamura, Yukihiko Okada,
    Cost-Efficient Integrated Analysis of Distributed Data in Secure Environments,
    SIAM Conference on Computational Science and Engineering (CSE23), RAI Congress Centre, The Netherlands, February 26 - March 3, 2023.


14. Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Collaborative Future Selection for Distributed Data,
    SIAM Conference on Computational Science and Engineering (CSE23), RAI Congress Centre, The Netherlands, February 26 - March 3, 2023.


15. Anna Bogdanova, Akira Imakura, Tetsuya Sakurai, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe,
    Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration,
    Knowledge Management and Acquisition for Intelligent Systems (PKAW 2022), Shanghai, China, November 10, 2022.


16. Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota,
    Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space,
    Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2022, Chiang Mai, Thailand, November 7-10, 2022.


17. Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa, Akira Imakura, Hiroshi Nakamura, Kenjiro Taura, Tomohiro Kudoh, Toshihiro Hanawa, Yuji Sekiya, Hiroki Kobayashi, Shin Matsushima, Yohei Kuga, Ryo Nakamura, Renhe Jiang, Junya Kawase, Masatoshi Hanai, Hiroshi Miyazaki, Tsutomu Ishizaki, Daisuke Shimotoku, Daisuke Miyamoto, Kento Aida, Atsuko Takefusa, Takashi Kurimoto, Koji Sasayama, Naoya Kitagawa, Ikki Fujiwara, Yusuke Tanimura, Takayuki Aoki, Toshio Endo, Satoshi Ohshima, Keiichiro Fukazawa, Susumu Date, Toshihiro Uchibayashi,
    mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations,
    The 8th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2022), Calabria, Italy, September 12-15, 2022


18. Takaya Yamazoe, Hiromi Yamashiro, Kazumasa Omote, Akira Imakura, Tetsuya Sakura,
    Image Data Recoverability against Data Collaboration and its Countermeasure,
    The 4th International Conference on Science of Cyber Security (SciSec 2022), Matsue city, Shimane, Japan, August 10-12, 2022.


19. Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota,
    Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans and Online, June 21-24, 2022.


20. Akira Imakura, Tetsuya Sakurai,
    Complex moment-based method with nonlinear transformation for computing partial singular triplets,
    Numerical Methods for Large Scale Problems, Belgrade and Online, June 6-10, 2022.


21. Ryuki Yamamoto, Tatsuya Yokota, Akira Imakura, Hidekata Hontani,
    Fast algorithm for low-rank tensor completion in delay embedded space,
    13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA2021), Online, December 14-17, 2021.


22. Akihiro Mizoguchi, Akira Imakura, Tetsuya Sakurai,
    Application of Data Collaboration Analysis to Distributed Metabolomics Data with Misaligned Features,
    2022 6th International Conference on Medical and Health Informatics (ICMHI 2022), Kyoto, May 13-15, 2022.


23. Meng Huang, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Sequential active gene selection in renal cell carcinoma,
    2022 6th International Conference on Medical and Health Informatics (ICMHI 2022), Kyoto, May 13-15, 2022.


24. Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
    Collaborative Novelty Detection for Distributed Data by a Probabilistic Method,
    The 13th Asian Conference on Machine Learning (ACML 2021), Online, November 17-19, 2021.


25. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Eigensolvers using complex moments for operators,
    Numerical Methods and Scientific Computing (NMSC21), Online, November 8-12, 2021.


26. Xiucai Ye, Chunhao Wang, Akira Imakura, Tetsuya Sakurai,
    Spectral Clustering Joint Deep Embedding Learning by Autoencoder,
    The 2021 International Joint Conference on Neural Networks (IJCNN 2021), Online, Jury 18-22, 2021.


27. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Complex Moment-Based Methods for Differential Eigenvalue Problems,
    SIAM Conference on Applied Linear Algebra (SIAM-LA21), Online, May 17-21, 2021.


28. Kensuke Aihara, Akira Imakura, Keiichi Morikuni,
    On the Residual Gap of Block Lanczos-Type Methods and Its Remedy by Cross-Interactive Residual Smoothing,
    SIAM Conference on Applied Linear Algebra (SIAM-LA21), Online, May 17-21, 2021.


29. Keiichi Morikuni, Akira Imakura, Akitoshi Takayasu,
    Verifying eigenvalues of generalized Hermitian eigenproblems using contour integrals,
    Second Workshop on Numerical Algebra, Algorithms and Analysis in Honor of Professor Ken Hayami's Retirement, Online, March 16-17, 2021.


30. Anna Bogdanova, Akira Imakura, Tetsuya Sakurai,
    Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations,
    Mini-Workshop on Cyber-Physical Systems and Cyber-Resilience - ZOOM Edition, Online, March 11, 2021.


31. Tetsuya Sakurai, Yasunori Futamura, Anna Bogdanova, Xiucai Ye, Akira Imakura,
    Collaborative Data Analysis Method based on Dimensionality Reduction,
    SIAM Conference on Computational Science and Engineering (CSE21), Online, March 1-5, 2021.


32. Akira Imakura, Anna Bogdanova, Takaya Yamazoe, Kazumasa Omote, Tetsuya Sakurai,
    Accuracy and Privacy Evaluations of Collaborative Data Analysis,
    The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), Online, February 8-9, 2021. (poster presentation)


33. Takahiro Yano, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace,
    HPC Asia 2021, Online, January 20-22, 2021.


34. Akira Imakura, Xiucai Ye, Tetsuya Sakurai,
    Collaborative Data Analysis: Non-Model Sharing-Type Machine Learning for Distributed Data,
    2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), Online, January 7-8, 2021.


35. Suhyeon Baek, Akira Imakura, Tetsuya Sakurai, Ichiro Kataoka,
    Accelerating the Backpropagation algorithm by Using the NMF-based method on Deep Neural Networks,
    2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), Online, January 7-8, 2021.


36. Anna Bogdanova, Akie Nakai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai,
    Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations,
    International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI'20), Online, January 8, 2021.


37. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Ensemble Learning for Spectral Clustering,
    20th IEEE International Conference on Data Mining (ICDM2020), Online, November 17-20, 2020.


38. Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Hubness-based Sampling Method for Nystrom Spectral Clustering,
    The 2020 International Joint Conference on Neural Networks (IJCNN 2020), Online, July 19-24, 2020.


39. Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai,
    Distributed Feature Selection by Collaborative Data Analysis,
    2020 12th International Conference on Machine Learning and Computing (ICMLC 2020), Online, June 19-21, 2020.
    


40. 【Best Presentation Award】 Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya, Sakurai,
    Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis,
    2020 12th International Conference on Machine Learning and Computing (ICMLC 2020), Online, June 19-21, 2020.
    


41. Naoya Chiba, Akira Imakura, Koichi Hashimoto,
    Fast ADMM l1 minimization by applying SMW formula and multi-row simultaneous estimation for Light Transport Matrix acquisition,
    The 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), Dali, China, December 6-8, 2019.
    


42. Naoya Chiba, Mingyu Li, Akira Imakura, Koichi Hashimoto,
    Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation,
    The 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), Dali, China, December 6-8, 2019.
    


43. Yusaku Yamamoto, Akira Imakura,
    Efficient Implementations of the Modified Gram-Schmidt Orthogonalization in a Non-Standard Inner Product,
    The 13th workshop in the series of the "Parallel Numerics" (ParNum 2019), Dubrovnik, Croatia, October 28-30, 2019.
    


44. Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    A complex moment-based dimensionality reduction for data analysis,
    2019 Mini-Workshop on Computational Science (MWCS2019), Dalian University of Technology, China, August 18-19, 2019.
    


45. Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai,
    Distributed Collaborative Feature Selection Based on Intermediate Representation,
    The 28th International Joint Conference on Artificial Intelligence (IJCAI-19), THE VENETIAN MACAO RESORT HOTEL, Macao, August 10-16, 2019.
    


46. Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    A novel dimensionality reduction method using a complex momnet-based subspace,
    International Congress on Industrial and Applied Mathematics (ICIAM2019), the Universitat de Valencia, Spain, July 15-19, 2019.
    


47. Akira Imakura, Tetsuya Sakurai,
    Nonlinear semi-NMF based method for deep neural network computations and its improvements,
    International Congress on Industrial and Applied Mathematics (ICIAM2019), the Universitat de Valencia, Spain, July 15-19, 2019. (poster presentation)
    


48. Tetsuya Sakurai, Yasunori Funamura, Xiucai Ye, Akira Imakura,
    A complex moment-based spectral method for detecting anomalous structures in large graphs,
    International Congress on Industrial and Applied Mathematics (ICIAM2019), the Universitat de Valencia, Spain, July 15-19, 2019.
    


49. Akira Imakura,
    Nonlinear semi-NMF based method for deep neural network computations,
    International Symposium on "Digital Science Now", University of Tsukuba, Japan, June 7, 2019. (poster presentation)
    


50. Yasunori Futamura, Xiucai Ye, Akira Imakura, Tetsuya Sakurai,
    Spectral Anomaly Detection in Large Graphs Using a Complex Moment-based Eigenvalue Solver,
    2019 International Workshop on Cyber-Physical Systems and Cyber-Resilience, University of Delaware, USA, March 20, 2019.
    


51. Yasunori Futamura, Takahiro Yano, Akira Imakura, Tetsuya Sakurai,
    A contour intergral-based stochastic estimator for eigenvalue counts of generalized eigenproblems,
    SIAM Conference on Computational Science and Engineering (CSE19), Spokane, Washington USA, February 25 - March 1, 2019.
    


52. Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction,
    The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), the Hilton Hawaiian Village, Honolulu, Hawaii, USA, January 28 - February 1, 2019. (both oral and poster presentation)
    


53. Haruka Yamada, Akira Imakura, Toshiyuki Imamura, Tetsuya Sakurai,
    Time-efficient tensor reordering procedures for HOTRG in distributed parallel environment,
    Tensor Network States: Algorithms and Applications (TNSAA), RIKEN Center for Computational Science (R-CCS), Kobe, Japan, December 3-6, 2018.


54. Akira Imakura, Tetsuya Sakurai,
    A complex moment-based method and its improvement for computing partial singular value decomposition,
    The 37th JSST Annual International Conference on Simulation Technology (JSST2018), Muroran Institute of Technology, Hokkaido, September 18-20, 2018.


55. Akitoshi Takayasu, Akira Imakura, Keiichi Morikuni,
    Verified computing for partial eigenvaluesusing a contour integral-type eigensolver,
    The 18th International Symposium on Scientific Computing, Computer Arithmetic, and Verified Numerical Computations (SCAN2018), Waseda Univeristy, Tokyo, September 10-15, 2018.


56. Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Symmetry-preserving of the Hankel-type Sakurai-Sugiura eigenvalue solver for large sparse Hermitian definite generalized eigenvalue problem,
    10th International Workshop on Parallel Matrix Algorithms and Applications (PMAA18), ETH Zurich, Zurich, Switzerland, June 27-29, 2018.
    


57. Yuta Inagawa, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Efficient Parallel Implementation of Spectral Nested Dissection for Large-Scale Sparse Linear System,
    10th International Workshop on Parallel Matrix Algorithms and Applications (PMAA18), ETH Zurich, Zurich, Switzerland, June 27-29, 2018.
    


58. Hongjia Chen, Akira Imakura, Tetsuya Sakurai,
    Improving numerical stability and analyzing backward error for heavily damped quadratic eigenvalue problem,
    13th SIAM East Asian Section Conference 2018 (EASIAM2018), The Univeristy of Tokyo, Tokyo, June 22-25, 2018.


59. Tetsuya Sakurai, Akira Imakura, Yasunori Futamura, Xiucai Ye,
    Matrix Computation-based Approach for Large-scale Simulation and Data Analysis,
    the second International Conference on Medical and Health Informatics 2018 (ICMHI 2018), Tsukuba, Japan, June 8-10, 2018.
    


60. Haruka Yamada, Akira Imakura, Toshiyuki Imamura, Tetsuya Sakurai,
    Optimization of Reordering Procedures in HOTRG for Distributed Parallel Computing,
    The 19th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2018), JW Marriott Parq Vancouver, Vancouver, Canada, May 25, 2018.
    


61. Akira Imakura, Tetsuya Sakurai,
    Complex Moment-Based Partial Singular Value Decomposition,
    SIAM Conference on Applied Linear Algebra (SIAM-ALA18), Hong Kong Baptist University, Hong Kong, May 4-8, 2018.
    


62. Akira Imakura, Yusaku Yamamoto,
    Efficient Implementations of the Modified Gram-Schmidt Orthogonalization with a Non-Standard Inner Product,
    SIAM Conference on Applied Linear Algebra (SIAM-ALA18), Hong Kong Baptist University, Hong Kong, May 4-8, 2018. (poster presentation)
    


63. Akira Imakura, Keiichi Morikuni, Akitoshi Takayasu,
    Verified computation of partial eigenvalues using contour integrals,
    SIAM Conference on Applied Linear Algebra (SIAM-ALA18), Hong Kong Baptist University, Hong Kong, May 4-8, 2018. (poster presentation)
    


64. Akira Imakura, Tetsuya Sakurai,
    A complex moment-based method for computing partial singular value decomposition,
    Workshop on UT/DAAD partnership program "Development of first-principles calculation codes for large scale simulations targeting materials and device design", University of Tsukuba, April 26, 2018.
    


65. Akira Imakura, Ryosuke Arai, Tetsuya Sakurai,
    A Nonlinear Semi-Nmf Based Method and Its Parallel Implementation for Deep Neural Networks,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018.
    


66. Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Recent Advances on the Algorithm-based Fault Tolerance of the Sakurai-Sugiura Eigensolver,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018.
    


67. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Shigeru Tsukamoto, Forschungszentrum Julich, Tomoya Ono,
    Contour Integral Method to Evaluate Self--Energy Matrices for Large-Scale Electron Transport Calculations,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018.
    


68. Akitoshi Takayasu, Akira Imakura, Keiichi Morikuni,
    Contour Integral-Based Verified Computing for Partial Eigenvalues,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018. (poster presentation)
    


69. Ryosuke Arai, Akira Imakura, Tetsuya Sakurai,
    A Proposal for a Nonlinear Semi-NMF Based Method with Bias Vectors and Regularization for Deep Neural Networks,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018. (poster presentation)
    


70. Haruka Yamada, Akira Imakura, Toshiyuki Imamura, Tetsuya Sakurai,
    A Parallel Implementation Technique of HOTRG for the 3D Cubic Lattice Ising Model,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP18), Waseda University, Japan, March 7-10, 2018. (poster presentation)
    


71. Takumi Yamashita, Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Algorithm development for higher order tensor renormalization group by large-scale parallel computing,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


72. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    An improvement of the block SS－Hankel method for solving Hermitian generalized eigenvalue problems,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


73. Akira Imakura,
    A novel minimal residual-like condition with collinearity for the shifted Krylov subspace methods,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


74. Hongjia Chen, Akira Imakura, Tetsuya Sakurai,
    A balancing technique for heavily damped quadratic eigenvalue problems,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


75. Ryosuke Arai, Akira Imakura, Tetsuya Sakurai,
    Nonlinear semi-NMF based method with bias vectors and regularization for deep neural networks,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


76. Haruka Yamada, Akira Imakura, Toshiyuki Imamura, Tetsuya Sakurai,
    Reducing the reordering costs in HOTRG for distributed parallel computing,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2018), EPOCHAL TSUKUBA, Tsukuba, Japan, March 5-6, 2018. (poster presentation)
    


77. Ryosuke Arai, Akira Imakura, Tetsuya Sakurai,
    An improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization for deep neural networks,
    2018 10th International Conference on Machine Learning and Computing (ICMLC 2018), University of Macau, China, February 26-28, 2018.
    


78. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Xiucai Ye,
    Development of an Eigen-analysis Engine for Large-scale Simulation and Data Analysis,
    2nd International Symposium on Research and Education of Computational Science (RECS), The University of Tokyo, Japan, December 18-19, 2017.
    


79. Shigeru Iwase, Yasunori Futamura, Akira Imakura, Tetsuya Sakurai, Tomoya Ono,
    Efficient and Scalable Calculation of Complex Band Structure Using Sakurai-Sugiura Method,
    International Conference for High Performance Computing, Networking, Storage and Analysis (SC17), Colorado Convention Center, Denver, USA, November 13-16, 2017.
    


80. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Structure-preserving of the block SS--Hankel method for solving Hermitian generalized eigenvalue problems,
    12th International Conference on Parallel Processing and Applied Mathematics (PPAM2017), Lublin, Poland, September 10-13, 2017.
    


81. Akira Imakura, Tetsuya Sakurai,
    A complex moment-based parallel eigensolver using the block communication-avoiding Arnoldi procedure,
    4th JCAHPC Seminar, The University of Tokyo, August 29, 2017.
    


82. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Shigeru Iwase, Tomoya Ono,
    Nonlinear Sakurai-Sugiura method for complex band structure calculation on Oakforest-PACS,
    4th JCAHPC Seminar, The University of Tokyo, August 29, 2017.
    


83. Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    A real-valued method for improving efficiency of a contour integral eigenvalue solver,
    2017 Meeting of the International Linear Algebra Society, Iowa State University, USA, July 24-28 2017.
    


84. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Shigeru Iwase, Tomoya Ono,
    Nonlinear Sakurai-Sugiura method for electronic transport calculation,
    2017 Meeting of the International Linear Algebra Society, Iowa State University, USA, July 24-28 2017.
    


85. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura, Shigeru Iwase, Tomoya Ono,
    Nonlinear Sakurai-Sugiura method for complex band structure calculation on KNL cluster,
    The Platform for Advanced Scientific Compuging (PASC) Conference, Palazzo dei Congressi, Lugano, Switzerland, June 26-28, 2017.
    


86. Akira Imakura, Tetsuya Sakurai,
    A complex moment-based nonlinear parallel eigensolver using the block communication-avoiding Arnoldi procedure,
    Householder Symposium XX on Numerical Linear Algebra (HHXX), The Inn at Virginia Tech, USA, June 18-23, 2017. (poster presentation)
    


87. Yasunori Futamura, Akira Imakura, Tetsuya Sakurai,
    Applications of the Parallel Complex Moment-Based Eigensolver Package z-Pares to Large-Scale Scientific Computations,
    SIAM Conference on Computational Science and Engineering (CSE17) , Hilton Atlanta, Atlanta, Georgia, USA, February 27-March 3, 2017.
    


88. Hiroki Nagakura, Wakana Iwakami, Shun Furusawa, Kohsuke Sumiyoshi, Shoichi Yamada, Hideo Matsufuru, Akira Imakura, Sherwood Richers, Christian Ott,
    Multi-Dimensional Boltzmann-Neutrino-Radiation-Hydrodynamic Simulations in Core Collapse Supernovae,
    SIAM Conference on Computational Science and Engineering (CSE17) , Hilton Atlanta, Atlanta, Georgia, USA, February 27-March 3, 2017.
    


89. 【Invited Talk】 Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Complex moment-based eigensolvers with hierarchical parallelism,
    International Workshop on Massively Parallel Programming for Quantum Chemistry and Physics 2017, RIKEN AICS, Kobe, Japan, January 9-10, 2017.
    


90. Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura,
    Alternating optimization method based on nonnegative matrix factorizations for deep neural networks,
    The 23rd International Conference on Neural Information Processing (ICONIP 2016), Kyoto, Japan, October 16-21, 2016.
    


91. Akira Imakura, Tetsuya Sakurai,
    Complex moment-based eigensolver using two Krylov subspaces,
    Sapporo Summer HPC Seminar 2016, Hokkaido University, Japan, August 22, 2016.
    


92. Akira Imakura, Tetsuya Sakurai,
    Block Krylov-type complex moment-based nonlinear eigensolver with hierarchical parallelism,
    The 9th International Workshop on Parallel Matrix Algorithms and Applications (PMAA16), Place de la Victoire, Bordeaux, France, July 6-8, 2016.
    


93. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura,
    A quadrature-based parallel eigensolver for large-scale simulations,
    The 9th International Workshop on Parallel Matrix Algorithms and Applications (PMAA16), Place de la Victoire, Bordeaux, France, July 6-8, 2016.
    


94. Kohsuke Sumiyoshi, Hiroki Nagakura, Wakana Iwakami Shun Furusawa, Hideo Matsufuru, Akira Imakura, Shoichi Yamada,
    Core-collapse supernovae explored by multi-D Boltzmann hydrodynamic simulations,
    14th International Symposium on Nuclei in the Cosmos XIV, Toki Messe, Niigata, Japan, June 19-24, 2016.
    


95. Tetsuya Sakurai, Akira Imakura, Yasunori Futamura,
    Algorithm-Based Fault Tolerance of the Sakurai-Sugiura Method,
    SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP16), Universite Pierre et Marie Curie, Cordeliers Campus, Paris, France, April 12-15, 2016.
    


