ReP USP - Resultado da busca

Trabalho de evento
Predicting soft robot's locomotion fitness (2021)
Biazzi, Renata Biaggi ; Fujita, André ; Takahashi, Daniel Yasumasa
Source: Proceedings. Conference titles: Genetic and Evolutionary Computation Conference Companion - GECCO. Unidades: IME, BIOINFORMÁTICA
Subjects: ROBÓTICA, LOCOMOÇÃO
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ABNT
BIAZZI, Renata Biaggi e FUJITA, André e TAKAHASHI, Daniel Yasumasa. Predicting soft robot's locomotion fitness. 2021, Anais.. New York: ACM, 2021. Disponível em: https://doi.org/10.1145/3449726.3459417. Acesso em: 30 jul. 2024.
APA
Biazzi, R. B., Fujita, A., & Takahashi, D. Y. (2021). Predicting soft robot's locomotion fitness. In Proceedings. New York: ACM. doi:10.1145/3449726.3459417
NLM
Biazzi RB, Fujita A, Takahashi DY. Predicting soft robot's locomotion fitness [Internet]. Proceedings. 2021 ;[citado 2024 jul. 30 ] Available from: https://doi.org/10.1145/3449726.3459417
Vancouver
Biazzi RB, Fujita A, Takahashi DY. Predicting soft robot's locomotion fitness [Internet]. Proceedings. 2021 ;[citado 2024 jul. 30 ] Available from: https://doi.org/10.1145/3449726.3459417
Artigo de periodico
Spectral density of random graphs: convergence properties and application in model fitting (2021)
Santos, Suzana de Siqueira ; Fujita, André ; Matias, Catherine
Source: Journal of Complex Networks. Unidade: IME
Assunto: TEORIA DOS GRAFOS
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ABNT
SANTOS, Suzana de Siqueira e FUJITA, André e MATIAS, Catherine. Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, v. 9, n. 6, p. 1-27, 2021Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnab041. Acesso em: 30 jul. 2024.
APA
Santos, S. de S., Fujita, A., & Matias, C. (2021). Spectral density of random graphs: convergence properties and application in model fitting. Journal of Complex Networks, 9( 6), 1-27. doi:10.1093/comnet/cnab041
NLM
Santos S de S, Fujita A, Matias C. Spectral density of random graphs: convergence properties and application in model fitting [Internet]. Journal of Complex Networks. 2021 ; 9( 6): 1-27.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1093/comnet/cnab041
Vancouver
Santos S de S, Fujita A, Matias C. Spectral density of random graphs: convergence properties and application in model fitting [Internet]. Journal of Complex Networks. 2021 ; 9( 6): 1-27.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1093/comnet/cnab041
Artigo de periodico
Convolution-based linear discriminant analysis for functional data classification (2021)
Guzman, Grover Enrique Castro ; Fujita, André
Source: Information Sciences. Unidade: IME
Subjects: ANÁLISE MULTIVARIADA, ESTATÍSTICA DE PROCESSOS ESTOCÁSTICOS
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ABNT
GUZMAN, Grover Enrique Castro e FUJITA, André. Convolution-based linear discriminant analysis for functional data classification. Information Sciences, v. 581, p. 469-478, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.09.057. Acesso em: 30 jul. 2024.
APA
Guzman, G. E. C., & Fujita, A. (2021). Convolution-based linear discriminant analysis for functional data classification. Information Sciences, 581, 469-478. doi:10.1016/j.ins.2021.09.057
NLM
Guzman GEC, Fujita A. Convolution-based linear discriminant analysis for functional data classification [Internet]. Information Sciences. 2021 ; 581 469-478.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1016/j.ins.2021.09.057
Vancouver
Guzman GEC, Fujita A. Convolution-based linear discriminant analysis for functional data classification [Internet]. Information Sciences. 2021 ; 581 469-478.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1016/j.ins.2021.09.057
Artigo de periodico
Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers (2021)
Fernandes, Pedro Augusto ; Kinker, Gabriela Sarti ; Navarro, Bruno Viana; Carvalho, Vinícius Jardim; Paz, Edson Duarte Ribeiro; Córdoba-Moreno, Marlina Olyissa ; Santos-Silva, Débora; Muxel, Sandra Márcia ; Fujita, André ; Moraes, Carolina Borsoi ; Nakaya, Helder Takashi Imoto ; Buckeridge, Marcos ; Markus, Regina P
Source: Melatonin Research. Unidades: IB, IME, FCF, BIOINFORMÁTICA
Subjects: MELATONINA, DOENÇAS INFECCIOSAS
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ABNT
FERNANDES, Pedro Augusto et al. Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers. Melatonin Research, v. 24, n. 1, p. 189-205, 2021Tradução . . Disponível em: https://doi.org/10.32794/mr11250090. Acesso em: 30 jul. 2024.
