867 research outputs found
Almost self-duality and Harada rings
No full textAbstractThe existence of self-duality for left Harada rings was investigated by J. Kado and K. Oshiro [J. Algebra 211 (1999) 384–408]. Recently the author constructed examples of left Harada rings without self-duality [J. Algebra 241 (2001) 731–744]. In this paper, we investigate almost self-duality and rings of a certain class, which contains right co-Harada rings (equivalently left Harada rings). Here almost self-duality is a generalization of self-duality. The main purpose of the paper is to show that every ring of the class (particularly every right co-Harada ring) has an almost self-duality
DCASE 2022 Challenge Task 2 Additional Training Dataset
No full textDescription
This dataset is the "additional training dataset" for the DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques".
Condition of use
This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Citation
If you use this dataset, please cite all the following three papers.
Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, 2022. [URL]
Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In arXiv e-prints: 2205.13879, 2022. [URL]
Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL]
Contact
If there is any problem, please contact us:
Kota Dohi, [email protected]
Daisuke Niizumi, [email protected]
Yohei Kawaguchi, [email protected]
Keisuke Imoto, [email protected]
Ground Truth for DCASE 2021 Challenge Task 2 Evaluation Dataset
No full textDescription
This data is the ground truth for the "evaluation dataset" for the DCASE 2021 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions".
In the task, three datasets have been released: "development dataset", "additional training dataset", and "evaluation dataset". The evaluation dataset was the last of the three released and includes around 200 samples for each machine type, section index, and domain, none of which have a condition label (i.e., normal or anomaly). This ground truth dataset contains the condition labels.
Data format
The CSV file for each machine type, section index, and domain includes the ground truth data like the following:
---------------------------------
section_03_source_test_0000.wav,1
section_03_source_test_0001.wav,1
...
section_03_source_test_0198.wav,0
section_03_source_test_0199.wav,1
---------------------------------
The first column shows the name of a wave file. The second column shows the condition label (i.e., 0: normal or 1: anomaly).
How to use
A script for calculating the AUC, pAUC, precision, recall, and F1 scores for the "evaluation dataset" is available on the Github repository [URL]. The ground truth data are used by this system. For more information, please see the Github repository.
Conditions of use
This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Publication
If you use this dataset, please cite all the following three papers:
Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo, "Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions," in arXiv e-prints: 2106.04492, 2021. [URL]
Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito, "ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions," in arXiv e-prints: 2106.02369, 2021. [URL]
Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," in arXiv e-prints: 2105.02702, 2021. [URL]
Feedback
If there is any problem, please contact us:
Yohei Kawaguchi, [email protected]
Daisuke Niizumi, [email protected]
Keisuke Imoto, [email protected]
A study on Harada Shigeyoshi's Jujireki Chukai (Study of the History of Mathematics 2022)
No full textAfter being introduced to Japan, the important ancient Chinese calendar, the Shoushi Calendar, was reprinted and disseminated. The Shoushili Yi in Yuan Shi·Li zhi is an important document about the ancient Chinese calendar theory. No one studied it in the Ming and Qing dynasties, but Japanese scholars in the Edo period commented on it, such as Takebe Katahiro (1664-1739), Nishimura Tōsato (1718-1787), and Harada Shigeyoshi (1740-1807), they annotated the Shoushili Yi. The article firstly verifies that the author of the Jujireki Chukai in the library affiliated to Tohoku University is Harada Shigeyoshi, not Takahashi Yoshitoki (1764-1804). Secondly, an investigation was carried out on Harada Shigeyoshi and his writings. The investigation found that there were three manuscripts of Harada Shigeyoshi's Jujireki Chukai, and the contents of the annotations and knowledge sources were verified and sorted out. It is believed that the Jujireki Chukai cited the contents of Tianwen Tujie Fahui (Nakane Genkei), Lisuan Quanshu (Mei Wending) and Juji Kai (Nishimura Tōsato) mostly. Finally, the article analyzes the annotations on “Yanqi (Collect or modify data for the solar terms)” and “Buyong Jinian Rifa (Abolition of the calendar epoch)” in Harada Shigeyoshi's Jujireki Chukai, and thinks that Harada's annotations in “Yanqi” through diagrams are commendable. The “Buyong Jinian Rifa” section is rich in annotations, which supplement the three possible situations that Li Qian and Qi Lvqian proposed to calculate Yanji Shangyuan. The two new situations which do not provide calculation procedures are similar to the methods of Li Qian and Qi Lvqian, and the other two situations are caculated by Seki Takakazu's Jianguan-Method. This method is essentially the same as that of Dayan-Zongshu-Method (Da-yan Rule) [大衍總數術]
A successful model of regional healthcare information exchange in Japan: Case Study in Kagawa Prefecture
No full textIn this study, we focused on analysis of healthcare data exchange over the network. For the advance of broadband capability development, many governments expect online medical information exchange between medical institutions. Japanese government also has tried to deploy ICT in the healthcare field. In Japan, many healthcare ICT projects started, but almost of all the projects face many issues and failed to continue. This situation caused us to clarify the success factor of healthcare information exchange network. For inspecting the success factors, we analyzed information access of healthcare systems in Kagawa prefecture of Japan. Kagawa prefecture is one of the most advance areas for healthcare information technology. We analyzed four medical ICT projects in Kagawa prefecture: K-MIX, Critical Pathway for Diabetes, E-prescription, and PHR. In addition, we inspected characteristics of exchanged data in the network, and stakeholder involved in these projects. This analysis lets us find various types of healthcare ICT projects. Characteristic of data processed in the projects caused differences of characteristic of the projects. On the other hand, multiple systems process same data, though the project does not share the data itself. Considering various types of medical information exchanges projects, we propose classification and standard format of exchanged data according to their characteristic are critical for efficient business deployment. --e-Health,regional healthcare information exchange,EHR
DCASE 2020 Challenge Task 2 Additional Training Dataset
No full textDescription
This dataset is the "additional training dataset" for the DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" [task description].
