Bayesian Inference of Self-intention Attributed by Observer

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior

Productivity Disparities Between Self-Employed Workers and Employees (Japanese)

This paper measures the productivity disparities between self-employed workers (i.e. business owners and unpaid family workers) and employees by estimating a production function based on enterprise-level micro data from the Census of Manufactures between 1981 and 2000. As a result, we find that, compared with male employees, 1) the productivity of male self-employed workers is significantly higher, and 2) no significant disparities in productivity are found with that of female self-employed workers. Using these results, we re-estimate the labor input indices and the labor quality indices for the manufacturing industries of the 2006 JIP database. As a result of this re-estimation, the annual growth rate of the labor quality index for the whole manufacturing industry between 1970 and 2002 becomes smaller than that of the 2006 JIP estimates by 0.43 percentage points. If we apply the productivity disparities among various types of employees estimated by Kawaguchi, et al. (2007) rather than using wages as productivity indices, the annual growth rate of the labor quality index is further reduced by 0.10 percentage points. Combining both results we get an annual 0.53 percentage points decline of the growth rate of the labor quality index, and the labor input index. This suggests that the annual growth rate of TFP in this period is underestimated by approximately by 0.4 percentage points.

Hepatitis B virus strains of subgenotype A2 with an identical sequence spreading rapidly from the capital region to all over Japan in patients with acute hepatitis B

ObjectiveTo examine recent trends of acute infection with hepatitis B virus (HBV) in Japan by nationwide surveillance and phylogenetic analyses.MethodsDuring 1991 through 2009, a sentinel surveillance was conducted in 28 national hospitals in a prospective cohort study. Genotypes of HBV were determined in 547 patients with acute hepatitis B. Nucleotide sequences in the preS1/S2/S gene of genotype A and B isolates were determined for phylogenetic analyses.ResultsHBV genotype A was detected in 137 (25% (accompanied by genotype G in one)) patients, B in 48 (9%), C in 359 (66%), and other genotypes in the remaining three (0.5%). HBV persisted in five with genotype A including the one accompanied by genotype G; another was co-infected with HIV type 1. The genotype was A in 4.8% of patients during 1991-1996, 29.3% during 1997-2002, and 50.0% during 2003-2008 in the capital region, as against 6.5%, 8.5% and 33.1%, respectively, in other regions. Of the 114 genotype A isolates, 13 (11.4%) were subgenotype A1, and 101 (88.6%) were A2, whereas of the 43 genotype B isolates, 10 (23.3%) were subgenotype B1, 28 (65.1%) were B2, two (4.7%) were B3, and three (7.0%) were B4. Sequences of 65 (64%) isolates of A2 were identical, as were three (23%) of A1, and five (18%) of B2, but none of the B1, B3 and B4 isolates shared a sequence.ConclusionsAcute infection with HBV of genotype A, subgenotype A2 in particular, appear to be increasing, mainly through sexual contact, and spreading from the capital region to other regions in Japan nationwide. Infection persisted in 4% of the patients with genotype A, and HBV strains with an identical sequence prevailed in subgenotype A2 infections. This study indicates the need for universal vaccination of young people to prevent increases in HBV infection in Japan

Lack of association between the CARD10 rs6000782 polymorphism and type 1 autoimmune hepatitis in a Japanese population

Background: Previous genome-wide association studies have evaluated the impact of common genetic variants and identified several non-HLA risk loci associated with autoimmune liver diseases. More recent genome-wide association studies and replication analyses reported an association between variants of the CARD10 polymorphism rs6000782 and risk of type 1 autoimmune hepatitis (AIH). In this case-control study, we genotyped 326 Japanese AIH patients and 214 control subjects. Results: Genomic DNA from 540 individuals of Japanese origin, including 326 patients with type-1 AIH and 214 healthy controls, was analyzed for two single nucleotide polymorphisms (SNPs) in the CARD10 gene. We selected CARD10 rs6000782 SNPs and genotyped these using PCR-RFLP method and direct sequencing. The Chi square test revealed that the rs6000782 variant alle (c) was not associated with the susceptibility for AIH in a Japanese population [p = 0.376, odds ratio (OR) 1.271, 95 % confidence interval (CI) 0.747-2.161] in an allele model. Our data also showed that CARD10 rs6000782 variants were not associated with AIH or with the clinical parameters of AIH. Conclusions: In this study we examined an association between rs6000782 SNPs in the CARD10 gene and type-1 AIH. Results showed no significant association of rs62000782 with type-1 AIH in a Japanese population. This study demonstrated no association between CARD10 rs6000782 variants and AIH in a Japanese population

Comparing feedforward neural networks using independent component analysis on hidden units

Neural networks are widely used for classification and regression tasks, but they do not always perform well, nor explicitly inform us of the rationale for their predictions. In this study we propose a novel method of comparing a pair of different feedforward neural networks, which draws on independent components obtained by independent component analysis (ICA) on the hidden layers of these networks. It can compare different feedforward neural networks even when they have different structures, as well as feedforward neural networks that learned partially different datasets, yielding insights into their functionality or performance. We evaluate the proposed method by conducting three experiments with feedforward neural networks that have one hidden layer, and verify whether a pair of feedforward neural networks can be compared by the proposed method when the numbers of hidden units in the layer are different, when the datasets are partially different, and when activation functions are different. The results show that similar independent components are extracted from two feedforward neural networks, even when the three circumstances above are different. Our experiments also reveal that mere comparison of weights or activations does not lead to identifying similar relationships. Through the extraction of independent components, the proposed method can assess whether the internal processing of one neural network resembles that of another. This approach has the potential to help understand the performance of neural networks