APA
Fernandes, P. A., Kinker, G. S., Navarro, B. V., Carvalho, V. J., Paz, E. D. R., Córdoba-Moreno, M. O., et al. (2021). Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers. Melatonin Research, 24( 1), 189-205. doi:10.32794/mr11250090
NLM
Fernandes PA, Kinker GS, Navarro BV, Carvalho VJ, Paz EDR, Córdoba-Moreno MO, Santos-Silva D, Muxel SM, Fujita A, Moraes CB, Nakaya HTI, Buckeridge M, Markus RP. Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers [Internet]. Melatonin Research. 2021 ; 24( 1): 189-205.[citado 2024 jul. 30 ] Available from: https://doi.org/10.32794/mr11250090
Vancouver
Fernandes PA, Kinker GS, Navarro BV, Carvalho VJ, Paz EDR, Córdoba-Moreno MO, Santos-Silva D, Muxel SM, Fujita A, Moraes CB, Nakaya HTI, Buckeridge M, Markus RP. Melatonin-Index as a biomarker for predicting the distribution of presymptomatic and asymptomatic SARS-CoV-2 carriers [Internet]. Melatonin Research. 2021 ; 24( 1): 189-205.[citado 2024 jul. 30 ] Available from: https://doi.org/10.32794/mr11250090
Artigo de periodico
Efficient Laplacian spectral density computations for networks with arbitrary degree distributions (2021)
Guzman, Grover Enrique Castro ; Stadler, Peter F. ; Fujita, André
Source: Network Science. Unidade: IME
Assunto: TEORIA DOS GRAFOS
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ABNT
GUZMAN, Grover Enrique Castro e STADLER, Peter F. e FUJITA, André. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions. Network Science, v. 9, n. 3, p. 312-327, 2021Tradução . . Disponível em: https://doi.org/10.1017/nws.2021.10. Acesso em: 30 jul. 2024.
APA
Guzman, G. E. C., Stadler, P. F., & Fujita, A. (2021). Efficient Laplacian spectral density computations for networks with arbitrary degree distributions. Network Science, 9( 3), 312-327. doi:10.1017/nws.2021.10
NLM
Guzman GEC, Stadler PF, Fujita A. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions [Internet]. Network Science. 2021 ; 9( 3): 312-327.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1017/nws.2021.10
Vancouver
Guzman GEC, Stadler PF, Fujita A. Efficient Laplacian spectral density computations for networks with arbitrary degree distributions [Internet]. Network Science. 2021 ; 9( 3): 312-327.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1017/nws.2021.10
Trabalho de evento-anais periodico
OA02.02 development of machine learning model to estimate overall survival in patients with advanced NSCLC and ECOG-PS > 1 (2021)
Cunha, M; Borges, A. P; Carvalho, Vinicius Jardim ; Fujita, André ; Castro, G. D
Source: Journal of Thoracic Oncology. Conference titles: World Conference on Lung Cancer Worldwide. Unidades: IME, BIOINFORMÁTICA
Subjects: INTELIGÊNCIA ARTIFICIAL, CUIDADOS PALIATIVOS
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ABNT
CUNHA, M et al. OA02.02 development of machine learning model to estimate overall survival in patients with advanced NSCLC and ECOG-PS > 1. Journal of Thoracic Oncology. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1016/j.jtho.2021.08.038. Acesso em: 30 jul. 2024. , 2021
APA
Cunha, M., Borges, A. P., Carvalho, V. J., Fujita, A., & Castro, G. D. (2021). OA02.02 development of machine learning model to estimate overall survival in patients with advanced NSCLC and ECOG-PS > 1. Journal of Thoracic Oncology. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1016/j.jtho.2021.08.038
NLM
Cunha M, Borges AP, Carvalho VJ, Fujita A, Castro GD. OA02.02 development of machine learning model to estimate overall survival in patients with advanced NSCLC and ECOG-PS > 1 [Internet]. Journal of Thoracic Oncology. 2021 ; 16( 10): S850.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1016/j.jtho.2021.08.038
Vancouver
Cunha M, Borges AP, Carvalho VJ, Fujita A, Castro GD. OA02.02 development of machine learning model to estimate overall survival in patients with advanced NSCLC and ECOG-PS > 1 [Internet]. Journal of Thoracic Oncology. 2021 ; 16( 10): S850.[citado 2024 jul. 30 ] Available from: https://doi.org/10.1016/j.jtho.2021.08.038
Artigo de periodico
Granger causality among graphs and application to functional brain connectivity in autism spectrum disorder (2021)
Ribeiro, Adèle Helena ; Vidal, Maciel Calebe ; Sato, João Ricardo ; Fujita, André
Source: Entropy. Unidade: IME
Subjects: GRAFOS ALEATÓRIOS, TRANSTORNO AUTÍSTICO
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ABNT
RIBEIRO, Adèle Helena et al. Granger causality among graphs and application to functional brain connectivity in autism spectrum disorder. Entropy, v. 23, n. 9, p. 1-21, 2021Tradução . . Disponível em: https://doi.org/10.3390/e23091204. Acesso em: 30 jul. 2024.
APA
Ribeiro, A. H., Vidal, M. C., Sato, J. R., & Fujita, A. (2021). Granger causality among graphs and application to functional brain connectivity in autism spectrum disorder. Entropy, 23( 9), 1-21. doi:10.3390/e23091204
NLM
Ribeiro AH, Vidal MC, Sato JR, Fujita A. Granger causality among graphs and application to functional brain connectivity in autism spectrum disorder [Internet]. Entropy. 2021 ; 23( 9): 1-21.[citado 2024 jul. 30 ] Available from: https://doi.org/10.3390/e23091204
Vancouver
Ribeiro AH, Vidal MC, Sato JR, Fujita A. Granger causality among graphs and application to functional brain connectivity in autism spectrum disorder [Internet]. Entropy. 2021 ; 23( 9): 1-21.[citado 2024 jul. 30 ] Available from: https://doi.org/10.3390/e23091204

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