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Aiyangar, A. K., Vivanco, J., Au, A. G., Anderson, P. A., Smith, E. L., & Ploeg, H. L. (2014). Dependence of Anisotropy of Human Lumbar Vertebral Trabecular Bone on Quantitative Computed Tomography-Based Apparent Density. J. Biomech. Eng.-Trans. ASME, 136(9), 10 pp.
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Barrera, J., Homem-De-Mello, T., Moreno, E., Pagnoncelli, B. K., & Canessa, G. (2016). Chance-constrained problems and rare events: an importance sampling approach. Math. Program., 157(1), 153–189.
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Bernales, A., Reus, L., & Valdenegro, V. (2022). Speculative bubbles under supply constraints, background risk and investment fraud in the art market. J. Corp. Financ., Early Access.
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Bertossi, L. (2021). Specifying and computing causes for query answers in databases via database repairs and repair-programs. Knowl. Inf. Syst., 63, 199–231.
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Bertossi, L. (2022). Declarative Approaches to Counterfactual Explanations for Classification. Theory Pract. Log. Program., Early Access.
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Bolte, J., Hochart, A., & Pauwels, E. (2018). Qualification Conditions In Semialgebraic Programming. SIAM J. Optim., 28(2), 1867–1891.
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Bustamante, M., & Contreras, M. (2016). Multi-asset Black-Scholes model as a variable second class constrained dynamical system. Physica A, 457, 540–572.
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Canessa, G., Gallego, J. A., Ntaimo, L., & Pagnoncelli, B. K. (2019). An algorithm for binary linear chance-constrained problems using IIS. Comput. Optim. Appl., 72(3), 589–608.
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Caniupan, M., Bravo, L., & Hurtado, C. A. (2012). Repairing inconsistent dimensions in data warehouses. Data Knowl. Eng., 79-80, 17–39.
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Carbonnel, C., Romero, M., & Zivny, S. (2020). Point-Width and Max-CSPs. ACM Trans. Algorithms, 16(4), 28 pp.
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Cho, A. D., Carrasco, R. A., & Ruz, G. A. (2022). Improving Prescriptive Maintenance by Incorporating Post-Prognostic Information Through Chance Constraints. IEEE Access, 10, 55924–55932.
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Contreras, G. M. (2014). Stochastic volatility models at rho = +/- 1 as second class constrained Hamiltonian systems. Physica A, 405, 289–302.
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Contreras, M., & Hojman, S. A. (2014). Option pricing, stochastic volatility, singular dynamics and constrained path integrals. Physica A, 393, 391–403.
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Contreras, M., & Pena, J. P. (2019). The quantum dark side of the optimal control theory. Physica A, 515, 450–473.
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Contreras, M., Pellicer, R., & Villena, M. (2017). Dynamic optimization and its relation to classical and quantum constrained systems. Physica A, 479, 12–25.
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Cortes, M. P., Mendoza, S. N., Travisany, D., Gaete, A., Siegel, A., Cambiazo, V., et al. (2017). Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing. Front. Microbiol., 8, 15 pp.
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Donoso, R. A., Ruiz, D., Garate-Castro, C., Villegas, P., Gonzalez-Pastor, J. E., de Lorenzo, V., et al. (2021). Identification of a self-sufficient cytochrome P450 monooxygenase from Cupriavidus pinatubonensis JMP134 involved in 2-hydroxyphenylacetic acid catabolism, via homogentisate pathway. Microb. Biotechnol., 14(5), 1944–1960.
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Espinoza, D., Goycoolea, M., & Moreno, E. (2015). The precedence constrained knapsack problem: Separating maximally violated inequalities. Discret Appl. Math., 194, 65–80.
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Fernandez, M., Munoz, F. D., & Moreno, R. (2020). Analysis of imperfect competition in natural gas supply contracts for electric power generation: A closed-loop approach. Energy Econ., 87, 15 pp.
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Henriquez, P. A., & Ruz, G. A. (2019). Noise reduction for near-infrared spectroscopy data using extreme learning machines. Eng. Appl. Artif. Intell., 79, 13–22.
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