BULLETIN BOARD

Publication of Results Made by ABCI Users（Article 21, ABCI Agreement）

When ABCI users intend to make the results public for academic publication, international conference, press release, etc, please send the following information to: application@abci.ai

• Academic Publication: Name of the academic institute and abstract of the publication
  
• International Conference: Name of the conference and abstract of the publication
  
• Press Release: Release date and abstract of the release
  

Example of the credit at the publication

Computational resource of AI Bridging Cloud Infrastructure (ABCI) provided by National Institute of Advanced Industrial Science and Technology (AIST) was used.

Research Publication (ABCI Grand Challenge)

[Academic Papers]

• Kates-Harbeck, J., Svyatkovskiy, A., Tang, W., “Predicting disruptive instabilities in controlled fusion plasmas through deep learning”, Nature (2019 Apr).
  

[International Conferences, Academic Publications]

• H. Kataoka, R. Hayamizu, R. Yamada, K. Nakashima, S. Takashima, X. Zhang, E.J. Martinez-Noriega, N. Inoue, R. Yokota, “Replacing Labeled Real-Image Datasets With Auto-Generated Contours”, CVPR 2022, Jun. 2022.
  
• Osawa, K., Swaroop, S., Jain, A., Eschenhagen,R., Turner, R. E., Yokota, R., Khan, M. E., “Practical Deep Learning with Bayesian Principles”, The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
  
• M. Yamazaki, “Yet Another Accelerated SGD: ResNet-50 Training in 70.4 sec.”, 8th ADAC Workshop, Oct. 2019.
  
• T. Narihara, H. Suganuma, “Sony’s deep learning development environment for massively large scale training”, 8th ADAC Workshop, Oct. 2019.
  
• Y. Tsuji, K. Osawa, Y. Ueno, A. Naruse, R. Yokota, and S. Matsuoka, “Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method”, The 1st Workshop on Parallel and Distributed Machine Learning 2019 (PDML19).
  
• Y. Ueno and R. Yokota, “Hierarchical Topology-aware Communication for Scaling Deep Learning to Thousands of GPUs”, The 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019).
  
• K. Osawa, Y. Tsuji, Y. Ueno, A. Naruse, T. Yokota, and S. Matsuoka, “Large-scale Distributed Second-order Optimization Using Kronecker-factored Approximate Curvature for Deep Convolutional Neural Networks”, Conference on Computer Vision and Pattern Recognition (CVPR 2019).
  
• Yokota, R. “Kronecker Factorization for Second Order Optimization in Deep Learning”, SIAM CSE, Spokane, USA, February 25-March 3 (2019).
  
• Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash　(arXiv:1811.05233) [v2] Tue, 5 Mar 2019.
  
• ImageNet/ResNet-50 Training in 224 Seconds　(arXiv:1811.05233) [v1] Tue, 13 Nov 2018.
  

[Press Releases]

• November 13, 2018 / SONY
  Sony Achieves World’s Fastest*1 Deep Learning Speeds through Distributed Learning
  

Research Results (ABCI Users and others)

[Press Releases]

• April 01, 2019 / Fujitsu Laboratories Ltd.
  Fujitsu Develops Deep Learning Acceleration Technology, Achieves World’s Highest Speed
  Achieves training time of 75 seconds in ResNet-50 through highly-efficient distributed parallel processing
  https://www.fujitsu.com/global/about/resources/news/press-releases/2019/0401-01.html
  Fujitsu Laboratories Ltd. today announced that it has developed technology to improve the speed of deep learning software, which has now achieved the world’s highest speed when the time required for machine learning was measured using the AI Bridging Cloud Infrastructure (ABCI) system, deployed by Fujitsu Limited for the National Institute of Advanced Industrial Science and Technology (AIST).
  
  

[International Conferences, Academic Publications]

