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Publicações


2014

  1. T. C. Silva and L. Zhao, “High-level pattern-based classification via tourist walks in networks”. Information Sciences, v. 294, p. 109-126, 2015.

  2. 2. M. G. Carneiro, L. Zhao, A. A. Lopes, and J. L. G. Rosa. “Network-based data classification: combining K-associated optimal graphs and high-level prediction”. Journal of The Brazilian Computer Society, v. 20, p. 1-14, 2014.

  3. 3. D. A. Vega-Oliveros, L. Berton, A. M. Eberle, A. A. Lopes, and L. Zhao, “Regular graph construction for semi-supervised learning”. Journal of Physics. Conference Series, v. 490, p. 012022, 2014.

  4. 4. T. H. Cupertino, L. Zhao, and M. G. Carneiro. “Network-based supervised data classification by using an heuristic of ease of access”. Neurocomputing, v. 144, p. 1-9, 2014.

  5. 5. F. A. Breve, L. Zhao, and M. G. Quiles, “Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise”. Neurocomputing, accepted for publication, 2014.

  6. M. Hoefner, H.J. Wuensche, and F. Hennerberger. A random laser as a dynamical network. New Journal of Physics, 2013. 11;

  7. Tomov, P., Pena, R.F.O., Zaks, M.A., and Roque, A.C. (2014). Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types. Frontiers in Computational Neuroscience, 8:103. doi: 10.3389/fncom.2014.00103.

  8. Tejada, J., Garcia-Cairasco, N., and Roque, A.C. (2014). Combined Role of Seizure-Induced Dendritic Morphology Alterations and Spine Loss in Newborn Granule Cells with Mossy Fiber Sprouting on the Hyperexcitability of a Computer Model of the Dentate Gyrus. PLOS Computational Biology, 10:e1003601. doi: 10.1371/journal.pcbi.1003601.

  9. Simões-de-Souza, F.M., Antunes, G., and Roque, A.C. (2014). Electrical responses of three classes of granule cells of the olfactory bulb to synaptic inputs in different dendritic locations. Frontiers in Computational Neuroscience, 8:128. doi: 10.3389/fncom.2014.00128.

  10. Costa, A.A., Morato, S., Roque, A.C., and Tinós, R. (2014). A computational model for exploratory activity of rats with different anxiety levels in elevated plus-maze. Journal of Neuroscience Methods, 236:44-50. doi: 10.1016/j.jneumeth.2014.08.006.
  11. Celis, C.C., Publio, R., and Roque, A.C. (2014). Electrical coupling in the retina ganglion cell layer increases the dynamic range. BMC Neuroscience, 15:P59. doi:10.1186/1471-2202-15-S1-P59 (extended abstract).

  12. Pinto, T.M., Schilstra, M.J., Steuber, V., and Roque, A.C. (2014). ßCaMKII regulates bidirectional long-term plasticity in cerebellar Purkinje cells by a CaMKII/PP2B switch mechanism. BMC Neuroscience, 15:P58. doi:10.1186/1471-2202-15-S1-P58 (extended abstract).

  13. Catuogno, P. J. , Lucinger, L. R. - Random Lie-point symmetries. Journal of Nonlinear Mathematical Physics, vol. 21, p. 149-165, 2014.

  14. Catuogno, P. J. Olivera, C. - Renormalized generalized solutions for the KPZ equation. To appear in “Infinite Dimensional Analysis, Quantum Probability and Related Topics”.

  15. Catuogno, P.J. D.S. Ledesma and P. Ruffino – Foliated stochastic calculus: Hamonic measures. To appear in Transaction of American Mathematical Society.

  16. Catuogno, P.J. ; Olivera, Christian. Time-dependent tempered generalized functions and Itô's formula. Applicable Analysis, vol. 93, p. 539-550, 2014.

