Leobardo Valera | University of Texas at El Paso (UTEP) (original) (raw)
Papers by Leobardo Valera
Predictions are rarely absolutely accurate. Often, the future values of quantities of interest de... more Predictions are rarely absolutely accurate. Often, the future values of quantities of interest depend on some parameters that we only know with some uncertainty. To make sure that all possible solutions satisfy desired constraints, it is necessary to generate a representative finite sample, so that if the constraints are satisfied for all the functions from this sample, then we can be sure that these constraints will be satisfied for the actual future behavior as well. At present, such a sample is selected based by Monte-Carlo simulations, but, as we show, such selec-tion may underestimate the danger of violating the constraints. To avoid such an underestimation, we propose a different algorithms that uses interval computations.
The Soil Moisture database ESA_CCI was updated to version 6.1
Use-case of road map towards reproducibility of scientific workflows through leveraging container... more Use-case of road map towards reproducibility of scientific workflows through leveraging containers. We present an automatic fine-grained metadata that provides a full description of the dataflow, ensuring traceability of data and explainability of results. We question the trade-off between scalability and reproducibility.
Use-case of road map towards trustworthiness of scientific workflows through leveraging container... more Use-case of road map towards trustworthiness of scientific workflows through leveraging containers and annotating the execution of every data component. We present an automatic fine-grained metadata that provides a full description of the dataflow, ensuring traceability of data and explainability of results. We compare the granularity level of containerization with the information captured in the metadata, concluding that in order to be able to trust in the results, fine-grained containerization is needed.
Remote Sensing
Soil moisture is an important parameter that regulates multiple ecosystem processes and provides ... more Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of indepen...
One of the most widely used methods for solving equations is the classical Newton\u27s method. Wh... more One of the most widely used methods for solving equations is the classical Newton\u27s method. While this method often works -- and is used in computers for computations ranging from square root to division -- sometimes, this method does not work. Usual textbook examples describe situations when Newton\u27s method works for some initial values but not for others. A natural question that students often ask is whether there exist functions for which Newton\u27s method never works -- unless, of course, the initial approximation is already the desired solution. In this paper, we provide simple examples of such functions
Abstract—In many application areas, such as meteorology, traffic control, etc., it is desirable t... more Abstract—In many application areas, such as meteorology, traffic control, etc., it is desirable to employ swarms of Unmanned Aerial Vehicles (UAVs) to provide us with a good picture of the changing situation and thus, to help us make better predictions (and make better decisions based on these predictions). To avoid duplication, interference, and collisions, UAVs must coordinate their trajectories. As a result, the optimal control of each of these UAVs should depend on the positions and velocities of all others – which makes the corresponding control problem very complicated. Since, in contrast to controlling a single UAV, the resulting problem is too complicated to expect an explicit solution, a natural idea is to extra expert rules and use fuzzy control methodology to translate these rules into a precise control strategy. However, with many possible combinations of variables, it is not possible to elicit that many rules.
One of the most widely used methods for solving equations is the classical Newton’s method. While... more One of the most widely used methods for solving equations is the classical Newton’s method. While this method often works – and is used in computers for computations ranging from square root to division – sometimes, this method does not work. Usual textbook examples describe situations when Newton’s method works for some initial values but not for others. A natural question that students often ask is whether there exist functions for which Newton’s method never works – unless, of course, the initial approximation is already the desired solution. In this paper, we provide simple examples of such functions. 1 Formulation of the Problem Newton’s method: a brief reminder. One of the most widely used methods for finding a solution to a non-linear equation f (x) = 0 is a method designed many centuries ago by Newton himself; see, e.g., [1]. This method is based on the fact that the derivative f ′(x) is defined as the limit of the ratio f (x+h)− f (x) h when h tends to 0. This means that fo...