96. 【Invited Talk】 Akira Imakura,
    Krylov subspace in complex moment-based eigensolver,
    Workshop on Numerical Algebra, Algorithms, and Analysis, National Institute of Informatics (NII), Tokyo, Japan, January 11-12, 2016.
    


97. Akira Imakura, Tetsuya Sakurai,
    A novel complex moment-based eigensolver using a communication-avoiding Arnoldi process,
    SIAM Conference on Applied Linear Algebra (SIAM LA15), Hyatt Regency Atranta, Atranta, Georgia, USA, October 26-30, 2015.
    


98. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura,
    A Contour Integral-based Parallel Eigensolver with Higher Complex Moments,
    SIAM Conference on Applied Linear Algebra (SIAM LA15), Hyatt Regency Atranta, Atranta, Georgia, USA, October 26-30, 2015.
    


99. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Error resilience strategy of a complex moment-based eigensolver,
    International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2015), EPOCHAL TSUKUBA, Tsukuba-city, Japan, September 14-16, 2015. (poster presentation)
    


100. Hiroto Tadano, Shusaku Saito, Akira Imakura,
     Accuracy improvement of the Shifted Block BiCGGR method for linear systems with multiple shifts and multiple right-hand sides,
     International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2015), EPOCHAL TSUKUBA, Tsukuba-city, Japan, September 14-16, 2015. (poster presentation)
     


101. Akira Imakura, Tetsuya Sakurai,
     Arnoldi-type contour integral-based eigensolver for solving nonlinear eigenvalue problems,
     ICIAM 2015, Beijing, China, August 10-14, 2015
     


102. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
     Inherent error resilience of a complex moment-based eigensolver for solving interior eigenvalue problem,
     2015 LBNL - Tsukuba Joint Meeting, LBNL, USA, May 28-29, 2015
     


103. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
     Fault tolerance inherent in a complex moment-based eigensolver,
     Sparse Solvers for Exascale: From Building Blocks to Applications, Greifswald, Germany, March 23-25, 2015. (poster presentation)
     


104. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
     Inherent error resilience of a complex moment-based eigensolver,
     SIAM Conference on Computational Science and Engineering (CSE15), Salt Lake City, Utah, USA, March 14-18, 2015
     


105. Akira Imakura, Tetsuya Sakurai,
     A novel contour integral based eigensolver for solving linear and nonlinear eigenvalue problems,
     East Asia Section of SIAM (EASIAM) 2014, Ambassador City Jomtien, Pattaya, Chonburi, Thailand, June 23-25, 2014
     


106. Tetsuya Sakurai, Yasunori Futamura, Akira Imakura,
     On error resilience of a complex moment-based eigensolver,
     10th International Workshop on Accurate Solution of Eigenvalue Problems (IWASEP10) , Dubrovnik, June 2-5, 2014.
     


107. Akira Imakura, Lei Du, Tetsuya Sakurai,
     Accuracy analysis on the Rayleigh-Ritz type of the contour integral based eigensolver,
     International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2014), EPOCHAL TSUKUBA, Tsukuba-city, Japan, March 7-9, 2014. (poster presentation)
     


108. Lei DU, Akira Imakura, Tetsuya Sakurai,
     Two-stage simultaneous band reduction for two dense symmetric matrices,
     International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2014), EPOCHAL TSUKUBA, Tsukuba-city, Japan, March 7-9, 2014. (poster presentation)
     


109. Hiroto Tadano, Akira Imakura,
     A high accuracy Block Krylov subspace method based on the bi-conjugate residual approach,
     International Workshop on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2014), EPOCHAL TSUKUBA, Tsukuba-city, Japan, March 7-9, 2014. (poster presentation)
     


110. Akira Imakura, Lei Du, Tetsuya Sakurai,
     A novel interpretation for the contour integral-based spectral projection methods for solving generalized eigenvalues problems,
     The 9th East Asia SIAM Conference - The 2nd Conference on Industrial and Applied Mathematics , The Newton Hotel, Bandung, West Java, Indonesia, June 18-20, 2013.
     


111. Lei Du, Akira Imakura, Tetsuya Sakurai,
     Band reduction for two dense symmetric matrices via congruence transformation,
     The 9th East Asia SIAM Conference - The 2nd Conference on Industrial and Applied Mathematics , The Newton Hotel, Bandung, West Java, Indonesia, June 18-20, 2013.
     


112. 【Invited Talk】 Akira Imakura, Tetsuya Sakurai, Kohsuke Sumiyoshi, Hideo Matsufuru,
     A parameter tuning technique of a weighted Jacobi-type preconditioner and its application to supernova simulations,
     Symposium: `Quarks to Universe in Computational Science (QUCS 2012)', Nara Prefectural New Public Hall, Nara, Japan, December 13-16, 2012.
     


113. Akira Imakura, Tetsuya Sakurai, Kohsuke Sumiyoshi, Hideo Matsufuru,
     An auto-tuning technique of the weighed Jacobi-type iteration used for preconditioners of Krylov subspace methods,
     IEEE 6th International Symposium on Embedded Multicore SoCs (MCSoC-12) , The University of Aizu, Aizu, Japan, September 20-22, 2012.
     


114. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     On convergence behavior of the GMRES(m) method with a hybrid restart technique,
     The 8th East Asia SIAM Conference (EASIAM 2012), National Taiwan University, Taipei, Taiwan, June 25-27, 2012.
     


115. 【Invited Talk】 Shao-Liang Zhang, Akira Imakura and Tomohiro Sogabe,
     Look-Back GMRES(m) method for solving large nonsymmetric linear systems,
     Numerical Linear Algebra - Algorithms, Applications, and Training, Delft University of Technology, Delft, Netherlands, April 10-13, 2012.
     


116. 【Invited Talk】 Shao-Liang Zhang, Akira Imakura and Tomohiro Sogabe,
     GMRES(m) method with Look-Back-type restart for solving nonsymmetric linear systems,
     The 7th International Conference on Scientific Computing and Application, Dalian P.R., China, June 13-16, 2010.
     


117. 【Invited Talk】 Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     A Look-Back technique of restart for the GMRES(m) method,
     Applied Linear Algebra - in honor of Hans Schneider, University of Novi Sad, Novi Sad, Serbia, May 24-28, 2010.
     


118. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     A study on the restart of the GMRES(m) method for solving nonsymmetric linear systems,
     International Symposium of Electronic Structure Calculations -Theory, Correlated and Large Scale Systems and Numerical Methods-, p.50, The University of Tokyo, Tokyo, Japan, December 7-9, 2009. (poster presentation)
     


119. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     A Look-Back strategy for the GMRES(m) method,
     The 7th International Conference on Numerical Optimization and Numerical Linear Algebra, p.31, Lijiang, China, August 16-19, 2009.
     


120. Shao-Liang Zhang, Akira Imakura and Tomohiro Sogabe,
     A new variant of the GMRES(m) method for solving nonsymmetric linear systems,
     The 2nd International Conference in Mathematical Modelling and Computation and The 5th East Asia SIAM Conference, P.52, Universiti Brunei Darussalam, Brunei, June 8-11, 2009.
     


121. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     An efficient variant of the GMRES(m) method based on error equations,
     Numerical Analysis and Scientific Computing with Applications (NASCA2009) , p.52, Hotel Le Tivoli, Agadir, Morocco, May 18-22, 2009.
     


122. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     A modification of implicit Wavelet sparse approximate inverse preconditioner based on a block finger pattern,
     International Symposium on Frontiers of Computational Science 2008, p.54, Nagoya University, Nagoya, Japan, November 27-29, 2008. (poster presentation)
     


123. 【The First Prize】 Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     Implicit Wavelet sparse approximate inverse preconditioners using blocked finger pattern for nonsymmetric linear systems,
     Applied Linear Algebra - in honor of Ivo Marek, p.44, University of Novi Sad, Novi Sad, Serbia, April 28-30, 2008.
     


124. Akira Imakura, Tomohiro Sogabe and Shao-Liang Zhang,
     Block algorithms of implicit Wavelet sparse approximate inverse preconditioner for nonsymmetric linear systems,
     Nagoya-COE and Beijing-LHD Joint Workshop on Frontier of Computational Science, Nagoya University, Nagoya, Japan, March 24, 2008.
     

国内会議 / Domestic Conferences

1. 【予定】 今倉暁, 山本有作, 立岡文理, 曽我部知広, 張紹良,
   DE型積分公式に基づく行列関数計算の収束性改善のための部分固有対デフレーション技術,
   RIMS共同研究 (公開型) 計算科学に資する数値解析学の展開, 京都大学, 2024 / 10/ 23-25.
   


2. 今倉暁,
   多機関分散データに対するデータコラボレーション解析,
   第2回 MfIP連携探索ワークショップ「数学を軸とする新たな連携の構築を目指して」, 大阪公立大学, 2024 / 9/ 17.
   


3. 今倉暁, 山本有作, 立岡文理, 曽我部知広, 張紹良,
   DE積分型行列関数計算法に対する部分固有対デフレーションに基づく収束性改善およびその性能評価,
   日本応用数理学会 2024年度 年会, 京都大学, 2024 / 9/ 14-16.
   


4. 瀬戸翔太, 今倉暁, 保國惠一, 高安亮紀,
   周回積分に基づく精度保証付き固有値解法の並列実装,
   2024年並列／分散／協調処理に関するサマー・ワークショップ（SWoPP 2024）, あわぎんホール　徳島県郷土文化会館, 2024 / 8/ 7-9.
   


5. 今倉暁, 山本有作, 立岡文理, 曽我部知広, 張紹良,
   DE型積分公式に基づく行列関数計算に対するデフレーションによる収束性改善,
   数値解析シンポジウム2024, 岩手大学, 2024 / 6/ 12-14.
   


6. 相原研輔, 今倉暁, 保國惠一,
   相互作用型残差スムージングにおける近似解ノルムの影響について,
   日本応用数理学会 第20回 研究部会連合発表会, 長岡技術科学大学, 2024 / 3/ 4-6.
   


7. 【依頼講演】 今倉暁,
   行列計算による機械学習：入門と応用,
   日本応用数理学会　第15回 三部会連携「応用数理セミナー」, オンライン, 2023 / 12/ 27.
   


8. 今倉暁, 櫻井鉄也,
   分散データ統合解析のためのデータコラボレーション解析技術,
   RIMS共同研究「新時代における高性能科学技術計算法の探究」, 京都大学 益川ホール, 2023 / 10/ 18-20.
   


9. 今倉暁, 相原研輔, 保國惠一,
   CGS 系統のブロッククリロフ部分空間法の枠組み,
   第４９回数値解析シンポジウム（NAS2023）, 岩手大学, 2023 / 7/ 12-14.
   