  17. Catuogno, P.; D.S. Ledesma and P. Ruffino - A note on stochastic calculus in vector bundles. Lecture Notes on Mathematics, vol. 2078, p. 353-364. 2013.

  18. Catuogno, P. and Olivera, C. - Strong solution of the stochastic Burgers equation. Applicable Analysis, vol. 93, p.646-652. 2014.

  19. Högele, M. ; Ruffino, P. R. - Averaging along Lévy Diffusions in Foliated Spaces. To appear in “Nonlinear Analysis: “

  20. Gargate, I.I. G. and Ruffino, P. R. - An averaging principle for diffusions in foliated spaces (ArXiv 1212.1587) To appear in “Annals of Probability”.

  21. Ruffino, P. - Erratum to: A sampling theorem for rotation numbers of linear processes in R^2. To appear in Random Operators and Stochastics Equations.

  22. L.W. Rossi, P. Radtke, C. Goldman, Long-range cargo transport on crowded microtubules: The motor jamming mechanism, Physica A: Statistical Mechanics and its Applications, Volume 401, Pages 319–329 (2014);

  23. C. Goldman, O demônio de Maxwell e os motores moleculares, Rev. Bras. Ensino Fís. vol.36 no.3 São Paulo July/Sept. (2014);

  24. Noise-controlled bistability in an excitable system with positive feedback, Justus A. Kromer, Reynaldo D. Pinto, Benjamin Lindner, and Lutz Schimansky-Geier,EPL, 108 (2014) 20007; doi: 10.1209/0295-5075/108/20007;

  25. Functional regulation of neuronal nitric oxide synthase expression and activity in the rat retina. Walter LT, Higa GS, Schmeltzer C, Sousa E, Kinjo ER, Rüdiger S, Hamassaki DE, Cerchiaro G, Kihara AH. Exp Neurol. 2014 Nov;261:510-7. doi: 10.1016/j.expneurol.2014.07.019;

  26. Luiz Renato Fontes, Domingos H. U. Marchetti, Immacolata Merola, Errico Presutti •e Maria Eulalia Vares, Phase Transitions in Layered Systems, Journal Statistical Physics, Volume 157, Pages 407-421 (2014);

  27. Domingos H. U. Marchetti, The Virial Series for a Gas of Particles with Uniformly Repulsive Pairwise Interaction and its Relation with the Approach to the Mean Field, To appear in Brazilian Journal of Probability and Statistics (2014);

  28. Deniz Eroglu, Thomas K. DM. Peron, Norbert Marwan, Francisco A. Rodrigues, Luciano da F. Costa, Michael Sebek, Istvan Kiss, and Jürgen Kurths, Entropy of weighted recurrence plots, Physical Review E, 90, 042919, 2014;

  29. Bernard Sonnenschein, Thomas K. DM. Peron, Francisco A. Rodrigues, Jürgen Kurths, and Lutz Schimansky-Geier, Cooperative behavior between oscillatory and excitable units: the peculiar role of positive coupling-frequency correlations, European Physical Journal B, Springer, 87: 182, (2014);

  30. Thomas .K.D. Peron, C.H. Comin, D.R. Amancio, L.F. Costa, Francisco .A. Rodrigues, and Jürgen Kurths, Correlations Between Climate Network and Relief Data, Nonlinear Processes in Geophysics, Accepted (2014);

  31. Peng Ji, Thomas K.DM. Peron, Francisco Aparecido Rodrigues, Jürgen Kurths, Low-dimensional behavior of Kuramoto model with inertia in complex networks, Scientific Reports, v. 4, n. 4783, (2014);

  32. Zemp, D. C., Schleussner, C.-F., Barbosa, H. M. J., Van der Ent, R. J., Donges, J. F., Heinke, J., Sampaio, G., and Rammig, A.: On the importance of cascading moisture recycling in South America, Atmospheric Chemistry and Physics Discussion, 14, 17479-17526, doi:10.5194/acpd-14-17479-2014, 2014;

