Publications

Papers WoS

  1. Cho, A.D., Carrasco, R.A., Ruz, G.A. Improving Prescriptive Maintenance by Incorporating Post-Prognostic Information Through Chance Constraints, IEEE Access, Vol. 10, 2022, 55924-55932.

  2. Billi, M., Mascareño, A., Henríquez, P.A., Rodríguez, I., Padilla, F., Ruz, G.A. Learning from crises? The long and winding road of the salmon industry in Chiloé Island, Chile, Marine Policy, Vol. 120, 2022, 105069.

  3. Gregor, C., Ashlock, D., Ruz, G.A., MacKinnon, D., Kribs, D. A novel linear representation for evolving matrices, Soft Computing, Vol. 26, 2022, 6645-6657.

  4. Cordero, R., Mascareño, A., Henríquez, P.A., Ruz, G.A. Drawing Constitutional Boundaries: A Digital Historical Analysis of the Writing Process of Pinochet’s 1980 Authoritarian Constitution, Historical Methods: A Journal of Quantitative and Interdisciplinary History​, 2022, DOI: 10.1080/01615440.2022.2065396

  5. Ruz, G.A., Henríquez, P.A., Mascareño, A. Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers, Mathematics, Vol. 10, 2022, 166.

  6. Truffello, R., Flores, M., Garretón, M., Ruz, G. La importancia del espacio geográfico para minimizar el error de muestras representativas, Revista de Geografía Norte Grande, Vol. 81, 2022, 137-160.

  7. Andaur, J.M.R., Ruz, G.A., Goycoolea, M. Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company, Electronics, Vol. 10, 2021, 2787.

  8. de la Cruz, R., Padilla, O., Valle, M.A., Ruz, G.A. Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks, Mathematics, Vol. 9, 2021, 639.

  9. Montalva-Medel, M., Ledger, T., Ruz, G.A., Goles, E. Lac operon Boolean models: dynamical robustness and alternative improvements, Mathematics, Vol. 9, 2021, 600.

  10. Valle, M.A., Ruz, G.A. Finding hierarchical structures of disordered systems: An application for market basket analysis, IEEE Access, Vol. 9, 2021, 1626-1641.

  11. Mascareño, A., Henríquez, P.A., Billi, M., Ruz, G.A. A Twitter-Lived Red Tide Crisis on Chiloé Island, Chile: What Can Be Obtained for Social-Ecological Research through Social Media Analysis?, Sustainability, Vol. 12, 2020, 8506.

  12. Cho, A.D., Carrasco, R.A., Ruz, G.A., Ortiz, J.L. Slow Degradation Fault Detection in a Harsh Environment, IEEE Access, Vol. 8, 2020, 175904-175920.

  13. Goles, E., Lobos, F., Ruz, G.A., Sené, S. Attractor landscapes in Boolean networks with firing memory: a theoretical study applied to genetic networks, Natural Computing, 19, 2020, 295-319.

  14. Timmermann, T., González, B., Ruz, G.A. Reconstruction of a gene regulatory network of the induced systemic resistance defense response in Arabidopsis using boolean networks, BMC Bioinformatics, 21, 142, 2020.

  15. Ruz, G.A., Henríquez, P.A., Mascareño, A. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers, Future Generation Computer Systems, Vol. 106, 2020, 92-104.

  16. Timmermann, T., Poupin, M.J., Vega, A., Urrutia, C., Ruz, G.A., González, B. Gene networks underlying the early regulation of Paraburkholderia phytofirmans PsJN induced systemic resistance in Arabidopsis, PLoS ONE, 14 (8), 2019, e0221358.

  17. Valle, M.A., Ruz, G.A., Rica, S. Market basket analysis by solving the inverse Ising problem: Discovering pairwise interaction strengths among products, Physica A: Statistical Mechanics and its Applications, Vol. 524, 2019, 36-44.

  18. Henríquez, P.A., Ruz, G.A. Noise reduction for near-infrared spectroscopy data using extreme learning machines, Engineering Applications of Artificial Intelligence, Vol. 79, 2019, 13-22.

  19. Ruz, G.A., Araya-Díaz, P. Predicting facial biotypes using continuous Bayesian network classifiers, Complexity, Vol. 2018, Article ID 4075656, 14 pages, 2018.