10. 今倉暁, 保國惠一, 高安亮紀,
    自己共役な微分作用素の固有値に対する周回積分型精度保証付き数値解法,
    第４９回数値解析シンポジウム（NAS2023）, 岩手大学, 2023 / 7/ 12-14.
    


11. 紀平真輝, 河又裕士, 管原侑希, 櫻井瑛一, 本村陽一, 今倉暁, 櫻井鉄也, 塚尾晶子, 久野譜也, 岡田幸彦,
    分散データに対する統合的なクラスタリングの提案：自治体住民のライフスタイルデータを題材に,
    2023年度 人工知能学会全国大会 (第37回), 熊本城ホール, 2023/6/6-9.
    


12. 豊田明広, 内立元豪, 小島真之, 香川璃奈, 大山孝, 今倉暁, 櫻井鉄也, 岡田幸彦,
    FLとDC解析における通信コストと精度の比較：筑波大学附属病院とつくば市役所の統合データ解析を想定して,
    2023年度 人工知能学会全国大会 (第37回), 熊本城ホール, 2023/6/6-9.
    


13. 関口拓海, 今倉暁, 櫻井鉄也,
    分散文章データ統合解析のためのデータコラボレーション文章解析,
    言語処理学会第29回年次大会(NLP2023), 沖縄コンベンションセンター, 2023 / 3/ 13-17.
    


14. 今倉暁, 角田亮也, 香川璃奈, 山縣邦弘, 櫻井鉄也,
    データコラボレーション解析手法に対する精度解析および生存時間分析への応用,
    日本応用数理学会 2023年研究部会連合発表会, 岡山理科大学（ハイブリッド開催）, 2023 / 3/ 8-10.
    


15. 【優秀講演賞】 相原研輔，今倉暁，保國惠一,
    ブロックLanczos型反復法の精度改善に向けた相互作用型残差スムージング,
    日本応用数理学会 2023年研究部会連合発表会, 岡山理科大学（ハイブリッド開催）, 2023 / 3/ 8-10.
    


16. 川上雄大, 高野祐一, 今倉暁,
    データコラボレーション解析における統合表現の最適化と加重法,
    情報処理学会 第85回全国大会, 電気通信大学, 2023/ 3/ 2-4.
    


17. 今倉暁, 保國惠一, 高安亮紀,
    無限次元固有値問題に対する複素モーメント型解法とその性能評価,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第34回単独研究会, オンライン, 2022/ 12/ 6.
    


18. 今倉暁, 角田亮也, 香川璃奈, 山縣邦弘, 櫻井鉄也,
    複数機関が分散保持するデータに対するデータコラボレーション生存時間分析,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第34回単独研究会, オンライン, 2022/ 12/ 6.
    


19. 相原研輔，今倉暁，保國惠一,
    後退安定な直交化を用いたブロックLanczos解法の残差ギャップについて,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第34回単独研究会, オンライン, 2022/ 12/ 6.
    


20. 山本有作，今倉暁，緒方秀教,
    量子力学の固有値問題に対する緒方の方法の加速について,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第34回単独研究会, オンライン, 2022/ 12/ 6.
    


21. 保國惠一, 今倉暁,
    線形非正方行列束の固有値問題に対する射影法,
    RIMS研究集会「数値解析が拓く次世代情報社会～エッジから富岳まで～」, 京都大学 益川ホール, 2022/ 10/ 12-14.
    


22. 川上雄大, 高野祐一, 今倉暁,
    データ統合解析における統合関数最適化問題の定式化と効率的解法,
    Tsukuba Global Science Week 2022 (TGSW2022), つくば国際会議場（ハイブリッド開催）, 2022/9/26-30.


23. 今倉暁, 保國惠一, 高安亮紀,
    一般化エルミート固有値問題に対するRayleigh-Ritz版の周回積分型精度保証付き部分固有対計算,
    日本応用数理学会 2022年度 年会, オンライン, 2022/ 9/ 8-10.
    


24. 磯田七海, アベミツテル, 福田直也, アランニャクラウス, 今倉暁, 櫻井鉄也, 藤内直道,
    機械学習と画像セグメンテーションに基づく温室栽培トマト群落の葉面積指数推定,
    園芸学会 令和4年度秋季大会, 山形大学, 2022/ 9/ 7-11.
    


25. 川上雄大, 高野祐一, 今倉暁,
    データ統合解析における統合関数最適化問題の定式化と効率的解法.
    RIMS共同研究（公開型）「数理最適化：モデル，理論，アルゴリズム」, 京都大学 数理解析研究所（ハイブリッド開催）, 2022/8/29-30.
    


26. 岡田幸彦, 罇涼稀, 河又裕士, 今倉暁, 櫻井鉄也,
    財務諸表データを学習した長期借入金増減の予測実験,
    日本会計研究学会第81回全国大会, 東京大学, 2022/ 8/ 26-28.
    


27. 今倉暁, 岡田幸彦, 櫻井鉄也
    複数機関が分散保持する秘匿データに対するデータコラボレーション解析技術,
    日本応用数理学会 第18回 研究部会連合発表会, オンライン開催, 2022/ 3/ 8-9.
    


28. 藤井智也, 坂元哲平, 渡部耕大, 安部裕之, 西村拓哉, 今倉暁, 櫻井鉄也,
    秘密保護学習技術における通信量比較および産業利用性の検討,
    第14回データ工学と情報マネジメントに関するフォーラム (DEIM2022), オンライン開催, 2022/ 2/ 27-3/ 2.
    


29. 【依頼講演】 今倉暁,
    データコラボレーション解析～複数機関が分散保持する秘匿データの安全な統合解析～,
    ものづくり企業に役立つ応用数理手法の研究会 第42回技術セミナー開催, オンライン開催, 2022/ 2/ 18.
    


30. 鈴村豊太郎, 杉木章義, 滝沢寛之, 今倉暁, 中村宏, 田浦健次朗, 工藤知宏, 塙敏博, 関谷勇司, 小林博樹, 松島慎, 空閑洋平, 中村遼, 姜仁河, 川瀬純也, 華井雅俊, 宮嵜洋, 石﨑勉, 下徳大祐, 関本義秀, 樫山武浩, 合田憲人, 竹房あつ子, 政谷好伸, 栗本崇, 笹山浩二, 北川直哉, 藤原一毅, 朝岡誠, 中田秀基, 谷村勇輔, 青木尊之, 遠藤敏夫, 森健策, 大島聡史, 深沢圭一郎, 伊達進, 天野浩文,
    データ活用社会創成プラットフォーム mdx の設計・実装・運用 ～多様な学際領域における共創に向けて～,
    大学ICT推進協議会2021年度 年次大会, オンライン開催, 2021/ 12/ 15-17.
    


31. 今倉暁,
    複数機関が持つ秘匿データの安全な統合解析技術,
    JST 新技術説明会, オンライン開催, 2021/ 10/ 21.
    


32. 岡田幸彦, 罇涼稀, 秦涼太, 今倉暁, 櫻井鉄也,
    財務諸表は長期借入金の増加予測に資するのか？,
    日本会計研究学会第80回全国大会, オンライン開催, 2021/ 9/ 8-10.
    


33. 今倉暁, 保國惠一, 高安亮紀,
    無限次元固有値問題に対する複素モーメント型解法,
    2021年並列／分散／協調処理に関するサマー・ワークショップ (SWoPP2021), オンライン開催, 2021/ 7/ 19-21.
    


34. 【依頼講演】 今倉暁,
    大規模数値シミュレーションの信頼性に対する各種リスクとその対策,
    リスク工学研究会, オンライン開催, 2021/ 7/ 12.
    


35. 今倉暁，相原研輔，保國惠一,
    複数右辺ベクトルを持つ線形方程式に対するblock generalized CGS法,
    日本応用数理学会 第17回 研究部会連合発表会, オンライン開催, 2021/ 3/ 4-5.
    


36. 相原研輔，今倉暁，保國惠一,
    Sylvester方程式に対するglobal Krylov部分空間法のresidual gap評価とその改善,
    日本応用数理学会 第17回 研究部会連合発表会, オンライン開催, 2021/ 3/ 4-5.
    


37. 山本龍宜, 横田達也, 今倉暁, 本谷秀堅,
    高階遅延埋め込み空間における低ランクテンソル補完の高速アルゴリズム,
    パターン認識・メディア理解研究会（PRMU）, オンライン開催, 2020/ 12/ 17-18.
    


38. 相原研輔，今倉暁，保國惠一,
    漸化式に着目したblock Krylov部分空間法のresidual gap評価と残差スムージング,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会 第30回研究会, オンライン開催, 2020/ 12/ 7.
    


39. 来栖壮馬, 今倉暁, 櫻井鉄也,
    エルミート一般化固有値問題に対するBlock SS-Hankel法の固有ベクトル精度改善法,
    日本応用数理学会2020年度年会, オンライン開催, 2020/ 9/ 8-10.
    


40. 伊田明弘, 今倉暁,
    カーネルリッジ回帰へのBLR行列近似の適用法検討,
    日本応用数理学会2020年度年会, オンライン開催, 2020/ 9/ 8-10.
    


41. 今倉暁, 櫻井鉄也,
    複素モーメント型部分特異値分解法,
    2020年並列／分散／協調処理に関する『福井』サマー・ワークショップ (SWoPP2020), オンライン開催, 2020/ 7/ 29-31.
    


42. Akie Nakai, Yuta Takahashi, Akira Imakura, Yukihiko Okada, Tetsuya Sakurai,
    Empirical Study of Non-Model Shared Data Collaboration Analysis Using Pseudo-data,
    サービス学会 第8回国内大会, 大阪成蹊大学, 2020/ 3/ 13. (ポスター発表)
    


43. 稲葉弘明, 今倉暁, 栗山大輔, 鎮目進一, 岡田幸彦, 櫻井鉄也,
    組織内に分散された共有不能データのデータコラボ レーション解析による活用実験,
    サービス学会 第8回国内大会, 大阪成蹊大学, 2020/ 3/ 13. (ポスター発表)
    


44. 今倉暁, 櫻井鉄也,
    Arnoldi型反復を用いたblock SS-CAA法の改良,
    日本応用数理学会 第16回 研究部会連合発表会, 中央大学 後楽園キャンパス, 2020/ 3/ 4-5.（開催中止、見なし発表）
    


45. Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai,
    Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction,
    情報系 WINTER FESTA Episode5, 一橋講堂, 2019/ 12/ 25-26. (ポスター発表)
    


46. 今倉暁, 櫻井鉄也,
    行列分解を基盤としたディープニューラルネットワーク計算法,
    RIMS研究集会「諸科学分野を結ぶ基礎学問としての数値解析学」, 京都大学数理解析研究所, 2019/ 11/ 6-8.
    


