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Publicações


2013

  1. Cupertino T. H. ; SILVA, T. C. ; L. ZHAO . Classification of multiple observation sets via network modularity. Neural Computing & Applications (Internet), v. 1, pp. 1-7, 2013.

  2. T. C. Silva and L. ZHAO, “Detecting and preventing error propagation via competitive learning”. Neural Networks, v. 41, pp. 70-84, 2013.

  3. T. C. SILVA, L.  ZHAO, and T. H.  CUPERTINO, “Handwritten Data Clustering Using Agents Competition in Networks”. Journal of Mathematical Imaging and Vision, v. 45, pp. 264-276, 2013.

  4. T. H. CUPERTINO, R. A. GUELERI, and L. ZHAO, “A semi-supervised classification technique based on interacting forces”. Neurocomputing, v. 121, pp. 1-17, 2013.

  5. X. Liang and L. ZHAO, “Phase-Noise-Induced Resonance in Arrays of Coupled Excitable”. IEEE Transactions on Neural Networks and Learning Systems, v. 24, pp. 1-7, 2013.

  6. J. R. BERTINI JR., L. ZHAO, and A. A. LOPES, “An incremental learning algorithm based on the K-associated graph for non-stationary data classification”. Information Sciences, v. 246, pp. 52-68, 2013.

  7. X. Liang and L. ZHAO, “Effect of nonidentical signal phases on signal amplification of two coupled excitable neurons”. Neurocomputing, v. 121, pp. 1-19, 2013.

  8. X. LIANG, S. YANSHUK, and L. ZHAO, “Gating-signal propagation by a feed-forward neural motif”. Physical Review. E, v. 88, pp. 012910, 2013.

  9. T. H. CUPERTINO, J. HUERTAS, and L. ZHAO, “Data clustering using controlled consensus in complex networks”. Neurocomputing, v. 118, pp. 132-140, 2013.

  10. T. C. SILVA and L. ZHAO, “Uncovering overlapping cluster structures via stochastic competitive learning”. Information Sciences, v. 247, pp. 40-61, 2013

  11.  T. Pereira, D. Eroglu, G. B. Bagavi, U. Tirnakli, H.J. Jensen, “Connectivity-Driven Coherence in Complex Networks”, Phys. Rev. Lett. 110, 234103 (2013).

  12.  Catuogno, P. J.; da Silva, F. B. and Ruffino, Paulo R. C.: Decomposition of stochastic flows in manifolds with complementary distributions. Stochastics and Dynamics, 13 (4), 2013.

  13. Catuogno, P. J. ; Eyup Kizil  Vieira, M. G. O. Monotonic homotopy for trajectories of young systems. To appear in  Journal of Dynamical and Control Systems, 2013.

  14. Catuogno, P.J. ; Olivera, Christian. Time-dependent tempered generalized functions and Itô's formula. To appear in Applicable Analysis, 2013.

  15. Catuogno, P.;  D.S. Ledesma and P. Ruffino - A note on stochastic calculus in vector bundles. To appear in Lecture Notes on Mathematics, 2013.

  16. Catuogno, P. and Olivera, C. - Strong solution of the stochastic Burgers equation. To appear in Applicable Analysis, 2013.

  17. Catuogno, P. and Olivera, C. - Lp-solutions of the stochastic transport equation. Random Operators and Stochastic Equations, v. 21, p. 125-134, 2013.

  18. HOEGELE, M. ; Ruffino, P. R.  - Averaging along Lévy Diffusions in Foliated Spaces. Potsdam, Germany: Preprint Potsdam University, Germany, 2013.

  19. Costa, P. H. P. ; Ruffino, P.R. -  Degenerate semigroups and stochastic flows of mappings in foliated manifolds 2013 (Arxiv 1306.0123, submetido).

  20. Rodrigues, C. S., Ruffino, P. R. -  A family of rotation number for discrete random dynamics on the circle 2013 (Arxiv 1308.1349, submetido).

  21. Lucas W. Rossi  (IFUSP), Paul K. Radtke (HUB) and Carla Goldman (IFUSP) - Long-range cargo transport on crowded microtubules: the motor jamming mechanism. To appear in Physica A.

  22. Arruda, D., Publio, R. and Roque, A. C. (2013). The periglomerular cell of the olfactory bulb and its role in controlling mitral cell spiking: a computational model. PLoS ONE 8(2): e56148.

