学習戦略に基づく学習分類子システムの設計

On Learning Classifier Systems dubbed LCSs a leaning strategy which defines how LCSs cover a state-action space in a problem can be one of the most fundamental options in designing LCSs. There lacks an intensive study of the learning strategy to understand whether and how the learning strategy affects the performance of LCSs. This lack has resulted in the current design methodology of LCS which does not carefully consider the types of learning strategy. The thesis clarifies a need of a design methodology of LCS based on the learning strategy. That is, the thesis shows the learning strategy can be an option that determines the potential performance of LCSs and then claims that LCSs should be designed on the basis of the learning strategy in order to improve the performance of LCSs. First, the thesis empirically claims that the current design methodology of LCS, without the consideration of learning strategy, can be limited to design a proper LCS to solve a problem. This supports the need of design methodology based on the learning strategy. Next, the thesis presents an example of how LCS can be designed on the basis of the learning strategy. The thesis empirically show an adequate learning strategy improving the performance of LCS can be decided depending on a type of problem difficulties such as missing attributes. Then, the thesis draws an inclusive guideline that explains which learning strategy should be used to address which types of problem difficulties. Finally, the thesis further shows, on an application of LCS for a human daily activity recognition problem, the adequate learning strategy according to the guideline effectively improves the performance of the application. The thesis concludes that the learning strategy is the option of the LCS design which determines the potential performance of LCSs. Thus, before designing any type of LCSs including their applications, the learning strategy should be adequately selected at first, because their performance degrades when they employ an inadequate learning strategy to a problem they want to solve. In other words, LCSs should be designed on the basis of the adequate learning strategy.電気通信大学201

Iron absorption in Crohn\u27s Disease

Anemia is observed in a considerable number of patients with Chohn\u27s disease (CD). Iron absorption was investigated, using radioactive iron (^FeCl_3), in 12 normal subjects (controls), seven patients with iron deficiency anemia (ID), and 23 patients with CD. After oral administration of 1 μ Ci of ^Fe with 4 mg of FeSO_4 as a carrier, serum ^59Fe levels were determined for approximately three hours with a liquid scintillation counter. The proportion of iron remaining in the body two weeks after administration was also determined as the absorption ratio by whole-body counting. In investigating the factors affecting the absorption ratio, erythrocyte count, hemoglobin, total protein, albumin, serum iron (Fe), serum transferrin (TS), and serum ferritin were used as dependent variables, and were subjected to a multiple regression analysis for the prediction of the absorption ratio. The analysis revealed that hemoglobin and ferritin levels correlated significantly with the absorption ratio. The absorption ratio was not useful in differentiating between ID and CD patient, while both TS and ferritin were useful in differentiating between CD, ID and normal subjects. The time-course change in serum ^Fe after administration of ^Fe showed slightly higher levels in ID and CD than in controls

Trials in development of a scheme of the educational guidance based on “the Layer model of mathematical activities”

　これまで数多く用いられてきた「数学的活動」について，本校における視点で再考するとともに，新しく改訂される学習指導要領に示される指導内容についても検討する。その具体として教科書の構成を「数学的活動のレイヤー論」の視点で捉え直す。これまで一つの単元で構成されがちであったカリキュラム設計を，単元や領域の枠を超えて組み直すことで，課題に対し様々なアプローチを可能とし，より深化した数学的活動を目指すこととなる。本年度は模索しながらもその実践を繰り返すことで数学的活動をどのように捉え直すことができるかを考え，その成果と課題について考察した。 / 　While reviewing various traditionally used “mathematical activities” from a viewpoint shared in our junior high school, we examined teaching contents exhibited by Education Ministry Guidelines which are supposed to be newly revised. As a result of the review, we propose a revision of composition of math textbooks in terms of the “Layer model of mathematical activities”. We also provide more elaborated mathematical activities that enable various approaches to a problem, by restructuring curriculum designs, that have been often structured by a single course unit, beyond the frames of course units or disciplines. In this paper we also discuss results and problems emerged from our trials and practices of these improved mathematical activities

Evolutionary inactivation of a sialidase in group B Streptococcus

Group B Streptococcus (GBS) is a leading cause of bacterial sepsis and meningitis in newborns. GBS possesses a protein with homology to the pneumococcal virulence factor, NanA, which has neuraminidase (sialidase) activity and promotes blood-brain barrier penetration. However, phylogenetic sequence and enzymatic analyses indicate the GBS NanA ortholog has lost sialidase function – and for this distinction we designate the gene and encoded protein nonA/NonA. Here we analyze NonA function in GBS pathogenesis, and through heterologous expression of active pneumococcal NanA in GBS, potential costs of maintaining sialidase function. GBS wild-type and ΔnonA strains lack sialidase activity, but forced expression of pneumococcal NanA in GBS induced degradation of the terminal sialic acid on its exopolysaccharide capsule. Deletion of nonA did not change GBS-whole blood survival or brain microvascular cell invasion. However, forced expression of pneumococcal NanA in GBS removed terminal sialic acid residues from the bacterial capsule, restricting bacterial proliferation in human blood and in vivo upon mouse infection. GBS expressing pneumococcal NanA had increased invasion of human brain microvascular endothelial cells. Thus, we hypothesize that nonA lost enzyme activity allowing the preservation of an effective survival factor, the sialylated exopolysaccharide capsule

An Empirical Analysis of Action Map in Learning Classifier Systems

An action map is one of the most fundamental options in designing a learning classifier system (LCS), which defines how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem effectively, resulting in a lack of basic design methodology of LCS in terms of the action map. This paper attempts to empirically conclude this issue with an intensive analysis comparing different action maps on LCSs. From the analysis on a benchmark classification problem, we identify a fact that an adequate action map can be determined depending on a type of problem difficulty such as class imbalance, more generally, a complexity of classification or decision boundary of problem. We also conduct an experiment on a human activity recognition task as a real world classification problem, and then confirm that a suggested adequate action map from the analysis enables an LCS to improve on the performance. Those results claim that the action map should be selected adequately in designing LCSs in order to improve their potential performance

Learning Optimality Theory for Accuracy-Based Learning Classifier Systems

Evolutionary computation has brought great progress to rule-based learning but this progress is often blind to the optimality of the system design. This article theoretically reveals an optimal learning scheme on the most popular evolutionary rule-based learning approach-the accuracy-based classifier system (or XCS). XCS seeks to form accurate, maximally general rules that together classify the state space of a given domain. Previously, setting up the system to perform well has been a 'blackart' as no systematic approach to XCS parameter tuning existed. We derive a theoretical approach that mathematically guarantees that XCS identifies the accurate rules, which also returns a theoretically valid XCS parameter setting. Then, we demonstrate our theoretical setting derives the maximum correctness of rule-identification in the fewest iterations possible. We also experimentally show that our theoretical setting enables XCS to easily solve several challenging problems where it had previously struggled. </p