Ab-initio Study on the Magnetic Structures in the Ordered Mn3Pt Alloy

We study the electronic states of the magnetically ordered Mn3Pt alloy within the density functional theory. Mn3Pt has been believed that one third of Mn atoms have no magnetic moment in an antiferromagnetic phase (so-called the F-phase) realized in the temperature range of 400 K < T < 475 K. We show that this experimentally suggested spin configuration is energetically so much unfavorable that it would be irrelevant to the F-phase. We discuss the possibility that the spin moments on the one third of Mn atoms are not paramagnetic but thermally fluctuating in the F-phase. The present results have an immediate connection with the recent neutron scattering study [T. Ikeda and Y. Tsunoda, J. Phys. Soc. Jpn., vol. 72, pp. 2614-2621, October. 2003.].Comment: 4 pages, 4 figure

Streaming Active Learning for Regression Problems Using Regression via Classification

One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost

Spin-polarized electronic structures and transport properties of Fe-Co alloys

The electrical resistivities of Fe-Co alloys owing to random alloy disorder are calculated using the Kubo-Greenwood formula. The obtained electrical esistivities agree well with experimental data quantitatively at low temperature. The spin-polarization of Fe50Co50 estimated from the conductivity (86%) has opposite sign to that from the densities of the states at the Fermi level (-73%). It is found that the conductivity is governed mainly by s-electrons, and the s-electrons in the minority spin states are less conductive due to strong scattering by the large densities of the states of d-electrons than the majority spin electrons.Comment: 3 pages, 4 figure

CAPTDURE: Captioned Sound Dataset of Single Sources

In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and timbre. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. In this work, we constructed a dataset with captions for a single-source sound named CAPTDURE, which can be used in various tasks such as environmental sound separation and synthesis. Our dataset consists of 1,044 sounds and 4,902 captions. We evaluated the performance of environmental sound extraction using our dataset. The experimental results show that the captions for single-source sounds are effective in extracting only the single-source target sound from the mixture sound.Comment: Accepted to INTERSPEECH202

Spectrocolorimetric assessment of cartilage plugs after autologous osteochondral grafting: correlations between color indices and histological findings in a rabbit model

We investigated the use of a commercial spectrocolorimeter and the application of two color models (L* a* b* colorimetric system and spectral reflectance distribution) to describe and quantify cartilage plugs in a rabbit model of osteochondral autografting. Osteochondral plugs were removed and then replaced in their original positions in Japanese white rabbits. The rabbits were sacrificed at 4 or 12 weeks after the operation and cartilage samples were assessed using a spectrocolorimeter. The samples were retrospectively divided into two groups on the basis of the histological findings (group H: hyaline cartilage, successful; group F: fibrous tissue or fibrocartilage, failure) and investigated for possible significant differences in the spectrocolorimetric analyses between the two groups. Moreover, the relationships between the spectrocolorimetric indices and the Mankin histological score were examined. In the L* a* b* colorimetric system, the L* values were significantly lower in group H than in group F (P = 0.02), whereas the a* values were significantly higher in group H than in group F (P = 0.006). Regarding the spectral reflectance distribution, the spectral reflectance percentage 470 (SRP470) values, as a coincidence index for the spectral reflectance distribution (400 to 470 nm in wavelength) of the cartilage plugs with respect to intact cartilage, were 99.8 ± 6.7% in group H and 119.8 ± 10.6% in group F, and the difference between these values was significant (P = 0.005). Furthermore, the a* values were significantly correlated with the histological score (P = 0.004, r = -0.76). The SRP470 values were also significantly correlated with the histological score (P = 0.01, r = 0.67). Our findings demonstrate the ability of spectrocolorimetric measurements to predict the histological findings of cartilage plugs after autologous osteochondral grafting. In particular, the a* values and SRP470 values can be used to judge the surface condition of an osteochondral plug on the basis of objective data. Therefore, spectrocolorimetry may contribute to orthopedics, rheumatology and related research in arthritis, and arthroscopic use of this method may potentially be preferable for in vivo assessment

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.Comment: arXiv admin note: substantial text overlap with arXiv:2106.0449