• Gaku Morio, Christopher D. Manning, “An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement”, NeurIPS 2023 Track on Datasets and Benchmarks, Dec 2023
• Shun Muroga, “Multimodal Deep Learning for Composite Materials”, MRM2023/IUMRS-ICA2023 Materials Innovation for Sustainable Development Goals, Dec 2023
• Yosuke Yamaguchi, Zhao Wang, Yuusuke Nakano, Jun Ohya, Katsuya Hasegawa, “A Chronological and Cooperative Route Optimization Method for Heterogeneous Vehicle Routing Problem”, The 25th Int’l Conf on Artificial Intelligence, Jul 2023
• Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa, “Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic”, Proceedings of the 40th International Conference on Machine Learning, (ICML 2023), Jul 2023
• Yosuke Oyama, Akihiro Tabuchi and Atsushi Tokuhisa, “Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku”, 13th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, Jun 2023
• Yosuke Oyama, Takumi Honda, Atsushi Ishikawa and Koichi Shirahata, “Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers”, The 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023), May 2023
• Minori Furukori, Yasushi Nagamune, Yasuo Nakayama, Takuya Hosokai, “High-throughput transient photoluminescence spectrometer for deep learning of thermally activated delayed fluorescence materials”, Journal of Materials Chemistry C, 2023
• I. Suemitsu, H. K. Bhamgara, K. Utsugi, J. Hashizume and K. Ito, “Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network”, IEEE Robotics and Automation Letters, vol. 7, no. 3, 2023
• Akifu Saptarshi Sinha, Hiroki Ohashi, and Katsuyuki Nakamura, “Class-Difficulty Based Methods for Long-Tailed Visual Recognition”, International Journal of Computer Vision, 2023
• Shun Muroga, Yasuaki Miki, Kenji Hata, “A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials”, arXiv:2303.16412, 29, Mar 2023
• Masaru Koido, Chung-Chau Hon, Satoshi Koyama, Hideya Kawaji, Yasuhiro Murakawa, Kazuyoshi Ishigaki, Kaoru Ito, Jun Sese, Nicholas F. Parrish, Yoichiro Kamatani, Piero Carninci & Chikashi Terao, “Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning”, Nature Biomedical Engineering, Nov 2022
• Masato Tamura, Rahul Vishwakarma, Ravigopal Vennelakanti, “Hunting Group Clues with Transformers for Social Group Activity Recognition”, European Conference on Computer Vision (ECCV) 2022, Oct 2022
• Jana Backhus, Yasutaka Kono, “Cooling power dependency simulation for real-world data center data”, SoftCOM 2022, Sep 2022
• I. Suemitsu, H. K. Bhamgara, K. Utsugi, J. Hashizume and K. Ito, “Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network”, IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7818-7825, Jul 2022
• Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, Yasuhiro Sogawa, “Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis”, International Workshop on Semantic Evaluation (SemEval-2022), Jul 2022
• Yosuke Oyama, Koichi Shirahata, “Optimizing Open-Source CFD Software on a GPU Supercomputer”, WCCM-APCOM YOKOHAMA 2022, Jul 2022
• Shun Muroga, Yasuaki Miki, Takashi Honda, Hiroshi Morita, Toshiya Okazaki, Kenji Hata, “Multimodal Artificial Intelligence for Virtual Screening of Complex Nanocomposite Materials”, 2022 MRS Fall Meeting, Dec 2022
• Yosuke Oyama, Koichi Shirahata, “Optimizing Open-Source CFD Software on a GPU Supercomputer”, WCCM-APCOM YOKOHAMA 2022, Jul 2022
• Haderbache Amir, Ohno Yoshinobu, Takahiro Miyashiro, Yasuhiro Goto, Koichi Shirahata, Hiroshi Okuda, “Performance Analysis of 3D Ground Application for Next-Generation Supercomputers”, WCCM-APCOM 2022, Jul 2022
• Shunsuke Onuma, Kazuma Kadowaki, “JRIRD at the NTCIR-16 FinNum-3 Task: Investigating the Effect of Numerical Representations in Manager‘s Claim Detection”, NTCIR-16, Jun 2022
• Tatsuya Ishigaki, Suzuko Nishino, Sohei Washino, Hiroki Igarashi, Yukari Nagai, Yuichi Washida and Akihiko Murai, “Automating Horizon Scanning in Future Studies”, 13th Conference on Language Resources and Evaluation (LREC2022), Jun 2022
• Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori, and Tetsuya Ogata, “Multimodal Time Series Learning of Robots Based on Distributed and Integrated Modalities: Verification with a Simulator and Actual Robots”, ICRA 2023, May 2023
• Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori and Tetsuya Ogata, “Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility”, ICRA2022, May 2022
• Hiroshi Ito, Hideyuki Ichiwara, Kenjiro Yamamoto, Hiroki Mori and Tetsuya Ogata, “Integrated Learning of Robot Motion and Sentences: Real-Time Prediction of Grasping Motion and Attention based on Language Instructions”, ICRA2022, May 2022
• Taku Monjo, Masaru Koido, Satoi Nagasawa, Yutaka Suzuki and Yoichiro Kamatani, “Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation”, Scientific Reports, Mar 2022
• Shanshan Liu, Tatsuya Ishigaki, Yui Uehara, Hiroya Takamura, Chowdhury Mohammad Mahir Asef, Mutsunori Uenuma, Hiroyuki Shindo, Yuji Matsumoto, ”A Generative Approach for End-to-End Relation Extraction”, SCIDOCA 2021, Nov 2021​
• Yumi Hamazono, Tatsuya Ishigaki, Yusuke Miyao, Hiroya Takamura, Ichiro Kobayashi, “Unpredictable Attributes in Market Comment Generation ”, The 35th Pacific Asia Conference on Language, Information and Computation(PACLIC35), Nov 2021​