47. 櫻井鉄也, 今倉暁, 二村保徳, 叶秀彩,
    スケーラブルな固有値解析エンジンとそのAIへの展開,
    RIMS研究集会「諸科学分野を結ぶ基礎学問としての数値解析学」, 京都大学数理解析研究所, 2019/ 11/ 6-8.
    


48. 今倉暁, 二村保徳, 櫻井鉄也,
    NMF型DNN計算法とその応用,
    日本応用数理学会2019年度年会, 東京大学 駒場Iキャンパス, 2019/ 9/ 3-5.
    


49. 山本有作, 今倉暁,
    一般内積における直交化のためのMGS-HP法の誤差解析,
    日本応用数理学会2019年度年会, 東京大学 駒場Iキャンパス, 2019/ 9/ 3-5.
    


50. 与田裕之, 今倉暁, 松田萌望, 叶秀彩, 櫻井鉄也,
    最小二乗確率的分類器を用いた多峰性のあるデータに対する特異点検出,
    第48回数値解析シンポジウム（NAS2019）, AOSSA (福井県), 2019/ 6/ 10-12.
    


51. 今倉 暁, 松田萌望, 叶 秀彩, 櫻井鉄也,
    複素モーメント型部分空間を用いた教師あり次元削減法の提案,
    日本応用数理学会 第15回 研究部会連合発表会, 筑波大学, 2019/ 3/ 4-5.
    


52. 今倉 暁, 保國 惠一, 高安 亮紀,
    一般化エルミート固有値問題の部分固有値計算における周回積分に基づく精度保証法の改良,
    日本応用数理学会 第15回 研究部会連合発表会, 筑波大学, 2019/ 3/ 4-5.
    


53. 【月間ベストプレゼンテーション賞】 千葉直也, 今倉暁, 橋本浩一,
    SMW公式を用いたADMM-L1最小化問題の高速解法,
    電子情報通信学会パターン認識・メディア理解(PRMU) 研究会 (2019-02-PRMU-CNR), 徳島大学, 2019/ 2/ 28 - 3/1.
    


54. 【依頼講演】 今倉 暁,
    複素モーメント型並列固有値解法の概要と最近の進展,
    岩手数理科学セミナー, 岩手大学, 2019/ 2/ 22.
    


55. 今倉 暁, 櫻井鉄也,
    複素モーメント型部分特異値分解アルゴリズムと非線形変数変換を用いた精度改善,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会 第26回研究会, 武蔵野大学, 2018/ 11/ 28.
    


56. 今倉 暁, 松田萌望, 叶 秀彩, 櫻井鉄也,
    複素モーメント型教師あり次元削減法,
    日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5.
    


57. 今倉 暁, 保國 惠一, 高安 亮紀,
    一般化エルミート固有値問題の周回積分型精度保証付き部分固有値計算,
    日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5.
    


58. 山田 悠加, 今倉 暁, 今村 俊幸, 櫻井 鉄也,
    計算手順と配列の並び替え手順の最適化によるn次元HOTRGの計算時間の削減,
    日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5. (ポスター発表)
    


59. 【優秀ポスター賞】 松田萌望, 保國惠一, 今倉 暁, 櫻井鉄也,
    スペクトラル特徴量スケーリングの多クラス分類問題への拡張,
    日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5. (ポスター発表)
    


60. 矢野貴大, 二村保徳, 今倉 暁, 櫻井鉄也,
    反復線形ソルバを用いた大規模密一般化固有値問題向けSS-RR法の性能評価,
    日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5. (ポスター発表)
    


61. 今倉 暁, 松田萌望, 叶 秀彩, 櫻井鉄也,
    複素モーメントを利用した教師あり次元削減法の高性能化,
    本部 SSOR 2018, 水上温泉, 2018/ 8/ 29-31.
    


62. 山田 悠加, 今倉 暁, 今村 俊幸, 櫻井 鉄也,
    n次元モデル向けHOTRGの分散並列計算における配列の並び替えの最適化,
    2018年並列／分散／協調処理に関する『熊本』サマー・ワークショップ (SWoPP2018), 熊本国際交流会館, 2018/ 7/ 30- 8/ 1.
    


63. 稲川 裕太, 二村 保徳, 今倉 暁, 櫻井 鉄也,
    Intel Xeon Phiを用いたSpectral nested dissectionの性能評価,
    2018年並列／分散／協調処理に関する『熊本』サマー・ワークショップ (SWoPP2018), 熊本国際交流会館, 2018/ 7/ 30- 8/ 1.
    


64. 今倉 暁, 松田萌望, 櫻井鉄也,
    複数固有ベクトルの線形結合を用いた教師あり次元削減法,
    第33回IBISML研究会, 沖縄科学技術大学院大学, 2018/ 6/ 13-15.
    


65. 松田萌望, 保國惠一, 今倉 暁, 櫻井鉄也,
    高次元データのスペクトラルクラス分類における特徴量スケーリング,
    第33回IBISML研究会, 沖縄科学技術大学院大学, 2018/ 6/ 13-15.
    


66. 今倉 暁, 保國惠一, 高安亮紀,
    Verified Partial Eigenvalue Computation for Generalized Hermitian Eigenproblems Using Contour Integrals,
    第47回数値解析シンポジウム （NAS2018）, あわら温泉 まつや千千, 2018/ 6/ 6-8.
    


67. 松田萌望, 保國惠一, 今倉暁, 櫻井鉄也,
    多クラス分類問題に対するスペクトラル特徴量スケーリング,
    第47回数値解析シンポジウム （NAS2018）, あわら温泉 まつや千千, 2018/ 6/ 6-8.
    


68. 与田裕之, 杉原正顯, 今倉暁,
    最小二乗確率的分類器を用いた企業の信用格付,
    第47回数値解析シンポジウム （NAS2018）, あわら温泉 まつや千千, 2018/ 6/ 6-8. (ポスター発表)
    


69. 岩瀬滋, 二村保徳, 今倉暁, 櫻井鉄也A, 塚本茂B, 小野倫也,
    周回積分法を用いた電極の自己エネルギーの計算方法の提案と第一原理伝導計算への応用,
    日本物理学会　第73回年次大会, 東京理科大学, 2018/ 3/ 22-25.
    


70. 今倉 暁, 二村 保徳, 櫻井 鉄也,
    対称性を保存するblock SS-Hankel法について,
    日本応用数理学会 第14回研究部会連合発表会, 大阪大学, 2018/ 3/ 15-16.
    


71. 今倉 暁, 荒井 亮祐, 櫻井 鉄也, 野村 暢彦, 八幡 穣,
    行列分解型ディープニューラルネットワーク計算法および単一細胞解析への応用,
    日本応用数理学会 第14回研究部会連合発表会, 大阪大学, 2018/ 3/ 15-16.
    


72. 山田 悠加, 今倉 暁, 今村 俊幸, 櫻井 鉄也,
    3次元高次テンソルくりこみ群におけるテンソルのリオーダリング手順の最適化,
    日本応用数理学会 第14回研究部会連合発表会, 大阪大学, 2018/ 3/ 15-16.
    


73. 今倉 暁, 荒井 亮祐, 櫻井 鉄也, 野村 暢彦, 八幡 穣,
    行列分解型ディープニューラルネットワーク計算法の開発および単一細胞解析への応用,
    C-Airワークショップ, 筑波大学, 2018/ 2/ 22.
    


74. 今倉 暁,
    複素モーメント型並列固有値解法とその応用,
    大規模複雑データの理論と方法論，及び，関連分野への応用, 筑波大学, 2017/ 12/ 1-3.
    


75. 今倉 暁, 山本有作,
    一般内積における直交化のためのMGS-HP(s)法,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第24回研究会, 東京大学, 2017/ 11/ 24.
    


76. 関川 悠太, 二村 保徳, 今倉 暁, 櫻井 鉄也,
    多項式前処理による複数右辺ベクトルを持つシフト線形方程式の求解,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第24回研究会, 東京大学, 2017/ 11/ 24.
    


77. 【最優秀ポスター賞】 今倉 暁, 保國惠一, 高安亮紀,
    複素モーメントの誤差評価を用いた周回積分型精度保証付き部分固有値計算,
    日本応用数理学会 2017年度 年会, 武蔵野大学有明キャンパス, 2017/ 9/ 6-8. (ポスター発表)
    


78. Chen Hongjia, Akira Imakura, Tetsuya Sakurai,
    A balancing technique for improving backward error of heavily damped quadratic eigenvalue problem,
    日本応用数理学会 2017年度 年会, 武蔵野大学有明キャンパス, 2017/ 9/ 6-8. (ポスター発表)
    


79. 山田悠加, 今倉 暁, 櫻井 鉄也,
    テンソル繰り込み群計算のスパース化による高速化および精度検証,
    日本応用数理学会 2017年度 年会, 武蔵野大学有明キャンパス, 2017/ 9/ 6-8. (ポスター発表)
    


80. 荒井亮祐, 今倉 暁, 櫻井 鉄也,
    NMF型DNN計算法におけるバイアス・正則化項の導入およびその性能評価,
    日本応用数理学会 2017年度 年会, 武蔵野大学有明キャンパス, 2017/ 9/ 6-8. (ポスター発表)
    


81. 今倉 暁, 荒井亮祐, 櫻井 鉄也,
    非負値行列因子分解のためのTall-Skinny型並列実装法,
    2017年並列／分散／協調処理に関する『秋田』サマー・ワークショップ (SWoPP2017), 秋田アトリオンビル, 2017/ 7/ 26-28.
    


82. 今倉 暁, 保國惠一, 高安亮紀,
    実対称行列に対する周回積分を用いた精度保証付き部分固有値計算,
    第46回数値解析シンポジウム(NAS2017), 滋賀県グリーンパーク想い出の森, 2017/ 6/ 28-30.
    


83. 山本有作, 今倉 暁,
    一般内積における直交化のためのMGS-HP 法の誤差解析,
    第46回数値解析シンポジウム(NAS2017), 滋賀県グリーンパーク想い出の森, 2017/ 6/ 28-30.
    


84. 荒井亮祐, 今倉 暁, 櫻井 鉄也,
    行列分解型ニューラルネットワーク計算法におけるバイアス・正則化項の導入,
    第46回数値解析シンポジウム(NAS2017), 滋賀県グリーンパーク想い出の森, 2017/ 6/ 28-30.
    