  23. Roque, A. C. (2013). Foreword to the special issue on LASCON 2012. Journal of Neuroscience Methods 220(2): 99.

  24. Arantes, R., Tejada, J., Bosco, G. G., Morato, S. and Roque, A. C. (2013). Mathematical methods to model rodent behavior in the elevated plus-maze. Journal of Neuroscience Methods 220(2): 141-148.

  25. Arantes, R., Tejada, J., Bosco, G. G., Morato, S. and Roque, A. C. (2013). A novel anxiety index for the rat behavior in the elevated plus-maze. BMC Neuroscience 14(Suppl 1): P326.

  26. A possible new mechanism for the control of miRNA expression in neurons. Kinjo ER, Higa GS, de Sousa E, Casado OA, Damico MV, Britto LR, Kihara AH. Exp Neurol. 2013 Oct;248:546-58.

  27. Developmental and functional expression of miRNA-stability related genes in the nervous system. de Sousa É, Walter LT, Higa GS, Casado OA, Kihara AH. PLoS One. 2013 May 20;8(5):e56908.

  28. A new and reliable guide for studies of neuronal loss based on focal lesions and combinations of in vivo and in vitro approaches. Paschon V, Higa GS, Walter LT, de Sousa E, Zuzarte FC, Weber VR, Resende RR, Kihara AH. PLoS One. 2013 Apr 9;8(4):e60486.

  29. N. Boers, B. Bookhagen, H.M.J. Barbosa, N. Marwan, J. Kurths and J.A. Marengo, 2013: Prediction of Extreme Floods in the Eastern Central Andes: A Complex Networks Approach. Submitted to Nature Geoscience.

  30. Domingos H. U. Marchetti e Walter F. Wreszinski. "Asymptotic Time Decay in Quantum Physics: A Selective Review and Some New Results", Rev. Math. Phys. 25, 1330007 (2013).

  31. D. H. U. Marchetti, V. Sidoravicius e M. E. Vares. "Commentary to: Oriented Percolation in One-Dimension 1/|x-y|² Percolation Models", Journ. Stat. Phys. 150, 804--805 (2013)

  32. Peng Ji, Thomas K.DM. Peron, Peter J. Menck, Francisco Aparecido Rodrigues, Jürgen Kurths, Cluster Explosive Synchronization in complex networks, Physical Review Letters, 110, 218701 (2013)

  33. Thomas Kauê Dal'Maso Peron, Francisco Aparecido Rodrigues, Jürgen Kurths, Synchronization in random clustered networks, Physical Review E, 87, 032807 (2013)

  34. Guilherme F. de Arruda, Thomas Kauê Dal'Maso Peron, Marinho Gomes de Andrade, Jorge Alberto Achcar, Francisco Aparecido Rodrigues, The influence of network properties on the synchronization of Kuramoto oscillators quantified by a Bayesian regression analysis, Journal of Statistical Physics, Volume 152, Issue 3, pp 519-533, 2013)

  35. Araújo, Moacyr ; Ambrizzi, T. ; Sampaio, G. . Sumario Executivo do Volume 1 - Base Cientifica das Mudanças Climáticas. Contribuição do Grupo de Trabalho 1 para o 1º Relatório de Avaliação Nacional do Painel Brasileiro de Mudanças Climáticas. 1. ed. Rio de Janeiro: , 2012. v. 1. 34p.

  36. SALATI, E. ; Dourojeanni, M. ; MUNDIM, A. ; Sampaio, G. ; LOVEJOY, T. . The Brazilian Amazon. In: JODI A. HILTY; CHARLES C. CHESTER; MOLLY S. CROSS. (Org.). Climate and Conservation. 01ed.Washington: Island Press, 2012, v. 01, p. 45-55.

  37. Marengo, Jose A. ; Borma, Laura S. ; Rodriguez, Daniel A. ; Pinho, Patrícia ; Soares, Wagner R. ; ALVES, LINCOLN M. . Recent Extremes of Drought and Flooding in Amazonia: Vulnerabilities and Human Adaptation. American Journal of Climate Change, v. 02, p. 87-96, 2013.

  38. Marengo, Jose A. ; ALVES, LINCOLN M. ; Soares, Wagner R. ; Rodriguez, Daniel A. ; Camargo, Helio ; Riveros, Marco Paredes ; Pabló, Amelia Diaz . Two contrasting severe seasonal extremes in Tropical South America in 2012: Floods in Amazonia and Drought in Northeast Brazil. Journal of Climate, v. 26, p. 130712132648002-22, 2013.