  20. Canals, C., Goles, E., Mascareño, A., Rica, S., Ruz, G.A. School choice in a market environment: individual vs. social expectations, Complexity, Vol. 2018, Article ID 3793095, 11 pages, 2018.

  21. Ruz, G.A., Zúñiga, A., Goles, E. A Boolean network model of bacterial quorum-sensing systems, International Journal of Data Mining and Bioinformatics, Vol. 21, 2018, 123-144.

  22. Osores, S.J.A., Ruz, G.A., Opitz, T., Lardies, M.A. Discovering divergence in the thermal physiology of intertidal crabs along latitudinal gradients using an integrated approach with machine learning, Journal of Thermal Biology, Vol. 78, 2018, 140-150.

  23. Mascareño, A., Cordero, R., Azócar, G., Billi, M., Henríquez, P., Ruz, G.A. Controversies in social-ecological systems: lessons from a major red tide crisis in Chiloe Island, Chile, Ecology and Society, Vol. 23, 2018, 15.

  24. Rengifo, F., Ruz, G.A., Mascareño, A. Managing the 1920s' Chilean educational crisis: A historical view combined with machine learning, PLoS ONE, 13(5), 2018, e0197429.

  25. Di Genova, A., Ruz, G. A., Sagot, M.-F., Maass, A. Fast-SG: An alignment-free algorithm for hybrid assembly, GigaScience, Vol. 7, 2018, giy048.

  26. Henríquez, P.A., Ruz, G.A. A non-iterative method for pruning hidden neurons in neural networks with random weights, Applied Soft Computing, Vol. 70, 2018, 1109-1121.

  27. Valle, M.A., Ruz, G.A., Morrás, R. Market basket analysis: Complementing association rules with minimum spanning trees, Expert Systems With Applications, Vol. 97, 2018, 146-162.

  28. Rodríguez-Valdecantos, G., Manzano, M., Sánchez, R., Urbina, F., Hengst, M.B., Lardies, M.A., Ruz, G.A., González, B. Early successional patterns of bacterial communities in soil microcosms reveal changes in bacterial community composition and network architecture, depending on the successional condition, Applied Soil Ecology, Vol. 120, 2017, 44-54.

  29. Zúñiga, A., Donoso, R.A., Ruiz, D., Ruz, G.A., González, B. Quorum-sensing systems in the plant growth-promoting bacterium Paraburkholderia phytofirmans PsJN exhibit cross-regulation and are involved in biofilm formation, Molecular Plant-Microbe Interactions, Vol. 30, 2017, 557-565.

  30. Valle, M.A., Ruz, G.A., Masías, V.H. Using self-organizing maps to model turnover of sales agents in a call center, Applied Soft Computing, Vol. 60, 2017, 763-774.

  31. Henríquez, P.A., Ruz, G.A. Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers, Neurocomputing, Vol. 226, 2017, 109-116.

  32. Ruz, G.A. Improving the performance of inductive learning classifiers through the presentation order of the training patterns, Expert Systems with Applications, Vol. 58, 2016, 1-9.

  33. Mascareño, A., Goles, E., Ruz, G.A. Crisis in Complex Social Systems: A Social Theory View Illustrated with the Chilean Case, Complexity, Vol. 21, No. S2, 2016, 13-23.

  34. Valle, M.A., Ruz, G.A. Turnover prediction in a call center: behavioral evidence of loss aversion using random forest and naive Bayes algorithms, Applied Artificial Intelligence, Vol. 29, 2015, 923-942.

  35. Valle, M.A., Ruz, G.A., Varas, S. Explaining job satisfaction and intentions to quit from a value-risk perspective, Academia Revista Latinoamericana de Administración, Vol. 28, 2015, 523-540.

  36. Valle, M.A., Ruz, G.A., Varas, S. A Survival Model Based on Met Expectations: Application to Employee Turnover in a Call Center, Academia Revista Latinoamericana de Administración, Vol. 28, 2015, 177-194.

  37. Goles, E., Ruz, G.A. Dynamics of neural networks over undirected graphs, Neural Networks, Vol. 63, 2015, 156-169.

  38. Ruz, G.A., Timmermann, T., Barrera, J., Goles, E. Neutral space analysis for a Boolean network model of the fission yeast cell cycle network, Biological Research, Vol. 47, 2014, 64.