Many engineering problems boil down to solving partial differential equations (PDEs) that describ... more Many engineering problems boil down to solving partial differential equations (PDEs) that describe real-life phenomena. Nevertheless, efficiently and reliably solving such problems constitutes a major challenge in computational sciences and in engineering in general. PDE-based systems can reach sizes so large after they are discretized. The large size in these problems generate several issues, among them we can mention: large space of storing, computing time, and the most important, lost of accuracy. A popular approach to solving such problems is assume that the PDE’s solution is in a subspace, and the solution is sought there. This assumption and later searching is named Model-Order Reduction (MOR). As we have mentioned before, MOR aims at reducing the size of the original large problem by projecting it onto a subspace. The quality of MOR is highly dependent on the right choice of the projection. Assuming that the projection is relevant, i.e. the behavior of the projected system re...
Many natural phenomena can be modeled as ordinary or partial differential equations. A way to fin... more Many natural phenomena can be modeled as ordinary or partial differential equations. A way to find solutions of such equations is to discretize them and to solve the corresponding (possibly) nonlinear large systems of equations; see (Li and Chen, 2008). Solving a large nonlinear system of equations is very computationally complex due to several numerical issues, such as high linear-algebra cost and large memory requirements. Model-Order Re- duction (MOR) has been proposed as a way to overcome the issues associated with large dimensions, the most used approach for doing so being Proper Orthogonal Decomposition (POD); see (Schilders and Vorst, 2008). The key idea of POD is to reduce a large number of interdependent variables (snapshots) of the system to a much smaller number of uncorrelated variables while retaining as much as possible of the variation in the original variables. In this work, we show how intervals and constraint solving techniques can be used to compute all the snapsh...
In this thesis, we are interested in making decision over a model of a dynamic system. We want to... more In this thesis, we are interested in making decision over a model of a dynamic system. We want to know, on one hand, how the corresponding dynamic phenomenon unfolds under different input parameters (simulations). These simulations might help researchers to design devices with a better performance than the actual ones. On the other hand, we are also interested in predicting the behavior of the dynamic system based on knowledge of the phenomenon in order to prevent undesired outcomes. Finally, this thesis is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. Understanding the behavior of such models is challenging. The numerical resolution of those model leads to systems of equations, sometimes nonlinear, that can involve millions of variables making it prohibitive in CPU time to run repeatedly for many different configurations. These types of models rarely take into account the uncertainty associated with the parameters...
To improve teaching and learning, it is important to understand how knowledge propagates. In gene... more To improve teaching and learning, it is important to understand how knowledge propagates. In general, when a new piece of knowledge is introduced, people start learning about it. Since the potential audience is limited, after some time, the number of new learners starts to decrease. Traditional models of knowledge propagation are based on differential equations; in these models, the number of new learners decreases exponentially with time. Recently, a new power law model for knowledge propagation was proposed. In this model, the number of learners decreases much slower, as a negative power of time. In this paper, we compare the two models on the example of readers’ comments on the Out of Eden Walk, a journalistic and educational project in which informative messages (“dispatches”) from different parts of the world are regularly posted on the web. Readers who learned the new interesting information from these dispatches are encouraged to post comments. Usually, a certain proportion o...
Competencies are behaviors that some people master better than others, which makes them more effe... more Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component ana...
Louisville-Bratu-Gelfand equation appears in many different physical situations ranging from comb... more Louisville-Bratu-Gelfand equation appears in many different physical situations ranging from combustion to explosions to astrophysics. The fact that the same equation appears in many different situations seems to indicate that this equation should not depend on any specific physical process, that it should be possible to derive it from general principles. This is indeed what we show in this paper: that this equation can be naturally derived from basic symmetry requirements.
Fuzzy Logic in Intelligent System Design
Computer simulations of dynamic systems are really important to better understand some processes ... more Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, "on-the-fly" answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an ongoing process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to... more The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, uncertainty is hardly taken into account. Changes in the definition of a model, for instance, could have dramatic effects on the outcome of simulations. Therefore, neither reduced models nor initial conclusions could be 100% relied upon. In this research, Interval Constraint Solving Techniques (ICST) are employed to handle and quantify uncertainty. The goal is to identify key features of a given dynamical phenomenon in order to be able to propagate the characteristics of the model forward and predict its future behavior to obtain 100% guaranteed results. This is specifically important in applications, as a reliable understanding of a developing situation could allow for preventative or palliative measures before a situation aggravates.
