Memorial Volume for Y. Nambu 2016
DOI: 10.1142/9789813108332_0014
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Nambu at Work

Abstract: This paper is based on a talk delivered on 16 November, 2015 in Osaka at the Nambu's Century: International Symposium on Yoichiro Nambu's Physics.Yoichiro Nambu, whose life and seminal contributions to Physics we celebrate here, went in 1952 to the Institute for Advanced Study in Princeton.Shortly after his arrival there, J. Robert Oppenheimer, the Institute's director, put Yoichiro and the other new arrivals on notice that though Albert Einstein was a professor at the Institute, and therefore had an office th… Show more

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Cited by 3 publications
(4 citation statements)
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“…13 N-doped TiO 2 samples with various N amounts were synthesized by heating the TiO 2 precursor in NH 3 atmosphere at various temperatures. 14,15 Three samples with increasing N amounts were prepared under 575, 625, and 700 °C and named as N-TiO 2 -1, -2, and -3, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…13 N-doped TiO 2 samples with various N amounts were synthesized by heating the TiO 2 precursor in NH 3 atmosphere at various temperatures. 14,15 Three samples with increasing N amounts were prepared under 575, 625, and 700 °C and named as N-TiO 2 -1, -2, and -3, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…Supportvectormachine(SVM)wascoinedbyCortesandVapnik (1995) Freund (2009). Linear Programming Boosting (LPBoost) is anothertypeofboostingalgorithm,whichbelongstotheclassofmarginmaximizing supervisedlearningalgorithm (Warmuthetal.,2006).Adaptivelogisticregression isthemostwidelyusedlearningmodelinthefieldofmedicalresearchforitsbinary data (Friedman,Hastie,&Tibshirani,2000).Thisisalsoapplicableinlinkprediction for the complex network as it deals with binary data (link existence and link nonexistence).…”
Section: Svm Classifiermentioning
confidence: 99%
“…In machine learning, an effective method to improve the performance and stability is ensemble learning [19]. Adaboost [11] is a representative ensemble learning algorithm, which combines the predictions of several trees with a weight sum to form the final prediction. In the training process, the weights of samples are updated according to those misclassified samples in previous training process.…”
Section: Adaboostmentioning
confidence: 99%
“…Therefore, machine learning ensemble meta-algorithms are used to improve the stability and accuracy. Two representative machine learning ensemble meta-algorithms are Adaptive Boosting (AdaBoost) [11] and Bootstrap aggregating (Bagging) [12]. Adaptive Boosting is formulated by Yoav Freund and Robert Schapire.…”
Section: Introductionmentioning
confidence: 99%