  39. Ruz, G.A., Goles, E., Montalva, M., Fogel, G.B. Dynamical and Topological Robustness of the Mammalian Cell Cycle Network: A Reverse Engineering Approach, Biosystems, Vol. 115, 2014, 23-32.

  40. Araya-Díaz, P., Ruz, G.A., Palomino, H.M. Discovering Craniofacial Patterns Using Multivariate Cephalometric Data for Treatment Decision Making in Orthodontics, International Journal of Morphology, Vol. 31, 2013, 1109-1115.

  41. Ruz, G.A., Varas, S., Villena, M. Policy making for broadband adoption and usage in Chile through machine learning, Expert Systems with Applications, Vol. 40, 2013, 6728-6734.

  42. Goles, E., Montalva, M., Ruz, G.A. Deconstruction and dynamical robustness of regulatory networks: application to the yeast cell cycle networks, Bulletin of Mathematical Biology, Vol. 75, 2013, 939-966.

  43. Ruz, G. A. and Goles, E. Learning gene regulatory networks using the bees algorithm, Neural Computing & Applications, Vol. 22, 2013, 63-70.

  44. Valle, M.A., Varas, S., Ruz, G.A. Job performance prediction in a call center using a naive Bayes classifier, Expert Systems with Applications, Vol. 39, 2012, 9939-9945.

  45. Ruz, G. A. and Pham, D. T. NBSOM: The naive Bayes self-organizing map, Neural Computing & Applications, Vol. 21, 2012, 1319-1330.

  46. Pham, D. T. and Ruz, G. A. Unsupervised training of Bayesian networks for data clustering, Proceedings of the Royal Society A, Vol. 465, 2009, 2927-2948.

  47. Ruz, G.A. and Pham, D.T. Building Bayesian network classifiers through a Bayesian complexity monitoring system, Proc. IMechE, Part C: J. Mechanical Engineering Science, Vol. 223(C3), 2009, 743-755.

  48. Ruz, G.A., Estévez, P.A. and Ramirez, P.A. Automated visual inspection system for wood defect classification using computational intelligence techniques, International Journal of Systems Science, Vol. 40, No. 2, February 2009,163-172.

  49. Ruz, G.A., Estévez, P.A., Perez, C.A. A neurofuzzy color image segmentation method for wood surface defect detection, Forest Products Journal, Vol. 55, N 4, April 2005, 52-58.

Papers ESCI

  1. Rubilar P., Hirmas, M., Matute, I., Browne, J., Little, C., Ruz, G., Aguilera, X., Ávila, C., Vial, P., Gutknecht Mackenzie, T. Seroprevalence and estimation of the impact of SARS-CoV-2 infection in older adults residing in Long-term Care Facilities in Chile, Medwave, Vol. 22, 2022, e8715.

Book Chapters

  1. Ruz, G.A. Analyzing Boolean Networks Through Unsupervised Learning. In: Adamatzky, A. (eds) Automata and Complexity. Emergence, Complexity and Computation, vol 42. Springer, Cham. 2022, pp. 219-231.

Papers in Conference Proceedings

  1. Rica, S., Ruz, G.A. Estimating SIR model parameters from data using differential evolution: an application with COVID-19 data. The 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2020), Virtual Conference, 27-29 October, 2020, pp. 1-6.

  2. Ruz, G.A., Henríquez, P.A. Random vector functional link with naive Bayes for classification problems of mixed data. The 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019), Portland, Oregon, USA, November 4-6, 2019, pp. 1749-1752.

  3. Valle, M.A., Ruz, G.A. Market basket analysis using Boltzmann machines. The 28th International Conference on Artificial Neural Networks (ICANN 2019), Munich, Germany, September 17-19, 2019, pp. 611-623.

  4. Valle, M.A., Ruz, G.A., Rica, S. Transactional database analysis by discovering pairwise interactions strengths. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, August 28-31, 2018, pp. 849-854.

  5. Henríquez, P.A., Ruz, G.A. Twitter sentiment classification based on deep random vector functional link. The 2018 IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2018), Rio de Janeiro, Brazil, July 8-13, 2018, pp. 1-6.