Predictions are rarely absolutely accurate. Often, the future values of quantities of interest de... more Predictions are rarely absolutely accurate. Often, the future values of quantities of interest depend on some parameters that we only know with some uncertainty. To make sure that all possible solutions satisfy desired constraints, it is necessary to generate a representative finite sample, so that if the constraints are satisfied for all the functions from this sample, then we can be sure that these constraints will be satisfied for the actual future behavior as well. At present, such a sample is selected based by Monte-Carlo simulations, but, as we show, such selec-tion may underestimate the danger of violating the constraints. To avoid such an underestimation, we propose a different algorithms that uses interval computations.
The Soil Moisture database ESA_CCI was updated to version 6.1
Use-case of road map towards reproducibility of scientific workflows through leveraging container... more Use-case of road map towards reproducibility of scientific workflows through leveraging containers. We present an automatic fine-grained metadata that provides a full description of the dataflow, ensuring traceability of data and explainability of results. We question the trade-off between scalability and reproducibility.
Use-case of road map towards trustworthiness of scientific workflows through leveraging container... more Use-case of road map towards trustworthiness of scientific workflows through leveraging containers and annotating the execution of every data component. We present an automatic fine-grained metadata that provides a full description of the dataflow, ensuring traceability of data and explainability of results. We compare the granularity level of containerization with the information captured in the metadata, concluding that in order to be able to trust in the results, fine-grained containerization is needed.
Remote Sensing
Soil moisture is an important parameter that regulates multiple ecosystem processes and provides ... more Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of indepen...
One of the most widely used methods for solving equations is the classical Newton\u27s method. Wh... more One of the most widely used methods for solving equations is the classical Newton\u27s method. While this method often works -- and is used in computers for computations ranging from square root to division -- sometimes, this method does not work. Usual textbook examples describe situations when Newton\u27s method works for some initial values but not for others. A natural question that students often ask is whether there exist functions for which Newton\u27s method never works -- unless, of course, the initial approximation is already the desired solution. In this paper, we provide simple examples of such functions
Abstract—In many application areas, such as meteorology, traffic control, etc., it is desirable t... more Abstract—In many application areas, such as meteorology, traffic control, etc., it is desirable to employ swarms of Unmanned Aerial Vehicles (UAVs) to provide us with a good picture of the changing situation and thus, to help us make better predictions (and make better decisions based on these predictions). To avoid duplication, interference, and collisions, UAVs must coordinate their trajectories. As a result, the optimal control of each of these UAVs should depend on the positions and velocities of all others – which makes the corresponding control problem very complicated. Since, in contrast to controlling a single UAV, the resulting problem is too complicated to expect an explicit solution, a natural idea is to extra expert rules and use fuzzy control methodology to translate these rules into a precise control strategy. However, with many possible combinations of variables, it is not possible to elicit that many rules.
One of the most widely used methods for solving equations is the classical Newton’s method. While... more One of the most widely used methods for solving equations is the classical Newton’s method. While this method often works – and is used in computers for computations ranging from square root to division – sometimes, this method does not work. Usual textbook examples describe situations when Newton’s method works for some initial values but not for others. A natural question that students often ask is whether there exist functions for which Newton’s method never works – unless, of course, the initial approximation is already the desired solution. In this paper, we provide simple examples of such functions. 1 Formulation of the Problem Newton’s method: a brief reminder. One of the most widely used methods for finding a solution to a non-linear equation f (x) = 0 is a method designed many centuries ago by Newton himself; see, e.g., [1]. This method is based on the fact that the derivative f ′(x) is defined as the limit of the ratio f (x+h)− f (x) h when h tends to 0. This means that fo...
