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Acknowledgment:

The experiments presented in this paper were carried out using ClusterUY (site: https://cluster.uy)

Publicaciones

En esta sección se encuentran los registros de las publicaciones que se generaron con contribución de cluster.uy.

2024

  1. Filipponi, F., Lorenzo, J. P., Locatelli, F., & Nesmachnow, S. Redes Neuronales Generativas Adversarias para Superresolución de Modelos Digitales de Elevación. Actas de las Jornadas Uruguayas de Ciencias de la Computación 2024, 22.

  2. Cuevas, F., Barenchi, L., Alonso, I., Nesmachnow, S., & Moreno-Bernal, P. (2024, November). A Computational Intelligence Approach for Car Damage Assessment. In Ibero-American Congress of Smart Cities (pp. 118-133). Cham: Springer Nature Switzerland.

  3. Nesmachnow, S., & Risso, C. (2024). A computational intelligence approach for solar photovoltaic power generation forecasting. Renewable Energies, 2(1), 27533735241237990.

  4. Mignaco, J., Rey, G., Correa, J., Nesmachnow, S., & Toutouh, J. (2024, July). Empirical comparison of evolutionary approaches for searching the latent space of Generative Adversarial Networks for the human face generation problem. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1631-1639).

  5. Marichal, H., Passarella, D., & Randall, G. (2025). Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation. In International Conference on Pattern Recognition (pp. 1-15). Springer, Cham.

  6. Cardona, A. L., Teruel, M., & Ventura, O. N. (2024). Unexpected high yield of acrolein underlies the importance of the hydrogen-abstraction mechanism in photooxidation of allyl methyl sulfide (AMS). Chemosphere, 354, 141693.

  7. Sastre, S., Manta, B., Semelak, J. A., Estrin, D., Trujillo, M., Radi, R., & Zeida, A. (2024). Catalytic Mechanism of Mycobacterium tuberculosis Methionine Sulfoxide Reductase A. Biochemistry, 63(4), 533-544.

  8. Straccia C, V. G., L. Cardona, A., Blanco, M. B., Ventura, O. N., & Teruel, M. (2024). Theoretical and In Situ FTIR Studies of the Atmospheric Sink of Methyl Dichloroacetate by• OH Radicals and Cl• Atoms: Kinetics, Product Distribution, and Mechanism. ACS Earth and Space Chemistry, 8(12), 2599-2610.

  9. Filevich, J. P., Marco, G., Castro, S., Chiruzzo, L., & Rosá, A. (2024, May). A Language Model Trained on Uruguayan Spanish News Text. In Proceedings of the Second International Workshop Towards Digital Language Equality (TDLE): Focusing on Sustainability@ LREC-COLING 2024 (pp. 53-60).

  10. Nogueira, M., Etcheverry, L., & Randall, G. (2024, August). Building Tools to Analyze the Files of the Uruguayan Dictatorship: Information Extraction From the Personal Records of Organización Coordinadora de Operaciones Antisubversivas (OCOA). In 2024 L Latin American Computer Conference (CLEI) (pp. 1-10). IEEE.

  11. Villar, S. F., Corrales‐González, L., Márquez de los Santos, B., Dalla Rizza, J., Zeida, A., Denicola, A., & Ferrer‐Sueta, G. (2024). Kinetic and structural assessment of the reduction of human 2‐Cys peroxiredoxins by thioredoxins. The FEBS Journal, 291(4), 778-794.

  12. Andrade, J., de Almeida, E., Gutiérrez, R., & Weschenfelder, F. CONEM2024-0347 CARACTERIZACIÓN DEL RECURSO EÓLICO OFFSHORE EN URUGUAY UTILIZANDO DATOS DE REANÁLISIS ERA5.

  13. Nesmachnow, S., & Hipogrosso, S. (2024). Assessment of Sustainable Mobility Initiatives Developed in Montevideo, Uruguay. Urban Science, 8(2), 52.

  14. Lucas, A., Baladón, A., Pardiñas, V., Agüero-Torales, M., Góngora, S., & Chiruzzo, L. (2024, June). Grammar-based data augmentation for low-resource languages: The case of Guarani-Spanish neural machine translation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 6385-6397).

  15. Gonzalez, J., de Almeida, E., & Gutiérrez, A. (2024). Wind farm power curve characterization under different atmospheric stability regimes. Wind Engineering, 48(6), 1174-1185.

  16. Torres-Aguilar, C., Moreno-Bernal, P., Nesmachnow, S., & Rossit, D. (2024, September). A Parallel Multi-threading Global Energy Balance for a Room Thermal Analysis in an Unsteady State. In Latin American High Performance Computing Conference (pp. 299-314). Cham: Springer Nature Switzerland.