  33. Boers, N B. Bookhagen, H.M.J. Barbosa, N. Marwan, J. Kurths & J.A. Marengo (2014) Prediction of extreme floods in the eastern Central Andes based on a complex networks approach, Nature Communications, DOI: 10.1038/ncomms6199;

  34. Boers, N, B. Bookhagen, J Marengo, N Marwan, J-S von Storch, J Kurths (2014) Extreme rainfall of the South American monsoon system: A dataset comparison using complex networks, in press, Geophys Res Letters;

  35. Boers, N, B Bookhagen, J Marengo, N Marwan,J-S von Storch, J. Kurths (2014) Extreme rainfall of the South American monsoon system: A dataset comparison using complex networks, Journal of Climate, dx.doi.org/10.1175/JCLI-D-14-00340.1;

  36. DRUMOND, A.; MARENGO, J.; AMBRIZZI, T.; NIETO, R. ; MOREIRA, L.; GIMENO, L. The role of the Amazon Basin moisture in the atmospheric branch of the hydrological cycle: a Lagrangian analysis. Hydrology and Earth System Sciences, v. 18, p. 2577-2598, 2014.

  37. PINHO, PATRICIA FERNANDA ; MARENGO, JOSÉ A. ; SMITH, MARK STAFFORD. Complex socio-ecological dynamics driven by extreme events in the Amazon. Regional Environmental Change (Print), v. 25, p. 12-24, 2014;

  38. OBREGÓN, GO; MARENGO, J. A ; NOBRE, CA . Rainfall and climate variability: long-term trends in the Metropolitan Area of São Paulo in the 20th century. Climate Research, v. 61, p. 93-107, 2014;

  39. BOERS, N.; BOOKHAGEN, B.; BARBOSA, H. M. J.; MARWAN, N. ; KURTHS, J.; MARENGO, J. A. Prediction of extreme floods in the eastern Central Andes based on a complex networks approach. Nature Communications, v. 5, p. 5199, 2014;

  40. BREVE, F. A. ; ZHAO, L. ; QUILES, M. G. . Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise. Neurocomputing (Amsterdam – accepted);

  41. Grzybowski, J. M. V. ; MACAU, E. E. N. ; Yoneyama, T. . Isochronal synchronization in networks and chaos-based TDMA communication. The European Physical Journal. Special Topics , v. 223, p. 1447-1463, 2014.

  42. VLASOV, VLADIMIR ; MACAU, ELBERT E. N. ; PIKOVSKY, ARKADY . Synchronization of oscillators in a Kuramoto-type model with generic coupling. Chaos (Woodbury, N.Y.) v. 24, p. 023120, 2014.

  43. SALAZAR, F. J. T. ; MACAU, E. E. N. ; Winter, O. C. . Pareto Frontier for the time-energy cost vector to an Earth-Moon transfer orbit using the patched-conic approximation. Matemática Aplicada e Computacional (Cessou em 1997. Cont. ISSN 1807-0302 Computational & Applied Mathematics), v. 1, p. 1-15, 2014.

  44. FOLLMANN, Rosangela ; MACAU, ELBERT E. N. ; ROSA, Epaminondas ; PIQUEIRA, JOSE R. C. . Phase Oscillatory Network and Visual Pattern Recognition. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2014.2345572, 2014.

  45. SALAZAR, F.J.T. ; MACAU, E.E.N. ; WINTER, O.C. . Alternative transfer to the Earth-Moon Lagrangian points L4 and L5 using lunar gravity assist. Advances in Space Research v. 53, p. 543-557, 2014;

  46. Macau, E. E. N., de Melo, C. F, Prado, A. B. A., Winter, O. C. – Celestial mechanics: from errant stars to guindance of spacecrafts. Computational and Applied Mathamatics, DOI 10.1007/s40314-014-0181-4, 2014.
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


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Universität Potsdam


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Universidade Federal do ABC


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Universidade Estadual de Campinas