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Institution

Tohoku University

EducationSendai, Japan
About: Tohoku University is a education organization based out in Sendai, Japan. It is known for research contribution in the topics: Magnetization & Population. The organization has 72116 authors who have published 170791 publications receiving 3941714 citations. The organization is also known as: Tōhoku daigaku.


Papers
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Journal ArticleDOI
01 Feb 2020
TL;DR: A survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.
Abstract: As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

414 citations

Journal ArticleDOI
TL;DR: It is revealed that one PA-selective PLA1 called mPA-PLA1alpha/LIPH is specifically expressed in hair follicles, where it has a critical role in hair growth by producing LPA through a novel LPA receptor called P2Y5.

413 citations

Journal ArticleDOI
12 Jan 2016-JAMA
TL;DR: Kidney failure risk equations developed in a Canadian population showed high discrimination and adequate calibration when validated in 31 multinational cohorts, but the original risk equations overestimated risk in some non-North American cohorts.
Abstract: Importance Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. Objective To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. Data Sources Thirty-one cohorts, including 721 357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. Study Selection Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. Data Extraction and Synthesis Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. Main Outcomes and Measures Kidney failure (treatment by dialysis or kidney transplant). Results During a median follow-up of 4 years of 721 357 participants with CKD, 23 829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overallCstatistic, 0.90; 95% CI, 0.89-0.92 at 2 years;Cstatistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibration (the difference between observed and predicted risk) was adequate in North American cohorts, but the original risk equations overestimated risk in some non-North American cohorts. Addition of a calibration factor that lowered the baseline risk by 32.9% at 2 years and 16.5% at 5 years improved the calibration in 12 of 15 and 10 of 13 non-North American cohorts at 2 and 5 years, respectively (P = .04 andP = .02). Conclusions and Relevance Kidney failure risk equations developed in a Canadian population showed high discrimination and adequate calibration when validated in 31 multinational cohorts. However, in some regions the addition of a calibration factor may be necessary.

413 citations

Journal ArticleDOI
TL;DR: In this article, support vector machine (SVM) is used to predict hourly building cooling load, which can achieve better accuracy and generalization than the traditional back-propagation (BP) neural network model.

413 citations

Journal ArticleDOI
Adrian John Bevan1, B. Golob2, Th. Mannel3, S. Prell4  +2061 moreInstitutions (171)
TL;DR: The physics of the SLAC and KEK B Factories are described in this paper, with a brief description of the detectors, BaBar and Belle, and data taking related issues.
Abstract: This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C.

413 citations


Authors

Showing all 72477 results

NameH-indexPapersCitations
John Q. Trojanowski2261467213948
Aaron R. Folsom1811118134044
Marc G. Caron17367499802
Masayuki Yamamoto1711576123028
Kenji Watanabe1672359129337
Rodney S. Ruoff164666194902
Frederik Barkhof1541449104982
Takashi Taniguchi1522141110658
Yoshio Bando147123480883
Thomas P. Russell141101280055
Ali Khademhosseini14088776430
Marco Colonna13951271166
David H. Barlow13378672730
Lin Gu13086856157
Yoichiro Iwakura12970564041
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022754
20216,412
20206,426
20196,076
20185,898