Rio Yokota

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 83

Top Venues

CoRR

Int. J. High Perform. Comput. Appl.

ICCV

CVPR

Publications

Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Hiroki Iida, Masanari Ohi, Kakeru Hattori, Hirai Shota, Sakae Mizuki, Rio Yokota, Naoaki Okazaki
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities. CoRR (2024)
Satoki Ishikawa, Rio Yokota
When Does Second-Order Optimization Speed Up Training? Tiny Papers @ ICLR (2024)
Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota
DGEMM on integer matrix multiplication unit. Int. J. High Perform. Comput. Appl. 38 (4) (2024)
Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Minh Chien Vu, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin P. Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Moustafa-Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order. CoRR (2024)
Qianxiang Ma, Rio Yokota
-ULV factorization for solving dense linear systems on GPUs. Int. J. High Perform. Comput. Appl. 38 (4) (2024)
Naoaki Okazaki, Kakeru Hattori, Hirai Shota, Hiroki Iida, Masanari Ohi, Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Rio Yokota, Sakae Mizuki
Building a Large Japanese Web Corpus for Large Language Models. CoRR (2024)
Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
Variational Learning is Effective for Large Deep Networks. CoRR (2024)
Kenta Niwa, Hiro Ishii, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota
Natural Gradient Primal-Dual Method for Decentralized Learning. IEEE Trans. Signal Inf. Process. over Networks 10 (2024)
Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs. CoRR (2024)
Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. Trans. Mach. Learn. Res. 2023 (2023)
Hiroyuki Ootomo, Rio Yokota
Mixed-Precision Random Projection for RandNLA on Tensor Cores. CoRR (2023)
Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota
Pixel-level Contrastive Learning of Driving Videos with Optical Flow. CVPR Workshops (2023)
Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. ICCV (2023)
M. Ridwan Apriansyah, Rio Yokota
Computing the k-th Eigenvalue of Symmetric H2-Matrices. ICPP (2023)
Damian W. I. Rouson, Konrad Hinsen, Jeffrey C. Carver, Irina Tezaur, John Shalf, Rio Yokota, Anshu Dubey
The 2023 Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering. Comput. Sci. Eng. 25 (2) (2023)
Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, Torsten Hoefler
ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. CoRR (2023)
Shaoshuai Zhang, Ruchi Shah, Hiroyuki Ootomo, Rio Yokota, Panruo Wu
Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core. PPoPP (2023)
Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota
Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection. CoRR (2023)
Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. CoRR (2023)
Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)
Hiroyuki Ootomo, Rio Yokota
Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library. CoRR (2023)
M. Ridwan Apriansyah, Rio Yokota
-Matrices. CoRR (2023)
Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota
Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection. ISC (2023)
Sameer Deshmukh, Rio Yokota, George Bosilca
Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors. ACM Trans. Math. Softw. 49 (3) (2023)
Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue
Pre-training Vision Transformers with Very Limited Synthesized Images. CoRR (2023)
Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota
Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves. CoRR (2023)
Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota
DGEMM on Integer Matrix Multiplication Unit. CoRR (2023)
Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota
Visual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves. CVPR (2023)
Sameer Deshmukh, Qinxiang Ma, Rio Yokota, George Bosilca
O(N) distributed direct factorization of structured dense matrices using runtime systems. CoRR (2023)
Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue
Pre-training Vision Transformers with Very Limited Synthesized Images. ICCV (2023)
Sameer Deshmukh, Rio Yokota, George Bosilca, Qianxiang Ma
O(N) distributed direct factorization of structured dense matrices using runtime systems. ICPP (2023)