  39. Marengo, Jose Antonio ; ALVES, LINCOLN M. ; Tomasella, Javier ; Nobre, Carlos A. ; Soares, Wagner R. . Extreme climatic events in the Amazon basin. Theoretical and Applied Climatology, v. 107, p. 73-85, 2013.

  40. Marengo J. A. ; J. Ronchail ; ALVES, LINCOLN M ; J Baez . Tropical South America East of the Andes. Bulletin of the American Meteorological Society, v. 93, p. S175-S177, 2013.

  41. ALVES, L. M.; Marengo, J. A.; Cavalcanti, I.F.A. PARTE I – CARACTERIZAÇÃO GERAL DAS SECAS: Histórico das secas na Amazônia. In: BORMA, L. S.; NOBRE, C. A (Org.). Secas na Amazônia: causas e conseqüências. Oficina de Textos, São Paulo, 2013.

  42. ALVES, L. M.; Cavalcanti, I.F.A.; Silveira, V.P.; Marengo, J. A. PARTE II – ASPECTOS CLIMATOLÓGICOS – OBSERVAÇÕES: Classificação dos anos de seca. In: BORMA, L. S.; NOBRE, C. A (Org.). Secas na Amazônia: causas e conseqüências. Oficina de Textos, São Paulo, 2013;

  43. Cavalcanti, I.F.A.; Silveira, V.P.; ALVES, L. M. PARTE II – ASPECTOS CLIMATOLÓGICOS – OBSERVAÇÕES: Características atmosféricas e oceânicas em anos de seca. In: BORMA, L. S.; NOBRE, C. A (Org.). Secas na Amazônia: causas e conseqüências. Oficina de Textos, São Paulo, 2013.

  44. Araújo, Moacyr ; Ambrizzi, T. ; ALVES, LINCOLN M. . Sumario Executivo do Volume 1 - Base Cientifica das Mudanças Climáticas. Contribuição do Grupo de Trabalho 1 para o 1º Relatório de Avaliação Nacional do Painel Brasileiro de Mudanças Climáticas. 1. ed. Rio de Janeiro: , 2012. v. 1. 34p

  45. Solmam S., E. Sanchez, P. Samuelsson, R. da Rocha, L. Li, J. MARENGO, N. Pessacg , A. Remedio, S. C. Chou and  H. Berbery, Evaluation of an ensemble of regional climate model simulations over South America: Models’s performance and major uncertainties submitted to Climate Dynamics, 2012.

  46. MARENGO, J, S. Chou, C. Mourao, S. Solman, E. Sanchez, P. Samuelsson, R. da Rocha, L. Li, N. Pessacg, A. R.C. Remedio,  A. F. Carril, I.F Cavalcanti, D. Jacob, 2013: Simulation of the drought of Amazonia in 2005 using a suite of seven regional climate models from the CLARIS LPB Project, Climate Dynamics.

  47. Boers, N. Bookhagen, B., Marwan, N., Kurth, J, Marengo, J, (2013) Complex Networks identifiy Extreme rainfall events of the South American Monsoon System, Geophysical Research Letters.

  48. JH Feldhoff, S Lange, J Volkholz, JF Donges, J Kurths, FW Gerstengarbe "Complex networks for climate model evaluation with application to statistical vs. dynamical modeling of South American climate", submetido ao Climate Dynamics, 2013/10/28.

  49. QUILES, M.G. ; ZORZAL, E. R. ; MACAU, E. E. N. . A Dynamical Model for Community Detection in Complex Networks. In: The International Joint Conference on Neural Networks (IJCNN), 2013, Dallas. Los Alamitos: IEEE Computer Press, 2013. v. 1. p. 1-8.

  50. QUILES, M. G. ; BASGALUPP, M. P. ; BARROS, R. . An Oscillatory Correlation Model for Semi-Supervised Classification. Learning and Nonlinear Models, v. 11, p. 3-10, 2013.
Instituições participantes:

0
Universidade de São Paulo


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Humboldt-Universität zu Berlin


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Instituto Nacional de Pesquisas Espaciais


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Potsdam-Institut für Klimafolgenforschung


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Universidade Federal de São Paulo


0
Universität Potsdam


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Universidade Federal do ABC


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Universidade Estadual de Campinas