Many engineering problems boil down to solving partial differential equations (PDEs) that describ... more Many engineering problems boil down to solving partial differential equations (PDEs) that describe real-life phenomena. Nevertheless, efficiently and reliably solving such problems constitutes a major challenge in computational sciences and in engineering in general. PDE-based systems can reach sizes so large after they are discretized. The large size in these problems generate several issues, among them we can mention: large space of storing, computing time, and the most important, lost of accuracy. A popular approach to solving such problems is assume that the PDE’s solution is in a subspace, and the solution is sought there. This assumption and later searching is named Model-Order Reduction (MOR). As we have mentioned before, MOR aims at reducing the size of the original large problem by projecting it onto a subspace. The quality of MOR is highly dependent on the right choice of the projection. Assuming that the projection is relevant, i.e. the behavior of the projected system re...
Many natural phenomena can be modeled as ordinary or partial differential equations. A way to fin... more Many natural phenomena can be modeled as ordinary or partial differential equations. A way to find solutions of such equations is to discretize them and to solve the corresponding (possibly) nonlinear large systems of equations; see (Li and Chen, 2008). Solving a large nonlinear system of equations is very computationally complex due to several numerical issues, such as high linear-algebra cost and large memory requirements. Model-Order Re- duction (MOR) has been proposed as a way to overcome the issues associated with large dimensions, the most used approach for doing so being Proper Orthogonal Decomposition (POD); see (Schilders and Vorst, 2008). The key idea of POD is to reduce a large number of interdependent variables (snapshots) of the system to a much smaller number of uncorrelated variables while retaining as much as possible of the variation in the original variables. In this work, we show how intervals and constraint solving techniques can be used to compute all the snapsh...
In this thesis, we are interested in making decision over a model of a dynamic system. We want to... more In this thesis, we are interested in making decision over a model of a dynamic system. We want to know, on one hand, how the corresponding dynamic phenomenon unfolds under different input parameters (simulations). These simulations might help researchers to design devices with a better performance than the actual ones. On the other hand, we are also interested in predicting the behavior of the dynamic system based on knowledge of the phenomenon in order to prevent undesired outcomes. Finally, this thesis is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. Understanding the behavior of such models is challenging. The numerical resolution of those model leads to systems of equations, sometimes nonlinear, that can involve millions of variables making it prohibitive in CPU time to run repeatedly for many different configurations. These types of models rarely take into account the uncertainty associated with the parameters...
To improve teaching and learning, it is important to understand how knowledge propagates. In gene... more To improve teaching and learning, it is important to understand how knowledge propagates. In general, when a new piece of knowledge is introduced, people start learning about it. Since the potential audience is limited, after some time, the number of new learners starts to decrease. Traditional models of knowledge propagation are based on differential equations; in these models, the number of new learners decreases exponentially with time. Recently, a new power law model for knowledge propagation was proposed. In this model, the number of learners decreases much slower, as a negative power of time. In this paper, we compare the two models on the example of readers’ comments on the Out of Eden Walk, a journalistic and educational project in which informative messages (“dispatches”) from different parts of the world are regularly posted on the web. Readers who learned the new interesting information from these dispatches are encouraged to post comments. Usually, a certain proportion o...
Competencies are behaviors that some people master better than others, which makes them more effe... more Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component ana...
Louisville-Bratu-Gelfand equation appears in many different physical situations ranging from comb... more Louisville-Bratu-Gelfand equation appears in many different physical situations ranging from combustion to explosions to astrophysics. The fact that the same equation appears in many different situations seems to indicate that this equation should not depend on any specific physical process, that it should be possible to derive it from general principles. This is indeed what we show in this paper: that this equation can be naturally derived from basic symmetry requirements.
Fuzzy Logic in Intelligent System Design
Computer simulations of dynamic systems are really important to better understand some processes ... more Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, "on-the-fly" answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an ongoing process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to... more The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, uncertainty is hardly taken into account. Changes in the definition of a model, for instance, could have dramatic effects on the outcome of simulations. Therefore, neither reduced models nor initial conclusions could be 100% relied upon. In this research, Interval Constraint Solving Techniques (ICST) are employed to handle and quantify uncertainty. The goal is to identify key features of a given dynamical phenomenon in order to be able to propagate the characteristics of the model forward and predict its future behavior to obtain 100% guaranteed results. This is specifically important in applications, as a reliable understanding of a developing situation could allow for preventative or palliative measures before a situation aggravates.