Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible frame- work for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is com- patible with a rich set of mapping functions. Results show that the availability of additional continuous response vari- ables can simultaneously improve the annotation of the se- quential observations and yield good prediction performance in both synthetic data and real-world datasets.Comment: AAAI 201

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood

We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-likelihood and (linear) Bayesian support vector machines, which are also built on the max-margin principle. The discriminative factor model is further extended to the {\em nonlinear} case through mixtures of local linear classifiers, via Dirichlet processes. Fully local conjugacy of the model yields efficient inference with both Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on benchmark and real data demonstrate superior performance of the proposed model and its potential for applications in computational biology.Comment: 14 pages, 7 figures, ICML 201

Determination of the zero-order fringe position in digital speckle pattern interferometry

A method for determining the position of the zero-order fringe in a metrological experiment with digital speckle pattern interferometry is proposed. It is based on an averaging procedure with shifted images obtained before and after a load is applied. This technique is a complement to the phase-shifting methods. Experimental examples are shown

Representation of electric power systems by complex networks with applications to risk vulnerability assessment

The occurrence of impact events (e.g. blackouts with vast geographic coverage) into electrical critic infrastructure systems usually require the analysis of cascade failure root causes through the conduction of structural vulnerability studies, with well-defined methodologies that may guide decision-making for implementation of preventing actions and for operation recovering into the power system (e.g. N-1 and N-t contingency studies). This technical contribution provides some alternative techniques based upon complex networks and graph theory, which in the last few years have been proposed as useful methodologies for analysis of physical behavior of electric power systems. Vulnerability assessment is achieved by testing their performance into random risks and deliberate attacks threats scenarios. Results shown in this proposal lead to conclusions on the use of complex networks for contingency analysis by means of studies of those events that result in cascade failures and consumer disconnections. La ocurrencia de eventos de alto impacto (e.g., apagón con alcance geográfico) en sistemas eléctricos usualmente se diagnostica a través de técnicas de análisis estructural de vulnerabilidad, constituidas por metodologías definidas que permiten guiar la toma de decisiones en acciones de prevención y recuperación de la normalidad en la red (e.g., contingencias N-1 y N-t). En esta contribución técnica se presenta una metodología alternativa frente a las herramientas clásicas de análisis de contingencias (teoría de grafos), que últimamente se ha validado como método útil en el análisis físico de sistemas de potencia. Se realiza una valoración de la vulnerabilidad en redes de prueba IEEE, mediante cuantificación de su comportamiento ante escenarios de riesgos de tipo aleatorio o de ataques deliberados. Estos resultados permiten concluir la viabilidad de redes complejas para análisis de contingencias, mediante el estudio de eventos desencadenantes de fallos en cascada y desconexión de consumidores

Representation of electric power systems by complex networks with applications to risk vulnerability assessment

The occurrence of impact events (e.g. blackouts with vast geographic coverage) into electrical critic infrastructure systems usually require the analysis of cascade failure root causes through the conduction of structural vulnerability studies, with well-defined methodologies that may guide decision-making for implementation of preventing actions and for operation recovering into the power system (e.g. N-1 and N-t contingency studies). This technical contribution provides some alternative techniques based upon complex networks and graph theory, which in the last few years have been proposed as useful methodologies for analysis of physical behavior of electric power systems. Vulnerability assessment is achieved by testing their performance into random risks and deliberate attacks threats scenarios. Results shown in this proposal lead to conclusions on the use of complex networks for contingency analysis by means of studies of those events that result in cascade failures and consumer disconnections. La ocurrencia de eventos de alto impacto (e.g., apagón con alcance geográfico) en sistemas eléctricos usualmente se diagnostica a través de técnicas de análisis estructural de vulnerabilidad, constituidas por metodologías definidas que permiten guiar la toma de decisiones en acciones de prevención y recuperación de la normalidad en la red (e.g., contingencias N-1 y N-t). En esta contribución técnica se presenta una metodología alternativa frente a las herramientas clásicas de análisis de contingencias (teoría de grafos), que últimamente se ha validado como método útil en el análisis físico de sistemas de potencia. Se realiza una valoración de la vulnerabilidad en redes de prueba IEEE, mediante cuantificación de su comportamiento ante escenarios de riesgos de tipo aleatorio o de ataques deliberados. Estos resultados permiten concluir la viabilidad de redes complejas para análisis de contingencias, mediante el estudio de eventos desencadenantes de fallos en cascada y desconexión de consumidores

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the positional and orientational order of water and methanol around indole: a study on the microscopic origin of solubility

Although they are both highly polar liquids, there are a number of compounds, such as many pharmaceuticals, which show vastly different solubilities in methanol compared with water. From theories of the hydrophobic effect, it might be predicted that this enhanced solubility is due to association between drugs and the less polar -CH3 groups on methanol. In this work, detailed analysis on the atomic structural interactions between water, methanol and the small molecule indole – which is a precursor to many drugs and is sparingly soluble in water yet highly soluble in methanol – reveal that indole preferentially interacts with both water and methanol via electrostatic interactions rather than with direction interactions to the –CH3 groups. The presence of methanol hydrogen bonds with p electrons of the benzene ring of indole can explain the increase in solubility of indole in methanol relative to water. In addition, the excess entropy calculations performed here suggest that this solvation is enthalpically rather than entropically driven.Postprint (author's final draft