85. 山田悠加, 今倉 暁, 櫻井 鉄也,
    スパース化によるテンソル繰り込み群計算の高速化,
    第46回数値解析シンポジウム(NAS2017), 滋賀県グリーンパーク想い出の森, 2017/ 6/ 28-30. (ポスター発表)
    


86. 【優秀講演賞】 今倉 暁, 井上 雄登, 櫻井 鉄也, 二村 保徳,
    非線形非負行列因子分解に基づくディープニューラルネットワーク計算法,
    日本応用数理学会 第13回研究部会連合発表会, 電気通信大学, 2017/ 3/ 6-7.
    


87. 荒井 亮祐, 今倉 暁, 櫻井 鉄也,
    正則化項を加えた制約付き非線形半非負値行列因子分解手法,
    日本応用数理学会 第13回研究部会連合発表会, 電気通信大学, 2017/ 3/ 6-7.
    


88. 山本 有作, 今倉 暁,
    一般内積に対する修正グラム・シュミット直交化の効率的実装法の誤差解析,
    日本応用数理学会 第13回研究部会連合発表会, 電気通信大学, 2017/ 3/ 6-7.
    


89. Akira Imakura, Tetsuya Sakurai, Yuto Inoue, Yasunori Futamura,
    Alternating optimization method based on nonnegative matrix factorizations for deep neural networks,
    情報系 WINTER FESTA Episode2, 一橋講堂, 2016/ 12/ 22-23. (ポスター発表)
    


90. 荒井 亮祐, 今倉 暁, 櫻井 鉄也,
    非負値行列因子分解手法の非線形問題への拡張,
    日本応用数理学会「行列・固有値問題の解法とその応用」研究部会　第22回研究会, 東京大学, 2016/ 11/ 25.
    


91. 今倉 暁, 櫻井 鉄也,
    2つのKrylov 部分空間による複素モーメント型固有値解法の改良,
    RIMS研究集会：現象解明に向けた数値解析学の新展開II, 京都大学数理解析研究所, 2016/ 10/ 19-21.
    


92. 今倉 暁, 二村 保徳, 櫻井 鉄也,
    複素モーメント型並列固有値解法の耐障害性とその性能評価,
    日本応用数理学会 2016年度 年会, 北九州国際会議場, 2016/ 9/ 12-14.
    


93. 【最優秀ポスター賞】 今倉 暁, 山本 有作,
    一般内積に対するグラム・シュミット直交化の効率的実装法の提案およびその性能評価,
    日本応用数理学会 2016年度 年会, 北九州国際会議場, 2016/ 9/ 12-14. (ポスター発表)
    


94. 井上 雄登, 櫻井 鉄也, 今倉 暁, 二村 保徳,
    非負値行列分解を用いた多層ニューラルネットワークとその並列化,
    日本応用数理学会 2016年度 年会, 北九州国際会議場, 2016/ 9/ 12-14.
    


95. Yuto Inoue, Tetsuya Sakurai, Akira Imakura, Yasunori Futamura,
    Distributed Parallel Implementation of Neural Network for Supercomputers,
    TIAかけはしポスター交流会, エポカルつくば, 2016/ 8/ 30. (ポスター発表)
    


96. Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura,
    Alternating optimization method based on nonnegative matrix factorizations for deep neural networks,
    TIAかけはしポスター交流会, エポカルつくば, 2016/ 8/ 30. (ポスター発表)
    


97. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
    Algorithm-based fault tolerance of the complex moment-based parallel eigensolver,
    TIAかけはしポスター交流会, エポカルつくば, 2016/ 8/ 30. (ポスター発表)
    


98. 今倉 暁, 山本 有作,
    一般内積に対するグラム・シュミット直交化の演算量削減およびその性能評価,
    2016年並列／分散／協調処理に関する『松本』サマー・ワークショップ (SWoPP2016), キッセイ文化ホール, 2016/ 8/ 8-10.
    


99. 長倉洋樹, 岩上わかな, 住吉光介, 古澤峻, 松古栄夫, 今倉暁, 山田章一,
    6Dボルツマン・ニュートリノ輻射流体計算による2次元超新星シミュレーション,
    日本物理学会 第71回年次大会（2016年）, 東北学院大学, 2016/ 3/ 19-22.
    


100. 今倉 暁, 櫻井 鉄也,
     2つのKrylov部分空間を利用した複素モーメント型固有値解法,
     日本応用数理学会 第12回 研究部会連合発表会, 神戸学院大学 ポートアイランドキャンパス, 2016/ 3/ 4-5.
     


101. 多田野 寛人, 齋藤 周作, 今倉 暁,
     複数右辺ベクトル・複数シフトをもつ線形方程式に対するShifted Block Krylov部分空間法の近似解の精度改善,
     【プラズマ壁相互作用における非線形現象の理論モデル構築と画像・動画解析手法開発に関する研究会】第１回非線形・可視化部門研究会, 核融合科学研究所, 2015/ 9/ 28-29.
     


102. 今倉 暁, 二村 保徳, 櫻井 鉄也,
     内部固有値問題のためのFEAST法に対するArnoldi/Lanczos型改良法の提案,
     2015年並列／分散／協調処理に関する『別府』サマー・ワークショップ (SWoPP2015), ビーコンプラザ 別府国際コンベンションセンター, 2015/ 8/ 4-6.
     


103. 齋藤 周作, 多田野 寛人, 今倉 暁,
     Shifted Block BiCGSTAB(l)法の構築とその近似解の精度について,
     第44回 数値解析シンポジウム (NAS2015), ぶどうの丘, 2015/ 6/ 8-10.
     


104. Akira Imakura, Yasunori Futamura, Tetsuya Sakurai,
     Error resilience strategy of a complex moment-based eigensolver,
     Annual Meeting on Advanced Computing System and Infrastructure (ACSI) 2015, 筑波国際会議場, 2015/ 1/ 26-28.
     


105. 今倉 暁, 杜 磊, 櫻井 鉄也,
     各種周回積分型固有値解法の関係性について,
     2014年度 RIMS 研究集会「新時代の科学技術を牽引する数値解析学」, 京都大学 数理解析研究所, 2014/ 10/ 8-10.
     


106. 長谷川 哲也, 今倉 暁, 櫻井 鉄也,
     実数計算のみを利用した周回積分型固有値解法における精度悪化とその対処,
     第8回協定講座シンポジウム, 神戸大学, 2014/ 9/ 11.
     


107. 今倉 暁, 杜 磊, 櫻井 鉄也,
     Communication-Avoiding Arnoldi版周回積分型固有値解法,
     日本応用数理学会 2014年度年会, 政策研究大学院大学, 2014/ 9/ 3-5.
     


108. 齋藤 周作, 多田野 寛人, 今倉 暁,
     Block BiCGSTAB(l)法の計算量削減,
     日本応用数理学会 2014年度年会, 政策研究大学院大学, 2014/ 9/ 3-5.
     


109. 長谷川 哲也, 今倉 暁, 櫻井 鉄也,
     実数計算のみを利用した周回積分型固有値解法における精度悪化とその対処,
     日本応用数理学会 2014年度年会, 政策研究大学院大学, 2014/ 9/ 3-5. (ポスター発表)
     


110. 今倉 暁, 櫻井 鉄也,
     周回積分型固有値解法の数理的耐故障性について,
     2014年並列／分散／協調処理に関する『新潟』サマー・ワークショップ（SWoPP新潟2014）, 朱鷺メッセ 新潟コンベンションセンター, 2014/ 7/ 28-30.
     


111. 今倉 暁, 杜 磊, 長谷川 哲也, 櫻井 鉄也,
     Hankel行列版周回積分型固有値解法の精度解析,
     第43回 数値解析シンポジウム -NAS2014-, ホテル日航八重山, 2014/ 6/ 11-13.
     


112. 齋藤 周作, 多田野 寛人, 今倉 暁,
     Block BiCGSTAB(l)法の構築と安定化,
     第10回 日本応用数理学会 研究部会連合発表会, 京都大学, 2014/ 3/ 19-20.
     


113. 蘇 黎炯, 今倉 暁, 櫻井 鉄也,
     BiCGSTAB法に対する残差のDノルム最小化手法の適用,
     第10回 日本応用数理学会 研究部会連合発表会, 京都大学, 2014/ 3/ 19-20.
     


114. 西村 恒希, 今倉 暁, 櫻井 鉄也,
     Adaptive Smoothed Aggregation マルチグリッド前処理の流体計算への適用,
     第10回 日本応用数理学会 研究部会連合発表会, 京都大学, 2014/ 3/ 19-20.
     


115. 今倉 暁, 櫻井 鉄也,
     疎行列向け直接法に基づく重み付き定常反復法,
     新学術領域研究「コンピューティクスによる物質デザイン：複合相関と非平衡ダイナミクス」平成25年度 第2回研究会, 東京大学, 2014/ 3/ 10-11. (ポスター発表)
     


116. 多田野 寛人, 今倉 暁,
     複数右辺ベクトルを持つ連立一次方程式に対する双共役残差型アプローチに基づく高精度Block Krylovアルゴリズムについて,
     新学術領域研究「コンピューティクスによる物質デザイン：複合相関と非平衡ダイナミクス」平成25年度 第2回研究会, 東京大学, 2014/ 3/ 10-11. (ポスター発表)
     


117. 【依頼講演】 今倉 暁, 杜 磊, 櫻井 鉄也,
     Rayleigh-Ritz法に基づく周回積分型固有値解法の精度解析,
     Kunitachi One-Day Symposium on Applied Mathematics and Related Topics, 一橋大学国立キャンパス, 2014/ 2/ 5.
     


118. 今倉 暁,
     疎行列向け直接解法に基づく定常反復法および前処理としての有効性,
     環瀬戸内応用数理研究部会 第17回シンポジウム, 愛媛大学, 2014/ 1/ 11-12.
     


119. 今倉 暁, 杜 磊, 櫻井 鉄也,
     周回積分型固有値解法に対する精度解析,
     日本応用数理学会「行列・固有値問題の解法とその応用」研究部会 第16回研究会, 東京大学, 2013/ 12/ 26.
     


120. 【優秀講演賞】 今倉 暁, 二村 保徳, 櫻井 鉄也
     周回積分型固有値解法に基づく並列固有値解析ソフトウェア,
     今後の HPC(基盤技術と応用) に関するワークショップ, 長崎市図書館, 2013/ 12/ 8-9.
     


121. Lei DU, Akira IMAKURA, Tetsuya SAKURAI,
     Simultaneous Band Reduction of Two Symmetric Matrices and its Applications,
     RIMS研究集会「応用数理と計算科学における理論と応用の融合」, 京都大学 数理解析研究所, 2013/ 10/ 15-17.
     


