Computer Science Algorithms

Researchers from The University of Osaka, Kyushu University, and the University of Victoria have developed MV-SZZ, a new method that accurately identifies defect-inducing software commits. By combining detailed code tracking with a majority voting system, the approach reduces false positives and outperforms existing techniques. This improvement could help developers debug software more efficiently and build more reliable systems.

An Osaka Metropolitan University-led research team used hourly precipitation data from 1981 to 2020 and spatial modeling to predict extreme precipitation events for the next 100 years in Japan.

Region-specific residential buildings solution optimizes energy-efficiency and thermal comfort

The largest ever reported database of dielectric material properties could speed up development of electronics like smartphones and energy storage systems.

Researchers have developed a machine learning approach to predict the mechanical behaviour of new alloys, offering potential savings in both costs and experiment time.

Understanding how tree-like structures that form in thin films could be the key to next-generation materials for beyond-5G communications technologies.

By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.

Innovative approaches in cancer screening, drug development, and radioactive hazard protection are accelerating healthcare solutions.

Researchers from SANKEN at The University of Osaka have successfully developed a groundbreaking “self-evolving” edge AI technology that enables real-time learning and forecasting capabilities directly within compact devices. This innovation, termed MicroAdapt, achieves unprecedented speed and accuracy. It processes data up to 100,000 times faster and achieves up to 60% higher accuracy compared to conventional state-of-the-art deep learning methods. This achievement represents a major advance toward next-generation real-time AI applications across manufacturing, automotive IoT, and medical wearables, addressing critical limitations of existing cloud-dependent AI.

New machine learning model cuts fluid simulation time from 45 minutes to 3

By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.

Edge computing on a smartphone has been used to analyze data collected by a multimodal flexible wearable sensor patch and detect arrhythmia, coughs and falls.

Potential benefits of method include subjecting patients to fewer exams and reducing health care costs

Time series data and causal impact algorithm reveal the effectiveness of a new transit station over a four-year period

Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research) and Chair Professor of Computational Intelligence of Lingnan University, co-authored the research paper that has received the Best Paper, Chester W. Sall Memorial Awards 2024 from the Institute of Electrical and Electronics Engineers (IEEE), the world's largest technical professional organisation dedicated to advancing technology for the benefit of humanity. This honour was published in their monthly peer-reviewed scientific journal “IEEE Transactions on Consumer Electronics”. Only three Best Paper Awards are given each year, and the competition is extremely fierce.

Machine learning algorithms allow analysis and characterization of the atomic arrangement of silicon surface superstructures without the need for human expertise.

A research group at Osaka Metropolitan University has derived a new evaluation method for the measuring the size X-ray microbeams (diameter) through mathematical analysis. The group then verified the validity of the mathematically derived evaluation method by measuring the diameter of X-ray microbeams using metal wires of various diameters with an X-ray fluorescence analysis system for small areas and found that it was possible to calculate the beam diameter more accurately than the previous conventional evaluation method.

Researchers from Osaka University have developed a computational analysis tool called CAPITAL for comparative analysis of single-cell RNA sequencing data with complicated branching trajectories. As cells undergo a dynamic process, they can be placed on a “pseudotime trajectory” to analyze the gene expression changes throughout the process. While previously only straightforward linear trajectories could be compared, CAPITAL is proven to be able to accurately compare branching trajectories, significantly advancing the field of high-throughput single-cell sequencing.

In a continuing effort to improve upon previous work, a research team at the Graduate School of Science, Osaka City University, have applied their recently developed Bayesian phase difference estimation quantum algorithm to perform full configuration interaction (full-CI) calculations of atoms and molecules without simulating the time evolution of the wave function conditional on an ancillary qubit. Superior to conventional methods in terms of parallel execution of quantum gates during quantum computing, this new algorithm is expected to be much easier to implement in actual quantum computers.

Researchers from The University of Tokyo Institute of Industrial Science develop an experimentally supported mathematical model that defines the velocity jump mechanism in crack propagation

Researchers from The University of Tokyo Institute of Industrial Science report a machine learning-based model for predicting the bonding properties of materials

Researchers improve their newly established quantum algorithm, bringing it to one-tenth the computational cost of Quantum Phase Estimation, and use it to directly calculate the vertical ionization energies of light atoms and molecules such as CO, O2, CN, F2, H2O, NH3 within 0.1 electron volts of precision.

Researchers at Osaka City University use quantum superposition states and Bayesian inference to create a quantum algorithm, easily executable on quantum computers, that accurately and directly calculates energy differences between the electronic ground and excited spin states of molecular systems in polynomial time.

Scientists devise a 2D-material–based stacked structure with applications in reducing computing power consumption

A group of students, affiliated with South Korea's Ulsan National Institute of Science and Technology (UNIST) has created a map to help track the spread of the COVID-19 in Ulsan region.

Researchers at Osaka City University create a quantum algorithm that removes spin contaminants while making chemical calculations on quantum computers. This allows for predictions of electronic and molecular behavior with degrees of precision not achievable with classical computers and paves the way for practical quantum computers to become a reality.

A research team led by Prof Jeongho Kwak from Daegu Gyeongbuk Institute of Science and Technology (DGIST) has designed a novel system architecture where collaboration between cloud service providers and mobile network operators plays a central role. Such a collaborative architecture would allow for optimizing the use of network, computing, and storage resources, thereby unlocking the potential of various novel services and applications.

Point set registration problems, i.e. finding corresponding points between shapes represented as point sets, are important in a variety of fields, so algorithms have been developed. Here, a new algorithm is discovered for point set registration problems. This algorithm is demonstrated to solve several typical problems remarkably faster than conventional methods and with the highest accuracy. This novel algorithm will be beneficial to a wide variety of technologies including computer graphics, computer vision, authentication etc.

A recent study, affiliated with South Korea's Ulsan National Institute of Science and Technology (UNIST) has presented an artificial intelligence technology that can predict traffic conditions for the next 5 to 15 minutes at an error rate of less than four kilometers an hour.

A recent study, affiliated with South Korea's Ulsan National Institute of Science and Technology (UNIST) has presented a novel approach to identify cancer suppressing microRNA (miRNA) targets and relevant cellular signaling pathways through large-scale gene-expression data analysis.