Contamination source inference in water distribution networks

We study the inference of the origin and the pattern of contamination in water distribution networks. We assume a simplified model for the dyanmics of the contamination spread inside a water distribution network, and assume that at some random location a sensor detects the presence of contaminants. We transform the source location problem into an optimization problem by considering discrete times and a binary contaminated/not contaminated state for the nodes of the network. The resulting problem is solved by Mixed Integer Linear Programming. We test our results on random networks as well as in the Modena city network

Statistical mechanics of sparse generalization and model selection

One of the crucial tasks in many inference problems is the extraction of sparse information out of a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penality term, the $L_p$ norm of the model parameters, with $p\leq 1$ for efficient dilution. Here we propose a statistical-mechanics analysis of the problem in the setting of perceptron memorization and generalization. Using a replica approach, we are able to evaluate the relative performance of naive dilution (obtained by learning without dilution, following by applying a threshold to the model parameters), $L_1$ dilution (which is frequently used in convex optimization) and $L_0$ dilution (which is optimal but computationally hard to implement). Whereas both $L_p$ diluted approaches clearly outperform the naive approach, we find a small region where $L_0$ works almost perfectly and strongly outperforms the simpler to implement $L_1$ dilution.Comment: 18 pages, 9 eps figure

Stability of the replica symmetric solution in diluted perceptron learning

We study the role played by the dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica symmetric solution as a function of the dilution field for the generalization and memorization problems. Thanks to a Gardner like stability analysis we show that at any fixed ratio $\alpha$ between the number of patterns M and the dimension N of the perceptron ($\alpha=M/N$), there exists a critical dilution field $h_c$ above which the replica symmetric ansatz becomes unstable.Comment: Stability of the solution in arXiv:0907.3241, 13 pages, (some typos corrected

Reconversión tecnológica de un motor diésel por motor eléctrico en la empresa Promatin S.A.

El constante crecimiento Industrial, no es ajena a la problemática de calentamiento global y por el contrario, lo convierten en un actor fundamental, lo anterior, lleva a este sector productivo a la necesidad de buscar y analizar ofertas tecnológicas más limpias, que permitan reducir sus emisiones de CO2, aumentar la eficiencia productiva y energética, obtener beneficios económicos y mejorar las condiciones sociales y de seguridad de sus trabajadores. Además de las variables mencionadas, la decisión de seleccionar una alternativa tecnológica en búsqueda de la sostenibilidad cuenta con otros problemas de carácter técnico, relacionadas con los altos costos, estrategias de financiación y falta de conocimiento, entre otros. Este proyecto de investigación orientado a un estudio de huella de carbono, propone ilustrar las ventajas y potencialidades que conllevaría una alternativa de producción más limpia a través de mejoras tecnológicas, al sustituir un motor Diésel por un motor Eléctrico en Compactadora (Prensa) de chatarra en estado estacionaria en el sector Siderúrgico. En este sentido, se procedió a realizar visitas técnicas en la Empresa Promatin S.A. y luego de analizar la información disponible se procedió a realizar los cálculos y determinar el desempeño de la reconversión tecnológica

Gauge-free cluster variational method by maximal messages and moment matching

We present an implementation of the cluster variational method (CVM) as a message passing algorithm. The kind of message passing algorithm used for CVM, usually named generalized belief propagation (GBP), is a generalization of the belief propagation algorithm in the same way that CVM is a generalization of the Bethe approximation for estimating the partition function. However, the connection between fixed points of GBP and the extremal points of the CVM free energy is usually not a one-to-one correspondence because of the existence of a gauge transformation involving the GBP messages. Our contribution is twofold. First, we propose a way of defining messages (fields) in a generic CVM approximation, such that messages arrive on a given region from all its ancestors, and not only from its direct parents, as in the standard parent-to-child GBP. We call this approach maximal messages. Second, we focus on the case of binary variables, reinterpreting the messages as fields enforcing the consistency between the moments of the local (marginal) probability distributions. We provide a precise rule to enforce all consistencies, avoiding any redundancy, that would otherwise lead to a gauge transformation on the messages. This moment matching method is gauge free, i.e., it guarantees that the resulting GBP is not gauge invariant. We apply our maximal messages and moment matching GBP to obtain an analytical expression for the critical temperature of the Ising model in general dimensions at the level of plaquette CVM. The values obtained outperform Bethe estimates, and are comparable with loop corrected belief propagation equations. The method allows for a straightforward generalization to disordered systems

Correlating Water Quality and Profile Data in the Florida Keys using Machine Learning Methods

Water quality is a very active subject of research in the water science field, where its importance includes maintaining the environment, managing wastewater, and securing fresh water. However, the increase of human development has led to problems that are affecting the ecosystem. Motivated by these problems, this research aims to find a solution for understanding the coastal water of the Florida Keys. The research used machine learning methods to find a correlation between water quality dataset and profile measurements dataset. To achieve this objective, the research first went through cleaning, rescuing, and structuring a readable dataset of the profile measurements that could be used in the analysis. Once the profile measurements dataset was completed, the next step was to find the correlation. To get a correlation between two datasets, the research proposed the use of regression coefficients coming from four different measurements in the profile dataset. Then, the coefficients were clustered using k-means and an independency test was carried out on the two datasets. Lastly, the research also built a water drone in the form of an airboat, which can collect data and can be controlled through an android app