Sameer Deshmukh, Rio Yokota, George Bosilca
Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors. CoRR (2023)
Hiroyuki Ootomo, Rio Yokota
Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library. HPC Asia (2023)
Edgar Josafat Martinez-Noriega, Rio Yokota
Towards real-time formula driven dataset feed for large scale deep learning training. High Performance Computing for Imaging (2023)
Hiroyuki Ootomo, Rio Yokota
Mixed-Precision Random Projection for RandNLA on Tensor Cores. PASC (2023)
Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota
Informative Sample-Aware Proxy for Deep Metric Learning. CoRR (2022)
Hiroyuki Ootomo, Rio Yokota
Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. CoRR (2022)
Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. CoRR (2022)
Hiroyuki Ootomo, Rio Yokota
Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. Int. J. High Perform. Comput. Appl. 36 (4) (2022)
Qianxiang Ma, Sameer Deshmukh, Rio Yokota
Scalable Linear Time Dense Direct Solver for 3-D Problems Without Trailing Sub-Matrix Dependencies. CoRR (2022)
Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. ICRA (2022)
Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota
Informative Sample-Aware Proxy for Deep Metric Learning. MMAsia (2022)
M. Ridwan Apriansyah, Rio Yokota
Parallel QR Factorization of Block Low-Rank Matrices. CoRR (2022)
Qianxiang Ma, Sameer Deshmukh, Rio Yokota
Scalable Linear Time Dense Direct Solver for 3-D Problems without Trailing Sub-Matrix Dependencies. SC (2022)
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44 (1) (2022)
Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
Replacing Labeled Real-image Datasets with Auto-generated Contours. CoRR (2022)
Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
Replacing Labeled Real-image Datasets with Auto-generated Contours. CVPR (2022)
M. Ridwan Apriansyah, Rio Yokota
Parallel QR Factorization of Block Low-rank Matrices. ACM Trans. Math. Softw. 48 (3) (2022)
Satoshi Ohshima, Akihiro Ida, Rio Yokota, Ichitaro Yamazaki
QR Factorization of Block Low-Rank Matrices on Multi-instance GPU. PDCAT (2022)
Tingyu Wang, Rio Yokota, Lorena A. Barba
ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces. J. Open Source Softw. 6 (61) (2021)
Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. CoRR (2021)
Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani
RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation. CoRR (2021)
Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering. ICCV (2021)
Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota
Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. KDD (2020)
Kento Doi, Ryuhei Hamaguchi, Shun Iwase, Rio Yokota, Yutaka Matsuo, Ken Sakurada
Epipolar-Guided Deep Object Matching for Scene Change Detection. CoRR (2020)
Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. HPC Asia (2020)
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. CoRR (2020)
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. CVPR (2019)
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
Practical Deep Learning with Bayesian Principles. CoRR (2019)
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
Practical Deep Learning with Bayesian Principles. NeurIPS (2019)
Akihiro Ida, Hiroshi Nakashima, Tasuku Hiraishi, Ichitaro Yamazaki, Rio Yokota, Takeshi Iwashita
QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition. J. Inf. Process. 27 (2019)
Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. ICPP Workshops (2019)
Yuichiro Ueno, Rio Yokota
Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs. CCGRID (2019)
Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. CoRR (2019)
Ichitaro Yamazaki, Akihiro Ida, Rio Yokota, Jack J. Dongarra
Distributed-memory lattice H-matrix factorization. Int. J. High Perform. Comput. Appl. 33 (5) (2019)
Hiroki Naganuma, Rio Yokota
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. CCGRID (2019)
Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes
Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. SIAM J. Sci. Comput. 41 (3) (2019)
Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota
Optimization of Numerous Small Dense-Matrix-Vector Multiplications in H-Matrix Arithmetic on GPU. MCSoC (2019)
Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota
Optimization of Hierarchical Matrix Computation on GPU. SCFA (2018)
Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes
Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. CoRR (2018)
Ichitaro Yamazaki, Ahmad Abdelfattah, Akihiro Ida, Satoshi Ohshima, Stanimire Tomov, Rio Yokota, Jack J. Dongarra
Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters. IPDPS (2018)
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. CoRR (2018)
Huda Ibeid, Rio Yokota, Jennifer Pestana, David E. Keyes
Fast multipole preconditioners for sparse matrices arising from elliptic equations. Comput. Vis. Sci. 18 (6) (2018)
Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Rio Yokota, David E. Keyes
Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture. Euro-Par (2017)
Kazuki Osawa, Rio Yokota
Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. ICANN (2) (2017)
Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. ISC (2017)
Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. HPCS (2017)
Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. CoRR (2017)
Abdelhalim Amer, Satoshi Matsuoka, Miquel Pericàs, Naoya Maruyama, Kenjiro Taura, Rio Yokota, Pavan Balaji
Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures. IWOMP (2016)
Rio Yokota, Huda Ibeid, David E. Keyes
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation. CoRR (2016)
Huda Ibeid, Rio Yokota, David E. Keyes
A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method. CoRR (2016)
Keisuke Fukuda, Motohiko Matsuda, Naoya Maruyama, Rio Yokota, Kenjiro Taura, Satoshi Matsuoka
Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms. ICPADS (2016)
Huda Ibeid, Rio Yokota, David E. Keyes
A performance model for the communication in fast multipole methods on high-performance computing platforms. Int. J. High Perform. Comput. Appl. 30 (4) (2016)
Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami
Scalable Fast Multipole Accelerated Vortex Methods. IPDPS Workshops (2014)
Rio Yokota, George Turkiyyah, David E. Keyes
Communication Complexity of the Fast Multipole Method and its Algebraic Variants. Supercomput. Front. Innov. 1 (1) (2014)
Hatem Ltaief, Rio Yokota
Data-driven execution of fast multipole methods. Concurr. Comput. Pract. Exp. 26 (11) (2014)
Mustafa Abdul Jabbar, Rio Yokota, David E. Keyes
Asynchronous Execution of the Fast Multipole Method Using Charm++. CoRR (2014)
Rio Yokota, George Turkiyyah, David E. Keyes
Communication Complexity of the Fast Multipole Method and its Algebraic Variants. CoRR (2014)
Yousuke Ohno, Rio Yokota, Hiroshi Koyama, Gentaro Morimoto, Aki Hasegawa, Gen Masumoto, Noriaki Okimoto, Yoshinori Hirano, Huda Ibeid, Tetsu Narumi, Makoto Taiji
Petascale molecular dynamics simulation using the fast multipole method on K computer. Comput. Phys. Commun. 185 (10) (2014)
Huda Ibeid, Rio Yokota, David E. Keyes
A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms. CoRR (2014)
Rio Yokota, Jennifer Pestana, Huda Ibeid, David E. Keyes
Fast Multipole Preconditioners for Sparse Matrices Arising from Elliptic Equations. CoRR (2013)
Abdelhalim Amer, Naoya Maruyama, Miquel Pericàs, Kenjiro Taura, Rio Yokota, Satoshi Matsuoka
Fork-Join and Data-Driven Execution Models on Multi-core Architectures: Case Study of the FMM. ISC (2013)
Rio Yokota, Lorena A. Barba, Tetsu Narumi, Kenji Yasuoka
Petascale turbulence simulation using a highly parallel fast multipole method on GPUs. Comput. Phys. Commun. 184 (3) (2013)
Rio Yokota
An FMM Based on Dual Tree Traversal for Many-core Architectures CoRR (2012)
Rio Yokota, Lorena A. Barba
Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems. Comput. Sci. Eng. 14 (3) (2012)
Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami
Poster: Scalable Fast Multipole Methods for Vortex Element Methods. SC Companion (2012)
Hatem Ltaief, Rio Yokota
Data-Driven Execution of Fast Multipole Methods CoRR (2012)
Enas Yunis, Rio Yokota, Aron J. Ahmadia
Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods. ISPDC (2012)
Kenjiro Taura, Jun Nakashima, Rio Yokota, Naoya Maruyama
A Task Parallel Implementation of Fast Multipole Methods. SC Companion (2012)
Rio Yokota, Lorena A. Barba
A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems. Int. J. High Perform. Comput. Appl. 26 (4) (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Rio Yokota