122. 【依頼講演】 今倉 暁,
     拡張Krylov部分空間に対する2ステップ基底生成法,
     第２回岐阜数理科学研究会, 飛騨高山まちの博物館, 2013/ 9/ 16-18.
     


123. 今倉 暁, 杜 磊, 櫻井 鉄也,
     周回積分型固有値解法に対するBlock Arnoldi法に基づく新解釈および改良法の提案,
     日本応用数理学会2013年度年会, アクロス福岡, 2013/ 9/ 9-11. (ポスター発表)
     


124. Lei DU, Akira IMAKURA, Tetsuya SAKURAI,
     Reducing Two Symmetric Matrices to Band Form by Congruence Transformations,
     日本応用数理学会2013年度年会, アクロス福岡, 2013/ 9/ 9-11.
     


125. 多田野 寛人, 石川 陽一, 今倉 暁,
     双共役残差型反復解法の複数右辺ベクトル対応版への拡張と性能評価,
     日本応用数理学会2013年度年会, アクロス福岡, 2013/ 9/ 9-11.
     


126. 【依頼講演】 今倉 暁,
     線形方程式の数値解法と科学技術計算への応用,
     2013年度 数値線形代数研究集会, 東京理科大学　大子研修センター, 2013/ 8/ 28-30.
     


127. 今倉 暁, 杜 磊, 櫻井 鉄也,
     周回積分型固有値解法に対するBlock Krylov部分空間に基づく新解釈,
     2013年並列／分散／協調処理に関する 『北九州』サマー・ワークショップ（SWoPP北九州2013）, 北九州国際会議場, 2013/ 7/ 31 - 8/ 2.
     


128. 今倉 暁, 杜 磊, 櫻井 鉄也,
     一般化固有値問題に対する周回積分射影法の新しい解釈について,
     第42回数値解析シンポジウム -NAS2013-, 四国道後舘, 2013/ 6/ 12-14.
     


129. Lei Du, Akira Imakura, Tetsuya Sakurai,
     Band reduction for a pair of symmetric matrices with applications,
     第42回数値解析シンポジウム -NAS2013-, 四国道後舘, 2013/ 6/ 12-14. (ポスター発表)
     


130. Lei Du, Akira Imakura, Tetsuya Sakurai,
     Simultaneous band reduction of two symmetric matrices,
     日本応用数理学会 2013年 研究部会 連合発表会, 東洋大学, 2013/ 3/ 14-15.
     


131. 【招待講演】 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
     大規模連立一次方程式に対する高並列前処理技術について,
     宇宙磁気流体・プラズマシミュレーションワークショップ --WS2013--, 千葉大学, 2013/ 2/ 18-19.
     


132. 【依頼講演】 今倉 暁,
     Extended Krylov部分空間に対する新しい基底生成アルゴリズムの提案,
     2012年 若手の会 単独研究会, 東京大学, 2012/ 12/ 26.
     


133. 今倉 暁, 杜 磊, 多田野 寛人,
     複数右辺ベクトルを持つ線形方程式に対するWeighted Block GMRES法,
     行列・固有値研究部会 第14回研究会, 筑波大学東京キャンパス, 2012/ 11/ 20.
     


134. 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
     重み付き定常反復型前処理のためのパラメータ最適化手法および超新星爆発計算における有効性,
     RIMS研究集会「次世代計算科学の基盤技術とその展開」, pp. 4-6, 京都大学 数理解析研究所, 2012/ 10/ 23 - 25.
     


135. 【若手優秀講演賞】 今倉 暁,
     Extended Krylov部分空間に対する効率的基底生成法,
     日本応用数理学会 2012年度 年会, 稚内全日空ホテル, 2012/ 8/ 28 - 9/ 2.
     


136. 【依頼講演】 今倉 暁,
     大規模線形方程式のための減速定常反復型前処理付きKrylov部分空間法およびその自動チューニング技法,
     芝浦工業大学 数理科学科 談話会, 芝浦工業大学, 2012/ 7/ 11.
     


137. 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
     減速Jacobi型前処理に対するパラメータ最適化手法の提案および超新星爆発計算への適用,
     京コンピュータ・シンポジウム2012および第2回戦略プログラム5分野合同WS, 神戸大学 統合研究拠点コンベンションホール, 2012/ 6/ 14-15. (ポスター発表)
     


138. 【依頼講演】 今倉 暁,
     シフト線形方程式に対するRestarted Shifted GMRES法およびその改良法について,
     線形計算研究会 (NLA) , 東京大学, 2012/ 3/ 23.
     


139. 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
     超新星爆発計算のための減速Jacobi型前処理に対するパラメータ最適化手法について,
     HPCI戦略プログラム分野５「物質と宇宙の起源と構造」全体シンポジウム, 秋葉原コンベンションホール, 2012/ 3/ 7-8. (ポスター発表)
     


140. 今倉 暁, 櫻井 鉄也,
     大規模並列化を考慮した減速Jacobi型前処理の有効性,
     第1回協定講座シンポジウム 「計算アルゴリズムと化学・生物学の融合」 , 神戸大学, 2012/ 2/ 17. (ポスター発表)
     


141. 今倉 暁, 櫻井 鉄也, 住吉 光介, 松古 栄夫,
     超新星爆発計算のための減速Jacobi型前処理,
     研究会「素核宇融合による計算基礎物理学の進展」, 合歓の郷, 2011/ 12/ 3-5.
     


142. 今倉 暁, 櫻井 鉄也,
     減速定常反復型前処理付きKrylov部分空間法のための減速パラメータ推定法,
     行列・固有値研究部会 第12回研究会, 国立情報学研究所, 2011/ 11/ 21.
     


143. 今倉 暁, 曽我部 知広, 張 紹良,
     シフト線形方程式に対するリスタート付きKrylov部分空間法,
     RIMS研究集会「科学技術計算における理論と応用の新展開」, 京都大学 数理解析研究所, 2011/ 10/ 25-27.
     


144. 【依頼講演】 今倉 暁,
     線形方程式に対するGMRES(m)法のリスタートに着目した改良法について,
     東京大学 数値解析セミナー (UTNAS) , 東京大学, 2011/ 10/ 18.
     


145. 今倉 暁, 曽我部 知広, 張 紹良,
     Restarted Shifted GMRES法の収束の振る舞いに関する考察 -- 収束の安定化に向けて --,
     日本応用数理学会 2011年度 年会, pp.89-90, 同志社大学, 2011/ 9/ 14-16.
     


146. 今倉 暁, 曽我部 知広, 張 紹良,
     シフト線形方程式に対するリスタート付きKrylov部分空間法のための新しい最小残差条件,
     2011年度 数値解析研究集会, 少年自然の家 八ヶ岳荘, 2011/ 9/ 5-7. (ポスター発表)
     


147. 今倉 暁, 曽我部 知広, 張 紹良,
     Restarted Shifted GMRES法の収束の安定化に向けての試み,
     2011年並列／分散／協調処理に関する『鹿児島』サマー・ワークショップ（SWoPP鹿児島2011）, かごしま県民交流センター, 2011/ 7/ 27-29.
     


148. 【依頼講演】 今倉 暁,
     非対称線形方程式に対するGMRES(m)法のリスタートおよびLook-Back戦略に基づく改良法について,
     神戸大学計算科学セミナー , 神戸大学, 2011/ 7/ 14.
     


149. 今倉 暁,
     Restarted Shifted GMRES法に対するLook-Back戦略の適用,
     第40回数値解析シンポジウム -NAS2011-, pp.125-128, 鳥羽シーサイドホテル, 2011/ 6/ 20-22.
     


150. 楊 済栄, 今倉 暁, 曽我部 知広, 張 紹良,
     デフレーションを用いたGMRES(m)法に対するLOOK-BACK型のリスタートの適用,
     日本応用数理学会「行列・固有値の解法とその応用」研究部会 第10回研究会, 国立情報学研究所, 2010/ 11/ 24.
     


151. 今倉 暁, 曽我部 知広, 張 紹良,
     Look-Back GMRES(m)法の収束の振る舞いについて,
     日本応用数理学会 2010年度 年会, pp.25-26, 明治大学, 2010/ 9/ 6-9.
     


152. 今倉 暁, 曽我部 知広, 張 紹良,
     Restarted Shifted GMRES(m)法のリスタートについて,
     2010年度数値解析研究集会, 国立信州高遠少年自然の家, 2010/ 8/ 30 - 9/ 1. (ポスター発表)
     


153. 今倉 暁, 曽我部 知広, 張 紹良,
     非対称線形方程式のためのLook-Back GMRES(m)法　―誤差方程式に基づくGMRES(m)法の拡張―,
     日本応用数理学会 2010年 研究部会 連合発表会, 筑波大学, 2010/ 3/ 8-9.
     


154. 今倉 暁, 曽我部 知広, 張 紹良,
     GMRES(m)法のリスタートに対する考察およびその拡張,
     日本応用数理学会「行列・固有値の解法とその応用」研究部会 第８回研究会, 国立情報学研究所, 2009/ 11/ 26.
     


155. 今倉 暁, 曽我部 知広, 張 紹良,
     GMRES(m)法の拡張およびその解析,
     第７回計算数学研究会, 裏磐梯ロイヤルホテル, 2009/ 10/ 16-18.
     


156. 早戸 拓也, 今倉 暁, 曽我部 知広, 張 紹良,
     離散wavelet変換を用いた正定値対称行列のためのFSAI前処理,
     日本応用数理学会 2009年度 年会, pp.289-290, 大阪大学, 2009/ 9/ 28-30.
     


157. 今倉 暁, 曽我部 知広, 張 紹良,
     非対称線形方程式のためのGMRES(m)法に対するLook-Back型の戦略,
     2009年度数値解析研究集会, 国立信州高遠少年自然の家, 2009/ 9/ 1-3. (ポスター発表)
     


158. 今倉 暁, 曽我部 知広, 張 紹良,
     残差多項式に基づいたLook-Back GMRES(m)法に対する解析,
     第38回数値解析シンポジウム -NAS2009-, pp.37-40, 熱川ハイツ, 2009/ 6/ 15-17.
     


159. 今倉 暁, 曽我部 知広, 張 紹良,
     Look-Back型のGMRES(m)法について,
     日本応用数理学会 平成21年 研究部会 連合発表会, 京都大学, 2009/ 3/ 7-8.
     


160. 今倉 暁, 曽我部 知広, 張 紹良,
     GMRES(m)法と反復改良法の数理的つながり,
     日本応用数理学会 2008年度 年会, pp.407-408, 東京大学, 2008/ 9/ 17-19.
     