  17. Garay, P. G., Machado, M. R., Verli, H., & Pantano, S. (2024). SIRAH late harvest: coarse-grained models for protein glycosylation. Journal of Chemical Theory and Computation, 20(2), 963-976.

  18. Rossit, D., & Nesmachnow, S. (2024). Enhancing mass customization manufacturing: Multiobjective metaheuristic algorithms for flow shop production in smart industry. SN Computer Science, 5(6), 782.

  19. Bonanata, J. (2024). The role of the active site lysine residue on FAD reduction by NADPH in glutathione reductase. Computational Biology and Chemistry, 110, 108075.

  20. Castelli Ottati, R., Viscardi, I., & Nesmachnow, S. (2024, September). High Performance Computing for Auto Supervised Machine Learning Training: Parallel-Distributed Implementation of the Word2Vec Algorithm for Training Word Embeddings. In Latin American High Performance Computing Conference (pp. 36-51). Cham: Springer Nature Switzerland.

  21. Torres-Aguilar, C., Moreno-Bernal, P., Nesmachnow, S., Rossit, D., Aguilar-Castro, K. M., & Macias-Melo, E. V. (2024, November). Regression Analysis for Prediction of Solar Chimney Performance. In Ibero-American Congress of Smart Cities (pp. 229-243). Cham: Springer Nature Switzerland.

  22. Goñi, G., Nesmachnow, S., & Chernykh, A. (2024, November). Design of Content Distribution Networks for smart cities. In Ibero-American Congress of Smart Cities (pp. 272-286). Cham: Springer Nature Switzerland.

2023

  1. Conde, R., Robledo, F., & de Lacalle, A. L. (2023). Silvopastoral and agroforestry systems: An integer linear programming model for investment decisions. Journal of Dynamics and Games, 10(4), 304-329.

  2. Delgado, S., Fossati, M. & Santoro, P. (2023). Métodos Numéricos Aplicados a la Gestión de Grandes Cuerpos de Agua a Superficie Libre. Mecánica Computacional, XL, 1601-1610.

  3. Pantano, S., & Barrera, E. E. Early Stages in Aβ1-42 Spontaneous Aggregation: An Unbiased Dataset from Coarse-Grained Molecular Dynamics Simulations. Available at SSRN 4359518.

  4. Porteiro, R., Nesmachnow, S., Moreno-Bernal, P., & Torres-Aguilar, C. E. (2023). Computational intelligence for residential electricity consumption assessment: Detecting air conditioner use in households. Sustainable Energy Technologies and Assessments, 58, 103319.

  5. Rossit, D., & Nesmachnow, S. (2023, November). Smart industry strategies for shop-floor production planning problems to support mass customization. In Ibero-American Congress of Smart Cities (pp. 123-137). Cham: Springer Nature Switzerland.

  6. Serrano, N., Betarte, G., & Campo, J. D. (2023, October). Third-party trackers in covid-19 mobile applications can enable privacy leaks. In Proceedings of the 12th Latin-American Symposium on Dependable and Secure Computing (pp. 80-89).

  7. Arce-García, I. Y., Moreno-Bernal, P., Pacheco-Valencia, V., Torres-Salazar, M. D. C., Nesmachnow, S., & León-Hernández, V. A. (2023, November). Simulated Annealing Metaheuristic Approach for Municipal Solid Waste Collecting Route Problem in the Historical Center of a Mexican City. In Ibero-American Congress of Smart Cities (pp. 108-122). Cham: Springer Nature Switzerland.

  8. Baladón, A., Sastre, I., Chiruzzo, L., & Rosá, A. (2023, July). RETUYT-InCo at BEA 2023 shared task: Tuning open-source LLMs for generating teacher responses. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) (pp. 756-765).

  9. Risso, C., Nesmachnow, S., & Faller, G. (2023). Optimized design of a backbone network for public transportation in Montevideo, Uruguay. Sustainability, 15(23), 16402.

  10. Muraña, J., & Nesmachnow, S. (2023, September). Multi-objective Analysis of Power Consumption and Quality of Service in Datacenters for Effective Demand Response. In Latin American High Performance Computing Conference (pp. 50-65). Cham: Springer Nature Switzerland.

  11. Cabrera, S., Leonel, E. D., & Marti, A. C. (2023). Regular and chaotic phase space fraction in the double pendulum. arXiv preprint arXiv:2312.13436.

  12. Nesmachnow, S., & Tchernykh, A. (2023). The impact of the COVID-19 pandemic on the public transportation system of montevideo, Uruguay: a urban data analysis approach. Urban Science, 7(4), 113.