In the task, three datasets have been or will be released: "development dataset", "additional training dataset", and "evaluation dataset". This additional training dataset was released before the "evaluation dataset". This dataset includes around 1,000 normal samples for each Machine Type and Machine ID used in the evaluation dataset and can be used for model training in advance.
The recording procedure and data format are the same as the development dataset. The Machine IDs in this dataset are different from those in the development dataset. For more information, please see the pages of the development dataset and the task description.
Directory structure
Once you unzip the downloaded files from Zenodo, you can see the following directory structure. Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as:
/eval_data
/ToyCar
/train (Only normal data for all Machine IDs are included.)
/normal_id_05_00000000.wav
...
/normal_id_05_00000999.wav
/normal_id_06_00000000.wav
...
/normal_id_07_00000999.wav
/ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)
/fan
/pump
/slider
/valve
The paths of audio files are:
"/eval_data//train/normal_id__[0-9]+.wav"
For example, the Machine Type and Machine ID of "/ToyCar/train/normal_id_05_00000000.wav" are "ToyCar" and "05", respectively, and its condition is normal (This dataset includes only normal samples).
Baseline system
A simple baseline system is available on the Github repository [URL]. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.
Conditions of use
This dataset was created jointly by NTT Corporation and Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Publication
If you use this dataset, please cite all the following three papers:
Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. [pdf]
Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. [pdf]
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring," in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. [pdf]
Feedback
If there is any problem, please contact us:
Yuma Koizumi, [email protected]
Yohei Kawaguchi, [email protected]
Keisuke Imoto, [email protected]
Choroidal thickening prior to anterior recurrence in patients with Vogt-Koyanagi-Harada disease
No full textAim: To assess choroidal thickness changes associated with anterior segment recurrences in patients with Vogt-Koyanagi-Harada (VKH) disease using enhanced depth imaging optical coherence tomography (EDI-OCT). Methods: EDI-OCT images were obtained periodically from 11 VKH disease patients (22 eyes) who were followed-up due to anterior segment recurrences. Subfoveal choroidal thickness (SCT) values at the following stages were evaluated: (1) during the remission phase, (2) one month before detecting the anterior recurrence, (3) during the anterior recurrence, and (4) after systemic prednisolone (PSL) treatment leading to remission. In comparison with SCT values in remission as baseline, the changing ratios of SCT were statistically analyzed at subsequent three stages. Results: The average of the SCT changing ratios compared to the remission phase significantly increased to 1.45 ± 0.11 during anterior segment recurrences (P=0.00044) lacking any funduscopic signs of posterior involvement. Interestingly, the average SCT ratio one month before detecting the recurrence had already increased to 1.30 ± 0.08 (P=0.002). After the PSL treatment, the ratio of SCT recovered to 0.95 ± 0.03 which was equivalent with the remission level. However, in patients with their remission SCT values less than 240 μm, the SCT ratio did not increase significantly at any time points evaluated. Conclusions: The choroid in eyes with VKH disease thickened in association with the anterior segment recurrence, and this thickening was observed prior to the recurrence. EDI-OCT may be useful for detecting latent choroidal inflammation in VKH disease, whereas it may not for patients with the relatively thin choroid
Ground Truth for DCASE 2020 Challenge Task 2 Evaluation Dataset
No full textDescription
This data is the ground truth for the "evaluation dataset" for the DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" [task description].
In the task, three datasets have been released: "development dataset", "additional training dataset", and "evaluation dataset". The evaluation dataset was the last of the three released and includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have any condition label (i.e., normal or anomaly). This ground truth data contains the condition labels.
