Hideyuki Miyahara

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We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization algorithm (DQAEM). The expectation-maximization algorithm (EM) is an established algorithm to compute maximum likelihood estimates and applied to many practical applications.(More)
Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was(More)
The Laguerre-Gaussian (LG) beam has a phase term called the Gouy phase shift which is changing in proportion to the propagation distance and the order of LG beam. We consider the effect of this phase term on the multi-zeros optical beam, a beam with propagation-invariant isolated zeros generated by an common-Gaussian combination of the LG beams, and obtain(More)
Measurement of soil CO2 concentrations is important for investigating the dynamics and diffusion of CO2 in soil. In this study, we developed a small CO2 analyzer for measuring in situ-soil CO2 concentrations. The CO2 analyzer consists of a module containing an infrared CO2 gas sensor, a temperature sensor, and a relative humidity sensor. These sensors are(More)
The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations. However, many of maximum likelihood estimation problems are nonconvex, and it is known that the EM algorithm fails to give the optimal estimates by being trapped by local optima. In order to deal with this difficulty, we propose(More)
a single curve. This is, in a way, consistent with our observation regarding throughput, which leads us to the conclusion that n = 2 is suucient for implementing the type of multiplane switch we described in this section. For the various values of m we see that m = 4 qualiies as a good choice for m. As far as the cell drop probability is concerned we can(More)
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