  13. Kieninger, M., & Ventura, O. N. (2023). SVECV‐f12: A composite scheme for accurate and cost‐effective evaluation of reaction barriers. II. Benchmarking using Karton’s BH28 barrier heights database. International Journal of Quantum Chemistry, 123(24), e27069.

  14. Marchesoni-Acland, F., Herrera, A., Mozo, F., Camiruaga, I., Castro, A., & Alonso-Suárez, R. (2023). Deep learning methods for intra-day cloudiness prediction using geostationary satellite images in a solar forecasting framework. Solar Energy, 262, 111820.

  15. Baptista, A., Gibilisco, R. G., Vega-Teijido, M., Ventura, O. N., & Teruel, M. A. (2023). Atmospheric oxidation of furanones by• OH and• Cl radicals: In situ FTIR rate coefficient determinations, SAR and theoretical studies. Chemosphere, 338, 139500.

  16. Cardona, A. L., Blanco, M. B., Teruel, M. A., & Ventura, O. N. (2023). Mechanistic study of the complex photooxidation of allyl methyl sulfide (AMS): reaction paths and products of addition under different atmospheric conditions. Environmental Science: Atmospheres, 3(7), 1075-1089.

  17. Carbajal, G., Vitoria, P., Lezama, J., & Musé, P. (2023). Blind motion deblurring with pixel-wise kernel estimation via kernel prediction networks. IEEE Transactions on Computational Imaging, 9, 928-943.

  18. Pedron, F. N., Messias, A., Zeida, A., Roitberg, A. E., & Estrin, D. A. (2023). Novel lennard-jones parameters for cysteine and selenocysteine in the AMBER force field. Journal of Chemical Information and Modeling, 63(2), 595-604.

  19. Ladino Cardona, M. A., Blanco, M. B., Teruel, M. A., & Ventura, O. N. (2023). Mechanistic study of the complex photooxidation of allyl methyl sulfide (AMS): reaction paths and products of addition under different atmospheric conditions.

  20. Tancredi, G., Liu, P. Y., Campo-Bagatin, A., Moreno, F., & Domínguez, B. (2023). Lofting of low-speed ejecta produced in the DART experiment and production of a dust cloud. Monthly Notices of the Royal Astronomical Society, 522(2), 2403-2414.

  21. Risso, C., Nesmachnow, S., & Rossit, D. (2023). Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables. Applied Sciences, 13(24), 13032.

  22. Llagueiro, P., Porteiro, R., & Nesmachnow, S. (2023, November). Characterization of Household Electricity Consumption in Uruguay. In Ibero-American Congress of Smart Cities (pp. 33-47). Cham: Springer Nature Switzerland.

  23. Nesmachnow, S., Toutouh, J., Ripa, G., Mautone, A., & Vidal, A. (2023, September). Parallel-Distributed Implementation of the Lipizzaner Framework for Multiobjective Coevolutionary Training of Generative Adversarial Networks. In Latin American High Performance Computing Conference (pp. 97-112). Cham: Springer Nature Switzerland.

2022

  1. Chavat, J., Nesmachnow, S., Graneri, J., & Alvez, G. (2022). ECD-UY, detailed household electricity consumption dataset of Uruguay. Scientific Data, 9(1), 1-16.

  2. Gonella, R., Bourel, M., & Bel, L. (2022). Facing spatial massive data in science and society: Variable selection for spatial models. Spatial Statistics, 100627.

  3. Rivela, C. B., Cardona, A. L., Blanco, M. B., Barnes, I., Kieninger, M., Ventura, O. N., & Teruel, M. A. (2022). Degradation mechanism of 2-fluoropropene by Cl atoms: experimental and theoretical products distribution studies. Physical Chemistry Chemical Physics.

  4. Yunlong Shi, Ari Zeida, Caitlin E. Edwards, Michael L. Mallory, Santiago Sastre, Matías R. Machado, Raymond J. Pickles, Ling Fu, Keke Liu, Jing Yang, Ralph S. Baric, Richard C. Boucher, Rafael Radi, and Kate S. Carrol (2022). Thiol-based chemical probes exhibit antiviral activity against SARS-CoV-2 via allosteric disulfide disruptionin the spike glycoprotein. Proceedings of the National Academy of Sciences, 119(6)

  5. Chavat Pérez, F. (2022). Modelos Seq2Seq para la transcripción de documentos del Archivo Berrutti. Tesis de grado

  6. Gutiérrez, A., & Fovell, R. G. (2018). A new gust parameterization for weather prediction models. Journal of Wind Engineering and Industrial Aerodynamics, 177, 45-59.

