ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity

1 code implementation • 4 Feb 2025 • Yuji Tone, Masatoshi Hanai, Mitsuaki Kawamura, Kenjiro Taura, Toyotaro Suzumura

In this paper, we study invariance and continuity in the generative machine learning for CSP.

Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation

1 code implementation • 2 Oct 2023 • Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong, Masatoshi Hanai, Chuang Yang, Hiroki Kanezashi, Renhe Jiang, Shintaro Fukushima

Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.

On Data Imbalance in Molecular Property Prediction with Pre-training

no code implementations • 17 Aug 2023 • LiMin Wang, Masatoshi Hanai, Toyotaro Suzumura, Shun Takashige, Kenjiro Taura

In this study, we propose an effective pre-training method that addresses the imbalance in input data.

Molecular Property Prediction Property Prediction

Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?

no code implementations • 16 Aug 2023 • Shun Takashige, Masatoshi Hanai, Toyotaro Suzumura, LiMin Wang, Kenjiro Taura

In material science, the prediction of unobserved values, commonly referred to as extrapolation, is particularly critical for property prediction as it enables researchers to gain insight into materials beyond the limits of available data.

Molecular Property Prediction Property Prediction

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

no code implementations • 27 Mar 2022 • 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

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks.

Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling

no code implementations • 18 Jan 2021 • Masatoshi Hanai, Nikos Tziritas, Toyotaro Suzumura, Wentong Cai, Georgios Theodoropoulos

In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required.

graph partitioning Distributed, Parallel, and Cluster Computing Databases Discrete Mathematics Data Structures and Algorithms Social and Information Networks

Global Data Science Project for COVID-19

no code implementations • 10 Jun 2020 • Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao, Irene Li, Hiroshi Maruyama, Hiroki Kanezashi, Raquel P'erez-Arnal, Kunihiko Miyoshi, Euma Ishii, Keita Suzuki, Sayaka Shiba, Mariko Kurokawa, Yuta Kanzawa, Naomi Nakagawa, Masatoshi Hanai, Yixin Li, Tianxiao Li

At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.

Performance Evaluation of Dynamic Scaling on MPI

1 code implementation • 30 Dec 2019 • Masatoshi Hanai, Georgios Theodoropoulos

Our dynamic scaling implementation allows the new MPI processes from new hosts to communicate with the original ones immediately.

Distributed, Parallel, and Cluster Computing