161. 【分野別最優秀賞】 今倉 暁, 曽我部 知広, 張 紹良,
     非対称線形方程式のためのGMRES(m)法と反復改良法の関係性について,
     第３回 計算科学フロンティアフォーラム, 東京ガーデンパレス, 2008/ 9/ 9. (ポスター発表)
     


162. 今倉 暁, 曽我部 知広, 張 紹良,
     非対称線形方程式のためのGMRES(m)法のリスタートに対する考察,
     2008年度数値解析研究集会, 国立信州高遠少年自然の家, 2008/ 9/ 1-3.
     


163. 今倉 暁, 曽我部 知広, 張 紹良,
     非対称線形方程式のためのGMRES(m)法と反復改良法の関係性について,
     計算科学夏の学校2008, 料理旅館　紅葉屋, 2008/ 8/ 27-29. (ポスター発表)
     


164. 今倉 暁, 曽我部 知広, 張 紹良,
     GMRES(m)法のリスタートについて,
     第37回数値解析シンポジウム -NAS2008-, pp.21-24, たざわこ芸術村 温泉ゆぽぽ, 2008/ 6/ 12-14.
     


165. Akira IMAKURA, Tomohiro SOGABE and Shao-Liang ZHANG,
     Implicit Wavelet Sparse Approximate Inverse Preconditioner using Blocked Finger Pattern for Nonsymmetric Linear Systems,
     COE「計算科学フロンティア」アルゴリズム部門国内シンポジウム 超多自由度系の解析／最適化に向けたアルゴリズムの進展, 名古屋大学, 2008/ 2/ 15.
     


166. 今倉 暁, 曽我部 知広, 張 紹良,
     陰的wavelet近似逆行列前処理におけるfinger patternのブロック化およびその解析,
     2007年度数値解析研究集会, 諏訪東京理科大学, 2007/ 9/ 4-6.
     


167. 今倉 暁, 曽我部 知広, 張 紹良,
     陰的wavelet近似逆行列前処理のfinger patternおよびそのブロック化技法,
     日本応用数理学会 環瀬戸内応用数理研究部会 第11回シンポジウム, pp.38-41, 岡山理科大学, 2007/ 7/ 6-7.
     


168. 今倉 暁, 曽我部 知広, 張 紹良,
     陰的wavelet近似逆行列前処理のFinger patternに対するブロック化技法について,
     第36回数値解析シンポジウム -NAS2007-, pp.109-112, ウェルシティ湯河原, 2007/ 6/ 19-21.
     


169. 今倉 暁, 曽我部 知広, 張 紹良,
     Finger patternのブロック化による陰的wavelet近似逆行列前処理の高速化,
     研究部会「超大規模行列の数理的諸問題とその高速解法」, 東京大学, 2007/ 3/ 7.
     


170. 今倉 暁, 曽我部 知広, 張 紹良,
     Finger patternのブロック化による陰的wavelet近似逆行列前処理の高速化,
     日本応用数理学会 2007年 研究部会 連合発表会, 名古屋大学, 2007/ 3/ 3-4.
     


171. 今倉 暁, 曽我部 知広, 張 紹良,
     Block finger-patternを用いた陰的wavelet近似逆行列前処理,
     2007年ハイパフォーマンスコンピューティングと計算科学シンポジウム (HPCS2007), p.62. つくば国際会議場, 2007/ 1/ 17-18. (ポスター発表)
     

Awards

---

受賞 / Awards

1. 日本医療情報学会 第6回学術論文賞（2023年度）, [賞状], [Web page],
   

   Akira Imakura, Ryoya Tsunoda, Rina Kagawa, Kunihiro Yamagata, Tetsuya Sakurai, DC-COX: data collaboration Cox proportional hazards model for privacy-preserving survival analysis on multiple parties, Journal of Biomedical Informatics, Volume 137, 104264, 2023.



2. 日本応用数理学会 2023年研究部会連合発表会 優秀講演賞（受賞者：相原研輔）, [Web page],
   

   相原研輔，今倉暁，保國惠一, ブロックLanczos型反復法の精度改善に向けた相互作用型残差スムージング, 日本応用数理学会 2023年研究部会連合発表会, 岡山理科大学（ハイブリッド開催）, 2023 / 3/ 8-10.



3. Best Student Paper Award（学生筆頭著者：白守鉉）, [賞状],
   

   Suhyeon Baek, Akira Imakura, Tetsuya Sakurai, Ichiro Kataoka, Accelerating the Backpropagation algorithm by Using the NMF-based method on Deep Neural Networks, Proceedings of 2020 Principle and Practice of Data and Knowledge Acquisition Workshop (PKAW2020), (accepted).



4. Best Presentation Award（受賞者：与田裕之）,

   Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya, Sakurai, Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis, 2020 12th International Conference on Machine Learning and Computing (ICMLC 2020), Online, June 19-21, 2020.



5. Best Paper Finalist, [賞状],
   

   Naoya Chiba, Akira Imakura, Koichi Hashimoto, Fast ADMM l1 minimization by applying SMW formula and multi-row simultaneous estimation for Light Transport Matrix acquisition, Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), (accepted).



6. Best Paper Finalist, [賞状],
   

   Naoya Chiba, Mingyu Li, Akira Imakura, Koichi Hashimoto, Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation, Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO2019), (accepted).



7. 2018年度PRMU月間ベストプレゼンテーション賞（受賞者：千葉直也）, [Web page],
   

   千葉直也, 今倉暁, 橋本浩一, SMW公式を用いたADMM-L1最小化問題の高速解法, 電子情報通信学会パターン認識・メディア理解(PRMU) 研究会 (2019-02-PRMU-CNR), 徳島大学, 2019/ 2/ 28 - 3/1.



8. 日本応用数理学会 2018年度年会 優秀ポスター賞（受賞者：松田萌望）, [Web page],
   

   松田萌望, 保國惠一, 今倉 暁, 櫻井鉄也, スペクトラル特徴量スケーリングの多クラス分類問題への拡張, 日本応用数理学会 2018年度 年会, 名古屋大学, 2018/ 9/ 3-5. (ポスター発表)



9. 日本応用数理学会 2017年度年会 最優秀ポスター賞（受賞者：高安亮紀）, [Web page],
   

   今倉 暁, 保國惠一, 高安亮紀, 複素モーメントの誤差評価を用いた周回積分型精度保証付き部分固有値計算, 日本応用数理学会 2017年度 年会, 武蔵野大学有明キャンパス, 2017/ 9/ 6-8. (ポスター発表)



10. 日本応用数理学会 2017年研究部会連合発表会 優秀講演賞, [賞状], [Web page],
    

    今倉 暁, 井上 雄登, 櫻井 鉄也, 二村 保徳, 非線形非負行列因子分解に基づくディープニューラルネットワーク計算法, 日本応用数理学会 第13回研究部会連合発表会, 電気通信大学, 2017/ 3/ 6-7.



11. 日本応用数理学会 2016年度年会 最優秀ポスター賞, [賞状], [Web page],
    

    今倉 暁, 山本 有作 一般内積に対するグラム・シュミット直交化の効率的実装法の提案およびその性能評価, 日本応用数理学会 2016年度 年会, 北九州国際会議場, 2016/ 9/ 12-14. (ポスター発表)



12. 優秀講演賞, [賞状],

    今倉 暁, 二村 保徳, 櫻井 鉄也 周回積分型固有値解法に基づく並列固有値解析ソフトウェア, 今後の HPC(基盤技術と応用) に関するワークショップ, 長崎市図書館, 2013/ 12/ 8-9.



13. 日本応用数理学会 第9回 若手優秀講演賞（2012年度）, [賞状], [Web page],
    

    今倉 暁, Extended Krylov部分空間に対する効率的基底生成法, 日本応用数理学会 2012年度 年会, 稚内全日空ホテル, 2012/ 8/ 28 - 9/ 2.



14. 平成23年度 日本応用数理学会論文賞（応用部門）, [賞状], [Web page],
    

    則竹 渚宇, 今倉 暁, 山本 有作, 張 紹良, 行列の指数関数に基づく連立線形常微分方程式の大粒度並列化解法とその評価, 日本応用数理学会論文誌, Vol.19, No.3, 2009, pp.293-312.



15. 平成22年度 日本応用数理学会論文賞（ノート部門）, [賞状], [Web page],
    

    今倉 暁, 曽我部 知広, 張 紹良, GMRES(m)法のリスタートについて, 日本応用数理学会論文誌, Vol.19, No.4, 2009, pp.551-564.



16. 分野別最優秀賞（アルゴリズム・CAE・一般）, [賞状],
    

    今倉 暁, 曽我部 知広, 張 紹良, 非対称線形方程式のためのGMRES(m)法と反復改良法の関係性について, 第３回 計算科学フロンティアフォーラム, 2008/ 9/ 9.



17. The first prize for the best presentation among young participants, [Report from IMAGE 40],
    

    Akira IMAKURA, Tomohiro SOGABE and Shao-Liang ZHANG, Implicit Wavelet Sparse Approximate Inverse Preconditioners using Blocked Finger Pattern for Nonsymmetric Linear Systems, Applied Linear Algebra - in honor of Ivo Marek, April 28-30, 2008.

Patents

---

知財 / Patents

1. 特許第7527627号,

   発明の名称：分散データ処理装置、端末、分散データ処理プログラム、端末制御プログラム、分散データ処理方法及び端末制御方法 特許権者：国立大学法人 筑波大学 発明者：今倉暁、櫻井鉄也、稲葉弘明、岡田幸彦 出願番号：特願2020-127477 出願日：2020/7/28 登録日：2024/7/26



2. Patent No. US 11,934,558 B2,

   Title：Distributed data integration device, distributed data integration method, and program Assignee：University of Tsukuba Inventor：Akira Imakura, Tetsuya Sakurai Application No.：17/309,845 Data of Application：2019/12/18 Data of Publication：2024/3/19



3. 特許第7302851号,

   発明の名称：特徴量選択支援装置、特徴量選択支援プログラム及び特徴量選択支援方法 特許権者：国立大学法人 筑波大学 発明者：今倉暁、叶秀彩、櫻井鉄也 出願番号：特願2019-104396 出願日：2019/6/4 登録日：2023/6/26



4. 特許第7209378号,

   発明の名称：分散データ統合装置、分散データ統合解析装置、分散データ統合方法、及びプログラム 特許権者：国立大学法人 筑波大学 発明者：今倉暁、櫻井鉄也 出願番号：特願2020-563129 出願日：2019/12/18 登録日：2023/1/12