Data format
The ground truth data is a CSV file like the following:
---------------------------------
fan
id_01_00000000.wav,normal_id_01_00000098.wav,0
id_01_00000001.wav,anomaly_id_01_00000064.wav,1
...
id_05_00000456.wav,anomaly_id_05_00000033.wav,1
id_05_00000457.wav,normal_id_05_00000049.wav,0
pump
id_01_00000000.wav,anomaly_id_01_00000049.wav,1
id_01_00000001.wav,anomaly_id_01_00000039.wav,1
...
id_05_00000346.wav,anomaly_id_05_00000052.wav,1
id_05_00000347.wav,anomaly_id_05_00000080.wav,1
slider
id_01_00000000.wav,anomaly_id_01_00000035.wav,1
id_01_00000001.wav,anomaly_id_01_00000176.wav,1
...
---------------------------------
"Fan", "pump", "slider", etc mean "Machine Type" names. The lines following a Machine Type correspond to pairs of a wave file in the Machine Type and a condition label. The first column shows the name of a wave file. The second column shows the original name of the wave file, but this can be ignored by users. The third column shows the condition label (i.e., 0: normal or 1: anomaly).
How to use
A system for calculating AUC and pAUC scores for the "evaluation dataset" is available on the Github repository [URL]. The ground truth data is used by this system. For more information, please see the Github repository.
Conditions of use
This dataset was created jointly by NTT Corporation and Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Publication
If you use this dataset, please cite all the following three papers:
Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. [pdf]
Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. [pdf]
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring," in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. [pdf]
Feedback
If there is any problem, please contact us:
Yuma Koizumi, [email protected]
Yohei Kawaguchi, [email protected]
Keisuke Imoto, [email protected]
Vogt-Koyanagi-Harada disease: inquiry into the genesis of a disease name in the historical context of Switzerland and Japan
No full textPurpose: To delineate the historical steps associated with the genesis of the name and the definition of Vogt-Koyanagi-Harada (VKH) disease. Methods: A bibliographical review of the major publications that were relevant to the original development of the name of the clinical entity known today as Vogt-Koyanagi-Harada disease, in the historical context of the early 20th century. Results: Three distinct time periods can be considered to be important in terms of providing a historical perspective on VKH disease. Given that the cutaneous manifestations of VKH disease are so characteristic, these could not have been missed even before the actual clinical entity of VKH was recognized in the early 20th century. Indeed, several authors, including the Arabic doctor Mohammad-al-Ghâfiqî in the 12th century as well as Jacobi, Nettelship and Tay in the 19th century, described poliosis, neuralgias and hearing disorders. Many of these cases were probably due to sympathetic ophthalmia, but some were clearly VKH cases. The second phase is characterized by the surge of articles that appeared early in the 20th century that defined the disease more precisely. A number of these authors subsequently became associated with the disease name, the first being Alfred Vogt from Switzerland, followed by Japanese researchers. Yoshizo Koyanagi was in fact not the first Japanese author to describe the disease; this honor goes to the first Japanese Professor of Ophthalmology at the University of Tokyo, Dr. Jujiro Komoto, who published in a German language journal, Klinische Monatsblätter für Augenheilkunde in 1911. Yoshizo Koyanagi published his first report in the Nippon Ganka Gakkai Zasshi 3 years later, in 1914, but it was a much later article, one published in 1929, that definitively associated his name with the disease. In this review article, Koyanagi reported 16 cases, of which six were his own cases, that beautifully illustrate the natural course of the disease. In this same time period, Einosuke Harada, in an article published in Nippon Ganka Gakkai Zasshi in 1926 that was based on several case studies, comprehensively described a syndrome that included (1) a prodromal phase of malaise and meningeal irritation; (2) bilateral uveitis of diverse intensity; (3) bilateral retinal detachments spontaneously resolving; (4) integumentary changes; (5) lymphocytosis of the spinal fluid; (6) dysacousia. It is now accepted that Vogt-Koyanagi disease and the syndrome described by Harada are one entity with a diverse clinical spectrum bearing the universally accepted name of Vogt-Koyanagi-Harada disease. The third phase and most recent phase is characterized by the rapid progress made in terms of knowledge of the physiopathology of the disease, primarily due to the development of immunological methods. The evidence accumulated to date clearly points towards an autoimmune Th1 disease directed against proteins associated with choroidal melanin. Other analytical techniques, such as indocyanine green angiography, have enabled researchers to monitor more closely the primary lesional process at the level of the choroid, and standardized diagnostic criteria have been generated in the recent past. Conclusion: Those who earn scientific merit in clinical medicine are the ones who are able to visualize an overview based on the synthesis of ‘new' medical facts that have been made available, usually reported singly by several, unassociated authors concomitantly. This is certainly the case for Yoshizo Koyanagi and Einosuke Harada. Conversely, Alfred Vogt was primarily lucky in that he encountered and subsequently precisely described the first case in the literatur
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