  6. Ruz, G.A., Goles, E., Sené, S. Reconstruction of Boolean regulatory models of flower development exploiting an evolution strategy, The 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), Rio de Janeiro, Brazil, July 8-13, 2018, pp. 1-8.

  7. Henríquez, P.A., Ruz, G.A. An empirical study of the hidden matrix rank for neural networks with random weights. The IEEE 16th International Conference on Machine Learning and Applications (ICMLA 2017), Cancun, Mexico, 18-21 December, 2017, pp. 883-888.

  8. Valle, M.A., Ruz, G.A., Morrás, R. Market Basket Analysis Using Minimum Spanning Trees. 4th European Network Intelligence Conference ENIC 2017: Network Intelligence Meets User Centered Social Media Networks, Duisburg, Germany, 11-12 September, 2017, pp. 155-167.

  9. Ruz, G.A, Ashlock, D., Ledger, T., Goles, E. Inferring bistable lac operon Boolean regulatory networks using evolutionary computation. The 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2017), Manchester, U.K., 23-25 August, 2017, pp. 1-8.

  10. Ashlock, D., Ruz, G.A. A novel representation for Boolean networks designed to enhance heritability and scalability. The 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2017), Manchester, U.K., 23-25 August, 2017, pp. 1-8.

  11. Ruz, G.A., Timmermann, T., Goles, E. Neutral space analysis of gene regulatory network models of salt stress response in Arabidopsis using evolutionary computation, The 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24-29, 2016, pp. 4281-4288.

  12. Ruz, G.A., Timmermann, T., Goles, E. Reconstruction of a GRN model of salt stress response in Arabidopsis using genetic algorithms. The 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2015), Niagara Falls, Canada, August 12-15, 2015, pp. 1-8.

  13. Ruz, G.A., Goles, E. Neutral graph of regulatory Boolean networks using evolutionary computation. The 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2014), Honolulu, Hawaii, USA, May 21-24, 2014, pp.1-8.

  14. Ruz, G.A., Montalva, M., Goles, E. On the preservation of limit cycles in Boolean networks under different updating schemes, Advances in Artificial Life, ECAL 2013, 12th European Conference on Artificial Life, Taormina, Italy, September 2-6, pp.1085-1090, 2013.

  15. Ruz, G.A., Timmermann, T., Goles, E. Building synthetic networks of the budding yeast cell-cycle using swarm intelligence. IEEE the Eleventh International Conference on Machine Learning and Applications (ICMLA 2012), Boca Raton, Florida, USA, December 12-15, pp. 120-125, 2012.

  16. Ruz, G.A., Goles, E. Reconstruction and update robustness of the mammalian cell cycle network. The 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2012), San Diego, California, USA, May 9-12, pp. 397-403, 2012.

  17. Valle, M. A., Ruz, G.A. Predicción de la rotación de empleados en un call center mediante análisis discriminante. ENEFA 2012, XXVIII Encuentro Nacional de Facultades de Adminis- tración y Economía, Talca, Chile, November 21-23, Vol. 5 pp. 1622-1642, 2012.

  18. Valle, M.A., Varas S., Ruz, G.A. Explaining turnover: A survival model based on expectations. ENEFA 2011, XXVII Encuentro Nacional de Facultades de Administración y Economía, Pucón, Chile, November 23-25, Vol. 4 pp. 1486-1511, 2011.

  19. Ruz, G.A., Goles, E. Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. IEEE the Ninth International Conference on Machine Learning and Applications (ICMLA 2010), Washington DC, USA, December 12-14, pp. 889-894, 2010.

  20. Araya-Díaz, P., Ruz, G.A., Palomino, H.M. Discovering facial biotype pattern using multivariate cephalometric data for treatment decision making. Intelligent Production Machines and Systems-6th I*PROMS Virtual Conference, November 2010.

  21. Ruz, G.A., Goles, E. Cycle attractors for different deterministic updating schemes in Boolean regulation networks. The IASTED International Conference on Computational Bioscience (Comp-Bio 2010), Cambridge, Massachusetts, USA, 1-3 Nov, pp. 620-625, 2010.

