Information Technology and Human Factors

From IT infrastructure to application technology: make, connect, use

The rapid growth of devices (smartphone, tablets, robots, wearables, etc.) and the Internet has increased the amount of information that is being produced and accessed by society. In order to better utilize the data produced from millions of devices and systems, we are conducting research and development in a wide range of fields at the interface between information technologies and human factors. Our mission is to engage and enrich the public through the research and development of intelligent systems combining computational and physical capabilities for human use. A key component of our mission is making new discoveries in the hardware and software that interacts with physical devices to sense and change the state of the real world. Our discoveries will lead to industry innovations and contribute to the advancement of society by facilitating the interaction of humans with cyber-physical systems.

Information Technology and Human Factors Pamphlet

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New Research Results

Streamlining Protein Function Prediction

Researchers at AIST have developed a machine learning approach that uses molecular simulations and protein language models to accurately predict protein functional values from a small amount of experimental data.
In recent years, researchers have increasingly used machine learning methods to predict protein functions for designing functional proteins. However, this requires significant time and material costs due to the need for large amounts of experimental observations as training data. As a result, methods that use computational values for pseudo-training data along with experimental data have gained attention. Although these methods were previously used to predict protein stability, expending their scope to include predictions of binding affinity and enzyme activity is necessary to design functional proteins tailored to specific purposes.
We have developed a novel method to predict protein functional values. This method takes advantage of functional values computed via molecular simulation and protein language models as pseudo-training data. It achieves high accuracy in predicting functional value even with limited experimental data. Furthermore, we have expanded its applicability beyond protein stability to include binding affinity, enzyme activity, cytotoxicity, and fluorescence intensity. This achievement enables more efficient development of functional proteins compared to existing methods.

Improving the accuracy of protein functional value prediction through data augmentation

Development of a SAR Foundation Model Specialized for Japan's Land Area Based on Observation Data Obtained from ALOS-2 Satellite

Researchers at AIST, in collaboration with JAXA, have developed a national land-specific Synthetic Aperture Radar (SAR) foundation model using high-resolution observation SAR data routinely acquired over Japan by the SAR instrument, PALSAR-2, onboard the "DAICHI-2" (ALOS-2).
The PALSAR-2 observation data covers the entire country, and by extracting image patches centered on selected locations, a training dataset reflecting diverse land use and land cover types was constructed. Using this dataset, we performed large-scale self-supervised learning to build a SAR foundation model specialized for Japan’s land area. The results from transfer learning for land use/land cover estimation showed significantly higher accuracy compared to models trained from scratch, demonstrating the effectiveness of the foundation model trained on a more balanced dataset. Although the foundation model is initially developed by researchers at AIST and JAXA, it is expected to broaden the use of SAR by utilizing the foundation model that can reduce both the cost of model development and the barrier to interpreting SAR images, which typically requires specialized expertise.
This research was conducted based on the "Agreement on Research and Development of AI Analysis Methods for Satellite Data" between AIST and JAXA, and AIST policy-based budget project "R&D on Generative AI Foundation Models for the Physical Domain". We used ABCI 3.0 provided by AIST and AIST Solutions with support from “ABCI 3.0 Development Acceleration Use”. The details of the research results were presented at the 78th Annual Conference of the Remote Sensing Society of Japan (Spring 2025) held from June 4 to June 5, 2025.

Construction and anticipated applications of a national land-specific SAR foundation model. The high-resolution land use and land cover map of Japan, used in this work, was provided by JAXA.

Open Innovation Laboratory

Since FY 2016, as a part of the “Open Innovation Arena concept” promoted by the Ministry of Economy, Trade and Industry (METI), AIST has created the concept of “open innovation laboratories” (OILs), collaborative research bases located on university campuses, and has been engaged in their provision. We are planning to establish more than ten OILs by FY 2020.

AIST will merge the basic research carried out at universities, etc. with AISTʼs goal-oriented basic research and applied technology development, and will promote bridging research and evelopment and industry by the establishment of OILs.

Cooperative Research Laboratories

In order to conduct research and development more closely related to strategies of companies, we have established collaborative research laboratories, bearing partner company names.

Partner companies provide their researchers and funding, and AIST provides research resources, such as its researchers, research facilities, and intellectual property. The loaned researchers of companies and AIST researchers jointly conduct research and development.

By setting up cooperative research laboratories, we will accelerate the commercialization of our goal-oriented basic research and application research with partner companies.