• GYoichi Hirose, Nozomu Yoshinari, Shinichi Shirakawa, “NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters”, The 13th Asian Conference on Machine Learning (ACML 2021), Nov 2021​
• Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura, “FBR-CNN: A Feedback Recurrent Network for Video Saliency Detection”, IEEE International Workshop on Machine Learning for Signal Processing (IEEE MLSP 2021), Oct 2021​
• S. Matsumori, K. Shingyouchi, Y. Abe, Y. Fukuchi, K. Sugiura, and M. Imai, “Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue”, The International Conference on Computer Vision 2021 (ICCV 2021), Oct 2021
• Tatsuya Ishigaki, Goran Topic, Yumi Hamazono, Hiroshi Noji, Ichiro Kobayashi, Yusuke Miyao, Hiroya Takamura, ”Generating Racing Game Commentary from Vision, Language, and Structured Data”, The 14th International Conference on Natural Language Generation (INLG2021), Sep 2021
• Shintaro Yamamoto, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš, Shigeo Morishima, “Visual Summary Identification From Scientific Publications via Self-Supervised Learning”, Frontiers in Research Metrics and Analytics, Aug 2021
• Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Ryousei Takano, Hirotaka Ogawa, “ABCI 2.0: Advances in Open AI Computing Infrastructure at AIST”, IPSJ SIG Technical Reports HPC-180, Jul 2021​
• Akifumi Suzuki,　Hiroaki Akutsu, Takahiro Naruko, Koki Tsubota, Kiyoharu Aizawa, “Learned Image Compression with Super-Resolution Residual Modules and DISTS Optimization”, CVPR 2021, June 2021
• Hiroaki Yamada, Masataka Shirahashi, Naoyuki Kamiyama, Yumeka Nakajima, “MAS Network: Surrogate Neural Network for Multi-Agent Simulation”, The 22nd International Workshop on Multi-Agent-Based Simulation, 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2021), May 2021
• Shintaro Yamamoto, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš and Shigeo Morishima, “Self-Supervised Learning for Visual Summary Identification in Scientific Publications” 11th International Workshop on Bibliometric-enhanced Information Retrieval, Apr 1, 2021
• Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa, Joseph Park, George Sugihara, Ryousei Takano, Jason Haga, Gerald M. Pao, “Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution”, the 26th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2020).
• Yue Qiu, Yutaka Satoh, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, “Indoor Scene Change Captioning based on Multi-modality Data”, Sensors, 2020.
• Yuchi Ishikawa, Seito Kasai, Yoshimitsu Aoki, Hirokatsu Kataoka, “Alleviating Over-segmentation Errors by Detecting Action Boundaries,” Winter Conference on Applications of Computer Vision (WACV), 2021.
• Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka, “Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data,” International Conference on Pattern Recognition (ICPR), 2020.
• Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, “Pre-training without Natural Images”, Asian Conference on Computer Vision (ACCV), 2020. The Best Paper Award.
• Hideki Tsunashima, Hirokatsu Kataoka, Junji Yamato, Qiu Chen, Shigeo Morishima, “Adversarial Knowledge Distillation for a Compact Generator”, International Conference on Pattern Recognition (ICPR), 2020.
• Atsushi Ikeda, Hirokazu Nosato, Yuta Kochi, Hiromitsu Negoro, Takahiro Kojima, Hidenori Sakanashi, Masahiro Murakawa, and Hiroyuki Nishiyama, “Cystoscopic imaging for bladder cancer detection based on stepwise organic transfer learning with a pre-trained convolutional neural network,” Journal of Endourology.
• Yusuke Tanimura, Shinichiro Takizawa, Hirotaka Ogawa, “Building and Evaluation of Cloud Storage and Datasets Services on AI and HPC Converged Infrastructure”, The 9th Workshop on Scalable Cloud Data Management, co-located with 2020 IEEE International Conference on Big Data, Dec 2020​
• Hinari Daido, “New automated theorem prover for a fragment of Dependent Type Semantics (DTS)”, Logic and Engineering of Natural Language Semantics 17 (LENLS17), November 15-16, 2020.
• Motoki Taniguchi and others, “A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations”, The 28th International Conference on Computational Linguistics (COLING2020) December 8-11, 2020.
• Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura and Yusuke Miyao, “Learning with Contrastive Examples for Data-to-text Generation”, The 28th International Conference on Computational Linguistics (COLING2020), December 8-11, 2020.
• Hiroaki Ozaki, Gaku Morio, Koreeda Yuta, Terufumi Morishita, Toshinori Miyoshi, “Hitachi at MRP 2020: Text-to-Graph-Notation Transducer”, The SIGNLL Conference on Computational Natural Language Learning, November 19-20, 2020.
• Shogo Murai, Hiroaki Mikami, Masanori Koyama, Shuji Suzuki, Takuya Akiba, “Online-Codistillation Meets LARS: Going beyond the Limit of Data Parallelism in Deep Learning”, The 5th Deep Learning on Supercomputers Workshop, Supercomputing Conference 2020, November 11, 2020.
• Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, “Pre-training without Natural Images”, Asian Conference on Computer Vision (ACCV), 2020.
  
• Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka, “Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data”, International Conference on Pattern Recognition (ICPR), 2020.
  
• Yuchi Ishikawa, Seito Kasai, Yoshimitsu Aoki, Hirokatsu Kataoka, “Alleviating Over-segmentation Errors by Detecting Action Boundaries”, Winter Conference on Applications of Computer Vision (WACV), 2021.
  
• Yue Qiu, Yutaka Satoh, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, “Indoor Scene Change Captioning based on Multi-modality Data”, Sensors, 2020.
  
• Sol Cummings, Sho Nakamura, Yasunobu Shimazaki, “Building Change Detection Using Modified Siamese Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium 2020 (IGARSS 2020), Sep. 2020.
  
• Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
  
• Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humour Recognition”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
  
• Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 8: Simpler but Effective Modality Ensemble for Meme Emotion Recognition”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
  
• Gaku Morio, Terufumi Morishita, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
  
• Gaku Morio, Terufumi Morishita, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
  
• Sol Cummings, Sho Nakamura, Yasunobu Shimazaki, “Building Change Detection Using Modified Siamese Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium 2020, Sep. 2020
  
• Hiroaki Akutsu, Akifumi Suzuki, Zhong Zhisheng, Kiyoharu Aizawa, “Ultra Low Bitrate Learned Image Compression by Selective Detail Decoding”, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun. 2020
  
• Kazuki Uehara, “MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS”, 27th IEEE International Conference on Image Processing (ICIP2020), Oct. 2020.
  
• Dario Bertero, Takeshi Homma, Kenichi Yokote, Makoto Iwayama, Kenji Nagamatsu, “Model Ensembling of ESIM and BERT for Dialogue Response Selection”, The Eighth Dialog System Technology Challenge (DSTC8) Workshop, Aug. 2020.
  
• Kohei Ozaki and Shuhei Yokoo, “1st place in Retrieval Challenge” and “3rd place in Recognition Challenge”, CVPR 2019 Second Landmark Recognition Workshop, 16 June 2019.
  
• Kohei Ozaki and Shuhei Yokoo, “Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset” (arXiv:1906.04087) [v1] Mon, 10 June 2019.
  
• Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka, “iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction”, SC19, 17-22 November 2019.
  
• Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka, “A Versatile Software Systolic Execution Model for GPU Memory Bound Kernels”, SC19, 17-22 November 2019.
  
• Atsuro Okazawa, Tomoyuki Takahata, Tatsuya Harada, “Simultaneous transparent and non-transparent objects segmentation with multispectral scenes”, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4-8 November 2019.
• Shinichiro Takizawa, “AI Bridging Cloud Infrastructure (ABCI) and its Communication Performance”, 7th Annual MVAPICH User Group (MUG) Meeting, Aug 2019​
• Hirotaka Ogawa, “AI Bridging Cloud Infrastructure (ABCI) and its I/O architecture”, Fifth International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, Jun 2019​
  

Publications about ABCI

• Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Ryousei Takano, Hirotaka Ogawa. “ABCI 2.0: Advances in Open AI Computing Infrastructure at AIST”. IPSJ SIG Technical Reports HPC-180. July. 2021.
• Yusuke Tanimura, Shinichiro Takizawa, Hirotaka Ogawa. “Building and Evaluation of Cloud Storage and Datasets Services on AI and HPC Converged Infrastructure. The 9th Workshop on Scalable Cloud Data Management”, co-located with 2020 IEEE International Conference on Big Data, Dec. 2020.
• Shinichiro Takizawa, “ABCI’s Scheduling Design for Accommodating Various AI Jobs”, 8th ADAC Workshop, Oct. 2019.
• Shinichiro Takizawa. “AI Bridging Cloud Infrastructure (ABCI) and its Communication Performance”. 7th Annual MVAPICH User Group (MUG) Meeting. Aug. 2019.
• Hirotaka Ogawa. “AI Bridging Cloud Infrastructure (ABCI) and its I/O architecture”. Fifth International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale. Jun. 2019

ABCI Benchmarks

• 8th in the row at TOP500　(June 2019)
  
• 3rd in the row at Green500　(June 2019)
  
• 5th in the row at HPCG Performance　(June 2019)