  22. Ortega, P.A., Figueroa, C.J.,and Ruz, G.A. A medical claim fraud/abuse detection system based on data mining: A case study in Chile. DMIN’06,The 2006 International Conference on Data Mining,Las Vegas, Nevada, USA, June 26-29, pp. 224-231, 2006.

  23. Ruz,G.A.,Estévez, P.A. Image segmentation using fuzzy min-max neural networks for wood defect detection. Intelligent Production Machines and Systems-First I*PROMS Virtual Con- ference, D.T Pham, E.E. Eldukhri and A.J. Soroka (eds.), pp. 183-188, 4-15 July 2005. (BEST YOUNG AUTHOR AWARD!)

  24. Estévez, P.A., Ruz, G.A., and Perez, C.A. Fuzzy Min-Max Neural Network for Image Segmentation. CVPRIP’2003, International Conference on Computer Vision, Pattern Recognition and Image Processing, Cary, North Carolina, Sept. 2003, pp.655- 659.

Abstracts

  1. Cho, A.D., Carrasco, R.A., Ruz, G.A. Prescriptive maintenance scheduling, in Applied Combinatorial Optimization - ALIO/EURO International Conference 2021-2022, Online, April 2022.

  2. Cho, A.D., Carrasco, R.A., Ruz, G.A. Prescriptive maintenance framework, in XIV Chilean conference on Operations Research - OPTIMA 2021, Online, March 2022.

  3. Cho, A.D., Carrasco, R.A., Ruz, G.A. Prescriptive maintenance: from fault prediction to maintenance planning, in I Jornadas latinoamericanas de matemáticas puras y aplicadas de la FaCyT-UC, Online, January 2022, pp. 8-9.

  4. Cho Lo A.D., Carrasco, R.A., Ruz, G.A., Ortiz, J.L. Fault Detection with Echo State Networks, in XIII Chilean conference on Operations Research - OPTIMA 2019, Online, November 2019, pp. 4.

  5. Truffello, R., Flores, M., Garreton, M., Ruz, G. Spatial sampling via a local partition based on exhaustive demographic data at Metropolitan Urban Area of Santiago of Chile, AAG 2019 Annual Meeting, Washington, DC, USA, April 7-11, 2019.

  6. Ruz, G.A., Henríquez, P.A., Mascareño, A., Goles, E. Learning Bayesian network classifiers with applications to Twitter sentiment analysis, International Conference on Complex Systems (ICCS 2018), Cambridge, MA, USA, July 22-27, 2018, pp. 175.

  7. Goles, E., Ruz, G.A. A Boolean model of gene regulatory networks with memory: application to the elementary cellular automata, Conference on Complex Systems (CCS’16), Amsterdam, Netherlands, September 19-22, 2016.

  8. Mascareño, A., Goles, E., Ruz, G.A. Crisis in Complex Social Systems: A Social Theory View Illustrated with the Chilean Case, Conference on Complex Systems (CCS’16), Amsterdam, Netherlands, September 19-22, 2016.

  9. Osores, S., Optiz, T., Ruz, G.A., Lardies, M. Physiological responses of intertidal crabs to environmental gradients on the coast of Chile using an integrated approach with machine learning, Conference on Complex Systems (CCS’16), Amsterdam, Netherlands, September 19-22, 2016.

  10. Valle, M.A., Ruz, G.A., Masias, V. Using Self-Organizing Maps to model turnover of Sales Agents in a Call Center as probabilities of state changes, Conference on Business Analytics in Finance and Industry (BAFI 2015), Santiago, Chile, December 14-16, 2015, pp. 42-43.

  11. Timmermann, T., Ruz, G.A., Goles, E. , Reconstruction of a GRN Model of Salt Stress Response in Arabidopsis using Boolean Networks, Conference on Complex Systems (CCS’15), Tempe, Arizona, USA, September 28-October 2, 2015.

  12. Goles, E., Ruz, G.A. Threshold Networks: Universality and Applications, Conference on Complex Systems (CCS’15), Tempe, Arizona, USA, September 28-October 2, 2015.

  13. Zúñiga, A., Ruz, G., González, B. A synthetic bacterial consortium design to optimize and con- trol plant-bacteria interaction, II Latin American Workshop on PGPR, La Falda, Argentina, September 21-26, 2014, pp. 69.