  7. Gutiérrez, A., Porrini, C., & Fovell, R. G. (2020). Combination of wind gust models in convective events. Journal of Wind Engineering and Industrial Aerodynamics, 199, 104118.

  8. Rivara-Espasandín, M., Balestrazzi, L., Dufort y Álvarez, G., Ochoa, I., Seroussi, G., Smircich, P., … & Martín, Á. (2022). Nanopore quality score resolution can be reduced with little effect on downstream analysis. Bioinformatics Advances, 2(1), vbac054.

  9. Pazos Obregón, F., Silvera, D., Soto, P., Yankilevich, P., Guerberoff, G., & Cantera, R. (2022). Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning. Scientific Reports, 12(1), 1-11.

  10. Silvera, D. (2022). Implementación de clasificadores jerárquicos multiclase para la predicción de función de genes a partir de su ubicación en el genoma. Tesis de Maestría

2021

  1. Barrera, E. E., Zonta, F., & Pantano, S. (2021). Dissecting the role of glutamine in seeding peptide aggregation. Computational and structural biotechnology journal, 19, 1595-1602.

  2. Barrera, E. E., Pantano, S., & Zonta, F. (2021). A homogeneous dataset of polyglutamine and glutamine rich aggregating peptides simulations. Data in Brief, 107109.

  3. Di Chiara, L. (2021). Planificación de largo plazo y caracterización de sistemas eléctricos en américa latina en base a sus recursos. [Tesis de maestría en ingeniería de la energía, Facultad de Ingeniería, UdelaR]

  4. Garay, P. G., Barrera, E. E., Klein, F., Machado, M. R., Soñora, M., & Pantano, S. (2021). The SIRAH-CoV-2 Initiative: a coarse-grained simulations’ dataset of the sars-cov-2 proteome. database, 9, 28.

  5. Klein, F., Sardi, F., Machado, M. R., Ortega, C., Comini, M. A., & Pantano, S. (2021). CUTie2: the attack of the cyclic nucleotide sensor clones. Frontiers in molecular biosciences, 8.

  6. Machado, M. R., & Pantano, S. (2021). Fighting viruses with computers, right now. Current Opinion in Virology, 48, 91-99.

  7. López-Vázquez, C., Tasistro, A., and Hochsztain, E. (2021). Exact tables for the Friedman rank test: Case with ties. Chilean Journal of Statistics, vol 12, 1.

  8. Soñora, M., Martinez, L., Pantano, S., & Machado, M. R. (2021). Wrapping Up Viruses at Multiscale Resolution: Optimizing PACKMOL and SIRAH Execution for Simulating the Zika Virus. Journal of Chemical Information and Modeling, 61(1), 408-422.

  9. González Madina, F., Gutiérrez, A., & Galione, P. (2021). Computational fluid dynamics study of Savonius rotors using OpenFOAM. Wind Engineering, 45(3), 630-647.

2020

  1. Alonso, R., & Solari, S. (2020). Improvement of the high-resolution wave hindcast of the Uruguayan waters focusing on the Río de la Plata Estuary. Coastal Engineering, 161.

  2. Chavat J., Graneri J., Nesmachnow S. (2020) Household Energy Disaggregation Based on Pattern Consumption Similarities. In: Nesmachnow S., Hernández Callejo L. (eds) Smart Cities. ICSC-CITIES 2019. Communications in Computer and Information Science, vol 1152. Springer, Cham

  3. Frigini, E. N., Barrera, E. E., Pantano, S., & Porasso, R. D. (2020). Role of membrane curvature on the activation/deactivation of Carnitine Palmitoyltransferase 1A: A coarse grain molecular dynamic study. Biochimica et Biophysica Acta (BBA)-Biomembranes, 1862(2), 183094.

  4. Gutiérrez, A., Porrini, C., & Fovell, R. G. (2020). Combination of wind gust models in convective events. Journal of Wind Engineering and Industrial Aerodynamics, 199, 104118.

  5. Gutiérrez, A., Porrini, C., & Fovell, R. G. (2020). Combination of wind gust models in convective events. Journal of Wind Engineering and Industrial Aerodynamics, 199, 104118.

  6. Irigaray D., Dufrechou E., Pedemonte M., Ezzatti P., López-Vázquez C. (2020) Accelerating the Calculation of Friedman Test Tables on Many-Core Processors. In: Crespo-Mariño J., Meneses-Rojas E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham

  7. Landry, A. P., Moon, S., Bonanata, J., Cho, U. S., Coitiño, L., & Banerjee, R. (2020). Dismantling and rebuilding the trisulfide cofactor demonstrates its essential role in human sulfide quinone oxidoreductase. bioRxiv.

