Collaboration Development through Interactive Learning between Human and Robot

In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions

Compressibility of Liquefied Sand

Laboratory measurement using CCD camera was conducted to trace the sedimentation process of sand grains in a liquefied model layer. The purpose of this measurement was basically intended to obtain a visual evidence of appearance of suspended state in upper part of the liquefied soil. For this purpose, glass bead particles were used as model ground material. The test results prevailed that the glass bead grains were suspended in pore water at the instant when complete liquefaction was brought about to the layer, then they began to settle in the water. The measured pore water kept high value until grains ceased moving. And the moving velocity was far slower than that estimated by Stokes equation for sedimentation of single particle. From these findings, a predicting method was proposed to obtain the compressibility of liquefied sand layer and the continuation time of suspended state of grains

A novel symmetry in nanocarbons: pre-constant discrete principal curvature structure

Since the first-principles calculations in quantum chemistry precisely provide possible configurations of carbon atoms in nanocarbons, we have analyzed the geometrical structure of the possible carbon configurations and found that there exists a novel symmetry in the nanocarbons, i.e., the pre-constant discrete principal curvature (pCDPC) structure. In terms of the discrete principal curvature based on the discrete geometry for trivalent oriented graphs developed by Kotani, Naito, and Omori (Comput. Aided Geom. Design, $\bf{58}$, (2017), 24-54), we numerically investigated discrete principal curvature distribution of the nanocarbons, C$_{60}$, carbon nanotubes, C$_{120}$ (C$_{60}$ dimer), and C$_{60}$-polymers (peanut-shaped fullerene polymers). While the C$_{60}$ and nanotubes have the constant discrete principal curvature (CDPC) as we expected, it is interesting to note that the C$_{60}$-polymers and C$_{60}$ dimer also have the almost constant discrete principal curvature, i.e., pCDPC, which is surprising. A nontrivial pCDPC structure with revolutionary symmetry is available due to discreteness, though it has been overlooked in geometry. In discrete geometry, there appears a center axisoid which is the discrete analogue of the center axis in the continuum differential geometry but has three-dimensional structure rather than a one-dimensional curve due to its discrete nature. We demonstrated that such pCDPC structure exists in nature, namely in the C$_{60}$-polymers. Furthermore, since we found that there is a positive correlation between the degree of the CDPC structure and stability of the configurations for certain class of the C$_{60}$-polymers, we also revealed the origin of the pCDPC structure from an aspect of materials science.Comment: 18 page

Tunable interstitial and vacancy diffusivity by chemical ordering control in CrCoNi medium-entropy alloy

Li Y., Du J.P., Shinzato S., et al. Tunable interstitial and vacancy diffusivity by chemical ordering control in CrCoNi medium-entropy alloy. npj Computational Materials 10, 134 (2024); https://doi.org/10.1038/s41524-024-01322-6.In this study, we utilized a quantitative atomistic analysis approach to investigate the impact of chemical ordering structures on the diffusion behavior of interstitials and vacancies within the CrCoNi medium entropy alloy (MEA), employing an advanced neural network interatomic potential (NNP). We discovered that the degree of chemical ordering, which can be precisely controlled through annealing at elevated temperatures, significantly influences both interstitial and vacancy diffusion. This phenomenon contributes to the notable sluggish diffusion characteristic of CrCoNi, largely attributable to the restriction of diffusion pathways in regions with lower degree of chemical ordering. We also emphasized the crucial role of operating temperature on diffusion, which should be remained well below the annealing temperature to preserve the sluggish diffusion effect. Our research sheds light on the interplay between chemical ordering and defect diffusion in MEAs, and it proposes effective strategies for tailoring the diffusivity of MEAs by altering their chemical ordering. These insights are instrumental in the development of next-generation materials, which are optimized for use in challenging environments, such as high-temperature and irradiation conditions