  14. Goles, E., Montalva, M., Ruz, G.A. From the qualitative analysis in a lac operon model that predicts bistability, European Conference on Complex Systems (ECCS’14), Lucca, Italy, September 22-26, 2014.

  15. Timmermann, T., Ruz, G.A., Armijo, G., Holuigue, L., Goles, E., Gonzalez, B. Reconstruction of a gene regulatory network controlling the induced systemic resistance triggered by a plant growth promoting bacterium in Arabidopsis thaliana infected with a phytopathogen, 15th International Conference on Systems Biology (ICSB 2014), Melbourne, Australia, September 14-18, 2014, pp. 198.

  16. Montalva, M., Ruz, G.A., Goles, E. Attraction basins in a lac operon model under different update schedules, ALIFE14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems, New York, USA, July 30-August 2, 2014, pp. 689-690.

  17. Zuniga, A., Ruz, G., Gonzalez, B. A synthetic bacterial consortium design by compartmentalized logic gates, Synthetic Systems Biology Summer School (SSBSS 2014), Taormina, Italy, June 15-19, 2014, pp. 15.

  18. Valle, M.A., Ruz, G.A. Predicting Turnover of Sales Agents in a Call Center with Random Forest and Naive Bayes: Evidence of Loss Aversion in Turnover Intentions, Conference on Business Analytics in Finance and Industry (BAFI 2014), Santiago, Chile, January 6-9, 2014, pp. 14-15.

  19. Osores, S.J.A., Optiz, T., Prado, L., Ruz, G.A., Lardies, M.A. Máquina de aprendizaje para una clasificación biogeográfica latitudinal del cangrejo Cyclograpsus cinereus de acuerdo a sus atributos fisiológicos, V Reunión Binacional de Ecología, XX Reunión de la Sociedad de Ecología de Chile, Puerto Varas, Chile, November 3-6, 2013, pp. 128.

  20. Montalva, M., Ruz, G.A., Goles, E. Mathematical tools for the dynamical analysis of Boolean regulatory networks, LXXXII Encuentro Anual Sociedad de Matemáticas de Chile, Olmue, Chile, November 7-9, 2013, pp. 85.

  21. Montalva, M., Goles, E., Ruz, G.A. Dynamical robustness of the Mammalian cell cycle network: a mathematical approach, European Conference on Complex Systems (ECCS’13), Barcelona, Spain, September 16-20, 2013, pp. 71.

  22. Valle, M. A., Ruz, G.A. Predicción de la rotación de empleados en un call center mediante entrenamiento y aprendizaje de árbol de clasificación, VISIII2013, VI Simposio Internacional de Ingeniería Industrial: Actualidad y Nuevas Tendencias 2013, Colombia-Bogotá, July 24-26, 2013, pp. 66.

  23. Valle, M. A., Ruz, G. A., Varas, S. El rol de la aversión al riesgo en los ingresos y las expectativas de agentes de venta sobre la satisfacción en el trabajo y las intenciones de renuncia. CLADEA 2012, XLVII Annual Assembly, Lima - Peru, October 22-24, 2012.

  24. Goles, E. and Ruz, G.A. Robustness of limit cycle attractors in Boolean networks under update schedule perturbations. European Conference on Complex Systems (ECCS’11), Vienna, Austria, Sept. 12-16, 2011, pp 80-81.

  25. Araya, P., Ruz, G.A., Palomino, H. Growth patterns identification for prediction based on multivariate statistics. IADR Annual Meeting Chilean Division, Talca, Chile, October 16-17, 2008.

Other Publications

  1. Mascareño, A., Cordero, R., Henríquez, P.A., Ruz, G.A. Comunidades semánticas y distinciones políticas en el discurso de los convencionales constituyentes, Puntos de Referencia, Vol. 581, 1-27, 2021.

  2. Mascareño, A., Cordero, R., Henríquez, P.A., Ruz, G.A., Rodríguez, I., Ojeda, I., Foulon, C.L., Billi, M. Semánticas constitucionales: un análisis de los programas de los convencionales constituyentes, Puntos de Referencia, Vol. 578, 1-17, 2021.