Our Business
Services
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Research Paper Implementation
Implement specified research papers as software.
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Algorithm Implementation
Make modules or APIs of specified algorithms as you request.
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Development of User-Friendly Tools
We provide services to develop user-friendly application tools that can simplify a variety of data processing tasks at low cost.
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Research Software Development
Create software for academic research. We can also speed up existing software or add functionalities to existing software.
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Consigned Research Services
We can execute parts of your research, including surveying papers, developing necessary systems, validation, and preparing reports.
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Information Security Consulting
We provide consulting on cyber resilience (strengthening information security) for research sites.
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Large Language Models
Develop large language models for practical applications to meet a wide range of needs, from practical applications to pure research purposes.
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Optimization Software Development
Develop optimization software to find optimal solutions to problems with various constraints.
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Automated Analysis
Using RPA (Robotic Process Automation), build systems for efficient analysis.
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Neuroscience Software Development
Develop software tailored to the needs of each individual researcher in the field of neuroscience.
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Food Science Software Development
Supports a wide range of food processing stages from experimental design to analysis of results.
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Geophysical Exploration and Nondestructive Testing
Develop software for signal processing of measurement data, simulation, and data assimilation.
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Quantum Beam Research Software Development
Develop specialized software to implement and accelerate the algorithms used in quantum beam research.
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Ecological Research Software Development
Develop specialized software to fully leverage valuable data gathered from field surveys and laboratory experiments.
Business Cases
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2-D region optimal partitioning - software development
We have developed software that numerically finds an approximate solution to the optimization problem of partitioning a finite and connected two-dimensional region in a two-dimensional plane into a specified number of subregions and finding a region partitioning that minimizes the sum of the first eigenvalues (minimum eigenvalues) of the Laplace-Dirichlet eigenvalue problem in each of these subregions.
Since it is difficult to solve such an optimization problem directly, a relaxed formulation was developed by introducing a density function that approximates the characteristic function of the partitioned region and imposing a penalty outside the region in order to compute approximate eigenvalues of the partitioned region. In such a relaxed formulation, it is known that the density function converges to the characteristic function of the region as the penalty approaches infinity.
Therefore, we implemented an algorithm in which each density function is optimized by the gradient descent method so that the sum of eigenvalues becomes small while solving the approximate eigenvalue problem for each partitioned regions with appropriate initial settings. Parallel computation was also used to speed up the process.
We have also implemented an algorithm to optimize the same problem from a thermodynamic point of view. Since the property that a multi-component system of particles in Brownian motion is regionally distributed in such a way as to minimize Renyi entropy production in a stationary state is an equivalent formulation to this optimization problem, we implemented an algorithm to approximate this optimization problem by simulating this thermodynamic system.
- Technologies
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- Laplace-Dirichlet Eigenvalue Problem
- Eigenvalue Optimization
- Region Partitioning
- Parallel Computing
- C++
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Experimental condition optimization program using Bayesian optimization - development
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- Bayesian Optimization
- RPA (Robotic Process Automation)
- Sequential Experimental Design
- Python
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Machine learning on sets of samples where individual labels are unknown - R&D
We developed a machine learning algorithm to estimate whether a certain number of samples of a given type are present in a large number of samples.
The underlying technique is Multiple Instance Learning (MIL), which is a learning method used when labels for a set of samples are given as supervised data. In general supervised learning, each sample needs to be labelled in advance, but it can be applied when individual samples do not have labels.
Standard MIL assumes that the presence or absence of a certain type of sample in the set is used as a label. Even if the available label is whether or not a certain number of samples of a certain species in a set are present, it can be treated as standard MIL with special pre-processing.
- Technologies
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- Machine Learning
- Multiple Instance Learning
- Python
Engineers
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HASHIDA Yasuhiko - [Expertise]
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- Molecular Biology
- Protein Engineering
- Kinetic Analysis
- [Introduction]
- I have a wide variety of research experiences in life science fields, such as the kinetic analysis of protein interaction and molecular biology of cells.
Taking advantage of my accumulated experimental knowledge, I would be happy to support your research through development of useful and helpful software which are totally customized for you.
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NAKATA Shota - [Expertise]
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- Microbiology
- Molecular Biology
- Bioinformatics
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- I have conducted research in the fields of microbiology and molecular biology, such as metabolic modifications of microorganisms by genetic recombination and analysis of microbial ecosystems. I would be happy to apply my extensive experimental expertise to develop software and suggest analytical techniques appropriate to your research.
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INOUE Akimitsu - [Expertise]
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- Machine Learning
- Statistical Analysis
- Natural Language Processing
- Topic Model
- Text Mining
- Structural Equation Modeling
- Marketing Research
- Portfolio Optimization
- High Frequency Trade Data Analysis
- Risk Measurement
- Maximum Loss Analysis
- Econometrics
- Causal Inference
- Pricing Strategies
- Social Engineering
- Operations Research
- Combinatorial Optimization
- Discrete Event Simulation
- Spatial Data Analysis
- Sensor Data Analysis
- Anomaly Detection
- [Introduction]
- I am a data scientist with expertise in robust statistical methods.
My strength lies in analyzing data containing outliers, which are not easy to deal with using normal methods.
Products
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SparseTaroSparse Structure Estimation Software
Visualize correlations by high-speed partial correlation analysis with sparse structure estimation. This is useful for connectome analysis in the field of neuroscience.
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StackTaro3D Image Analysis Software
Analyzes image stacks and performs automatic counting and visualization. This is useful for analysis of confocal microscope images, etc.
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SpikeTaroSpike Sorting Software
Separates and clusters spike signals from aggregate potentials of neural signals. Dramatically improves the efficiency of neurophysiology research!
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ABDigitizerVideo Analysis Software for Behavioral Experiments
Automatically extracts location information from video footage of behavioral experiments and easily creates trajectory graphs and heat maps. No more manual work!
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NGS Metagenomics AOIMetagenome Analysis Software
Ultra-fast metagenome analysis compatible with next-generation sequencers. Rich visual functions are powerful for microbiological research and other applications! (Scheduled for release)
News
- 2025/12/01
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Our researcher presented at the Japanese Society of Food Engineering's Food New Technology Study Group.
- 2025/12/01
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We exhibited at the 42nd Annual Meeting of the Japan Society of Plasma and Fusion.
- 2025/12/01
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We have launched a service page for information security consulting.
Contact Us
If you have any inquiry
about our company, please contact us.
(Phone hours: 10:00 - 17:00, Japan Standard Time)
+81-75-321-7300