The Relational Interpretations on soft matter as intermediate asymptoitcs

In this paper, it is demonstrated that there is a parallelism between the relational interpretation of Rovelli and the interpretation of soft matter based on intermediate asymptotics. The general interpretation of physics strongly assumes the duality of the observer and the world, and the uniqueness of the world though the relational interpretation suggested different conclusions: {\ it no properties, no interaction}, and {\ it facts are relative}. These conclusions are seemingly counterintuitive, though this work shows that similar conclusions are found in the interpretation of soft matter based on the concept of intermediate asymptotics. The interpretation of soft matter based on intermediate asymptotics also concludes that the properties are not determined without the scale. This is due to the conclusion of intermediate asymptotics that any formalization and its interpretation are localized by the scale. It is demonstrated that the similarity between the two interpretations originated from its monism of relations. This logical structure is also compared with the works in other disciplines. This work reports the insight that the relational interpretation can be a general and fundamental concept, not the one applicable to special cases.Comment: 20 pages, 2 figure

発酵大麦エキス由来皮膚バリア機能改善物質に関する研究

京都大学新制・課程博士博士(農学)甲第23579号農博第2478号新制||農||1088(附属図書館)学位論文||R3||N5363(農学部図書室)京都大学大学院農学研究科応用生命科学専攻(主査)教授 栗原 達夫, 教授 小川 順, 教授 木岡 紀幸学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDGA

A Survey of High School Media Production Facilities in First Class School Districts in Western Washington

As more emphasis is placed on high schools to change towards a more flexible program to meet the learning needs of the students, teachers must be provided with adequate instructional materials that are up-to-date and relevant. The services rendered by a well-organized local production center are an important facet of a media program that can assist in achieving these needs

Effect of stripe order strength for the Nernst effect in La_{2-x}Sr_xCu_4 single crystals

We have precisely measured the Nernst effect in Nd-doped La$_{2-x}$Sr$_x$CuO$_4$ single crystals with controlling the strength (stability) of the stripe order. We found that the onset temperature $T_{onset}$, where the Nernst signal starts increasing, does not change conspicuously in spite of Nd-doping. At low temperatures, on the other hand, the absolute value of the Nernst signal is strongly suppressed in accordance with the strength of the stripe order. These results imply that the fluctuation of (charge) stripe order enhances the Nernst signal below $T_{onset}$ at high temperatures, and then the stripe order enhanced by Nd-doping suppresses the superconducting fluctuation to reduce the Nernst signal at low temperatures. We also observed an increase of the Nernst signal below the charge order temperature $T_{ch}$ which is observed in diffraction measurement.Comment: 3pages, 2figure

A Semi-Lagrange Galerkin Method for Shallow Water Equations

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Synthesis of Phenylcyclopropane-Based Secondary Amine Catalysts and Their Applications in Enamine Catalysis

A novel chiral motif based on a phenylcyclopropane scaffold has been designed, and a facile synthetic route to the key intermediate for the synthesis of phenylcyclopropane-based chiral secondary amines has been developed. Newly synthesized chiral amines function as effective catalysts for several asymmetric reactions through enamine intermediates

Proper Learning Algorithm for Functions of k Terms under Smooth Distributions

AbstractIn this paper, we introduce a probabilistic distribution, called a smooth distribution, which is a generalization of variants of the uniform distribution such as q-bounded distribution and product distribution. Then, we give an algorithm that, under the smooth distribution, properly learns the class of functions of k terms given as Fk∘Tkn={g(f1(v), …, fk(v))|g∈Fk, f1, …, fk∈Tn} in polynomial time for constant k, where Fk is the class of all Boolean functions of k variables and Tn is the class of terms over n variables. Although class Fk∘Tkn was shown by Blum and Singh to be learned using DNF as the hypothesis class, it has remained open whether it is properly learnable under a distribution-free setting