  8. Pienika, R., Usera, G., & Ramos, H. M. (2020). Simulation of a Hydrostatic Pressure Machine with Caffa3d Solver: Numerical Model Characterization and Evaluation. Water, 12(9), 2419.

  9. Porteiro R., Nesmachnow S., Hernández-Callejo L. (2020) Short Term Load Forecasting of Industrial Electricity Using Machine Learning. In: Nesmachnow S., Hernández Callejo L. (eds) Smart Cities. ICSC-CITIES 2019. Communications in Computer and Information Science, vol 1152. Springer, Cham

  10. Salta, Z., Lupi, J., Tasinato, N., Barone, V., & Ventura, O. N. (2020). Unraveling the role of additional OH-radicals in the H–Abstraction from Dimethyl sulfide using quantum chemical computations. Chemical Physics Letters, 739, 136963.

  11. Salta, Z., Lupi, J., Barone, V., & Ventura, O. (2020). H–Abstraction from Dimethyl Sulfide in the Presence of an Excess of Hydroxyl Radicals. A Quantum Chemical Evaluation of Thermochemical and Kinetic Parameters Unveil an Alternative Pathway to Dimethyl Sulfoxide.

  12. [Ventura, O. (2021). SVECV-F12: A Composite Scheme for an Accurate and Cost Effective Evaluation of Reaction Barriers. I. Benchmarking Using the HTBH38/08 and NHTBH38/08 Barrier Heights Databases.] (https://chemrxiv.org/engage/chemrxiv/article-details/60c7547cbb8c1a1ce63dc224)

  13. de Almeida Lucas, E., Arce, A. G., & Camargo, S. (2020). Pronóstico de energía eólica en Uruguay para horizontes temporales de corto plazo en base a modelo numérico de mesoescala y redes neuronales artificiales. ENERLAC. Revista de energía de Latinoamérica y el Caribe, 4(1), 32-43.

2019

  1. Carlos López-Vázquez & Esther Hochsztain (2019) Extended and updated tables for the Friedman rank test, Communications in Statistics - Theory and Methods, 48:2, 268-281, DOI: 10.1080/03610926.2017.1408829

  2. Garabedian S., Porteiro R., Nesmachnow S. (2019) Generation and Classification of Energy Load Curves Using a Distributed MapReduce Approach. In: Torres M., Klapp J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151. Springer, Cham

  3. Guggeri, A., & Draper, M. (2019). Large Eddy Simulation of an Onshore Wind Farm with the Actuator Line Model Including Wind Turbine’s Control below and above Rated Wind Speed. Energies, 12(18), 3508.

  4. Irving, K., Kieninger, M., & Ventura, O. N. (2019). Basis Set Effects in the Description of the Cl-O Bond in ClO and XClO/ClOX Isomers (X= H, O, and Cl) Using DFT and CCSD (T) Methods. Journal of Chemistry, 2019.

  5. José Lezama (2019) Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision. International Conference on Learning Representations

  6. Petsis, G., Salta, Z., Kosmas, A. M., & Ventura, O. N. (2019). Theoretical study of the microhydration of 1‐chloro and 2‐chloro ethanol as a clue for their relative propensity toward dehalogenation. International Journal of Quantum Chemistry, e25931.

  7. Salta, Z., Kosmas, A. M., Ventura, O. N., & Barone, V. (2019). Computational Evidence Suggests That 1-Chloroethanol May Be an Intermediate in the Thermal Decomposition of 2-Chloroethanol into Acetaldehyde and HCl. The Journal of Physical Chemistry A, 123(10), 1983-1998.

  8. Ventura, O.N., Kieninger, M., Salta, Z. et al.(2019) Enthalpies of formation of the benzyloxyl, benzylperoxyl, hydroxyphenyl radicals and related species on the potential energy surface for the reaction of toluene with the hydroxyl radical. Theor Chem Acc 138, 115 .

  9. Villamil, J., Avila, L. J., Morando, M., Sites Jr, J. W., Leaché, A. D., Maneyro, R., & Camargo, A. (2019). Coalescent-based species delimitation in the sand lizards of the Liolaemus wiegmannii complex (Squamata: Liolaemidae). Molecular Phylogenetics and Evolution, 138, 89-101.

2018

  1. Gutiérrez, A., & Fovell, R. G. (2018). A new gust parameterization for weather prediction models. Journal of Wind Engineering and Industrial Aerodynamics, 177, 45-59.