We designed, developed, and evaluated computer programming education at high schools and universities in 2012 and 2013 to offer students an opportunity to experience practical programming. We provided a programming course to a high school, preparing content-rich materials. In order to generate motivation for learning in students, we set “to create practical applications” as a goal of the course. In 2012, only a few students could create practical applications; however, in 2013, we improved the teaching methods and by the end of the course, almost half of the participants were able to produce practical applications. In addition, some students applied their programs for the Live E! science contest and they received prizes in 2012 and two students applied and one of them received a prize in 2013. An additional notable outcome of the course that we provided was the extent to which first-year students became interested in programming.

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A random-walk model is investigated and utilized to analyze the performance of a coding scheme that aims to extend the lifetime of flash memory. Flash memory is widely used in various products today, but the cells that constitute flash memory wear out as they experience many operations. This issue can be mitigated by employing a clever coding scheme that is known as a flash code. The purpose of this study is to establish a well-defined random-walk model of a flash code that is known as an index-less indexed flash code (ILIFC), and clarify the expected performance of ILIFC. Preliminary study has been made by the author for a simplified model of data operation, and the contribution of this study is to extend the model of data operation to more general and practical one. Mathematical properties of the random-walk model is reconsidered, and useful properties are derived that help analyzing the performance of ILIFC both in non-asymptotic and asymptotic scenarios.

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We treat an image restoration problem with a Poisson noise channel using a Bayesian framework. The Poisson randomness might be appeared in observation of low contrast object in the field of imaging. The noise observation is often hard to treat in a theoretical analysis. In our formulation, we interpret the observation through the Poisson noise channel as a likelihood, and evaluate the bound of it with a Gaussian function using a latent variable method. We then introduce a Gaussian Markov random field (GMRF) as the prior for the Bayesian approach, and derive the posterior as a Gaussian distribution. The latent parameters in the likelihood and the hyperparameter in the GMRF prior could be treated as hidden parameters, so that, we propose an algorithm to infer them in the expectation maximization (EM) framework using loopy belief propagation (LBP). We confirm the ability of our algorithm in the computer simulation, and compare it with the results of other image restoration frameworks.

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Feature selection problem has been widely used for various fields. In particular, the sparse estimation has the advantage that its computational cost is the polynomial order of the number of features. However, it has the problem that the obtained solution varies as the dataset has changed a little. The goal of this paper is to exhaustively search the solutions which minimize the generalization error for feature selection problem to investigate the problem of sparse estimation. We calculate the generalization errors for all combinations of features in order to get the histogram of generalization error by using the cross validation method. By using this histogram, we propose a method to verify whether the given data include information for binary classification by comparing the histogram of predictive error for random guessing. Moreover, we propose a statistical mechanical method in order to efficiently calculate the histogram of generalization error by the exchange Monte Carlo (EMC) method and the multiple histogram method. We apply our proposed method to the feature selection problem for selecting the relevant neurons for face identification.

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