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IMAGE: In this figure, the hopping amplitude and existence of possible pathways for atomic migrations [panel (a)] can be identified at the microscopic level. But it is not easy to count. view more
Credit: Ryo Maezono from JAIST
Ishikawa, Japan - One of the most important classes of problems that all scientists and mathematicians aspire to solve, due to their relevance in both science and real life, are optimization problems. From esoteric computer science puzzles to the more realistic problems of vehicle routing, investment portfolio design, and digital marketing at the heart of it all lies an optimization problem that needs to be solved.
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IMAGE: Estimating the variance of the number of clusters and the sample size for which it is maximum can give us an estimate of the total number of clusters for the. view more
Credit: Ryo Maezono from JAIST.
Ishikawa, Japan - Any high-performance computing should be able to handle a vast amount of data in a short amount of time an important aspect on which entire fields (data science, Big Data) are based. Usually, the first step to managing a large amount of data is to either classify it based on well-defined attributes or as is typical in machine learning cluster them into groups such that data points in the same group are more similar to one another than to those in another group. However, for an extremely large dataset, which can have trillions of sample points, it is tedious to even group data points into a single cluster without huge memory requirements.