Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Bipolar anodic electrochemical exfoliation of graphite powders

The electrochemical exfoliation of graphite has attracted considerable attention as a method for large-scale, rapid production of graphene and graphene oxide (GO). As exfoliation typically requires direct electrical contact, and is limited by the shape and/or size of the starting graphite, treatment of small graphite particles and powders, the typical form available commercially, is extremely difficult. In this study, GO nanosheets were successfully prepared from small graphite particles and powders by a bipolar electrochemical process. Graphite samples were placed between two platinum feeder electrodes, and a constant current was applied between the feeder electrodes using dilute sulfuric acid as the electrolyte. Optical microscopy, atomic force microscopy, X-ray diffractometry, Raman spectroscopy, and X-ray photoelectron spectroscopy were employed to examine the samples obtained after electrolysis. The results obtained from these analyses confirmed that anodic electrochemical exfoliation occurs in the graphite samples, and the exfoliated samples are basically highly crystalline GO nanosheets with a low degree of oxidation (C/O = 3.6–5.3). This simple electrochemical method is extremely useful for preparing large amounts of graphene and GO from small particles of graphite

Model-based and actual independence for fairness-aware classification

The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs

結晶化ガラス顆粒の臨床応用

This reports tha development of bioactive glass ceramic particles and evaluates their use inclinical applications. 1. The subjects of the evaluation were 13 impacted teeth, 17 intramaxillary cysts (not including radicular cysts), and 7 atrophic mandibular alveolar ridges. 2. The results were classified into effective, slightly effective, ineffective, and harmful, a very high proportion, 33 or 89.3%,were judged effective or slightly effective. 3. None were evaluated to be harmful, showing the safety of the present material. Among the ineffective cases there were open wounds due to infection, leakage of the supplied material, and fistulation. In cases where inflammation had not disappeared at the supply there were cases where the particles had to be completely removed due to infection, It was determined the that this was not due to the material, but possidly due to the surgical procedures, as there were no further complications in the tretment. 4. From the results reported here, the bioactive glass ceramic material here was found to be useful in the articial bone needed after atrophic mandibular alveolar ridge surgery

On the mean convergence time of evolutionary algorithms without selection and mutation

In this paper we study random genetic drift in a finite genetic population. Exact formulae for calculating the mean convergence time of the population are analytically derived and some results of numerical calculations are given. The calculations are compared to the results obtained in population genetics. A new proposition is derived for binary alleles and uniform crossover. Here the mean convergence time is almost proportional to the size of the population and to the logarithm of the number of the loci. The results of Monte Carlo type numerical simulations are in agreement with the results from the calculation