A novel framework for parsimonious multivariate analysis

This paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such as gene arrays, digital pictures, etc. The feature selection relies on the analysis of consistency in the behaviour of the input variables through the elements of an ensemble of MVA projection matrices. The ensemble is constructed following a bootstrap that builds on an efficient and generalized MVA formulation that covers PCA, CCA and OPLS. Moreover, it allows the estimation of the relative relevance of each selected input variable. Experimental results point out that the features extracted by the parsimonious MVA have excellent discrimination power, comparing favorably with state-of-the-art methods, and are potentially useful to build interpretable features. Besides, the parsimonious feature extractor is shown to be robust against to parameter selection, as we all computationally efficient.This work has been partly funded by the Spanish MINECO grant TEC2014-52289R and TEC2013-48439-C4-1-R. The authors want to thank the action editor and the reviewers for their valuable feedback

Regularized multivariate analysis framework for interpretable high-dimensional variable selection

Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we propose a novel method that takes advantage of the,2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations.This work has been partly supported by MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R and TEC2016-75161-C2-2-R, and Comunidad de Madrid projects PRICAM P2013/ICE-2933 and S2013/ICE-2933

Nonnegative OPLS for supervised design of filter banks: application to image and audio feature extraction

Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and why filters work for audio or visual applications is a desired property, especially when energy or spectral signals are involved. In these cases, due to the nature of these signals, the nonnegativity of the filter weights is a desired property to better understand its working. Because of these two necessities, we propose different methods to reduce the dimensionality of data while the nonnegativity and interpretability of the solution are assured. In particular, we propose a generalized methodology to design filter banks in a supervised way for applications dealing with nonnegative data, and we explore different ways of solving the proposed objective function consisting of a nonnegative version of the orthonormalized partial least-squares method. We analyze the discriminative power of the features obtained with the proposed methods for two different and widely studied applications: texture and music genre classification. Furthermore, we compare the filter banks achieved by our methods with other state-of-the-art methods specifically designed for feature extraction.This work was supported in parts by the MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R, TEC2016-75161-C2-1-R, TEC2016-75161-C2-2-R, TEC2016-81900-REDT/AEI, and PRICAM (S2013/ICE-2933)

Sparse and kernel OPLS feature extraction based on eigenvalue problem solving

Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. Usually, in machine learning papers OPLS projections are obtained by solving a generalized eigenvalue problem. However, in statistical papers the method is typically formulated in terms of a reduced-rank regression problem, leading to a formulation based on a standard eigenvalue decomposition. A first contribution of this paper is to derive explicit expressions for matching the OPLS solutions derived under both approaches and discuss that the standard eigenvalue formulation is also normally more convenient for feature extraction in machine learning. More importantly, since optimization with respect to the projection vectors is carried out without constraints via a minimization problem, inclusion of penalty terms that favor sparsity is straightforward. In the paper, we exploit this fact to propose modified versions of OPLS. In particular, relying on the ℓ1 norm, we propose a sparse version of linear OPLS, as well as a non-linear kernel OPLS with pattern selection. We also incorporate a group-lasso penalty to derive an OPLS method with true feature selection. The discriminative power of the proposed methods is analyzed on a benchmark of classification problems. Furthermore, we compare the degree of sparsity achieved by our methods and compare them with other state-of-the-art methods for sparse feature extraction.This work was partly supported by MINECO projects TEC2011-22480 and PRIPIBIN-2011-1266.Publicad

Evaluation of receiver-feedback techniques for fragmentation over LPWANs

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe Internet Engineering Task Force (IETF) has standardized a new framework for IPv6 support over Low Power Wide Area Networks (LPWANs), called Static Context Header Compression and Fragmentation (SCHC). SCHC includes acknowledgment (ACK)-based mechanisms for reliable fragmented packet transmission. For the latter, SCHC defines a Receiver-Feedback Technique (RFT), called Compressed Bitmap (CB), by which a receiver reports to the sender whether the fragments carrying a packet have been received or not. Such information is carried as ACK payload. Considering the extraordinary frame size and message rate constraints of LPWANs, ACK payload size becomes crucial. In this paper, we compare the performance of CB with that of several alternative RFTs, namely List of Lost Fragments (LLF), List of Deltas (LoD), and Uncompressed Bitmap (UB), where the latter is used as a benchmark. We evaluate the considered RFTs in terms of ACK size, number of Layer 2 (L2) frames needed to carry an ACK, and ACK Time on Air. Our analysis shows that the use of RFTs different from CB offers significant performance improvement in many scenarios. Furthermore, we provide guidance on which RFT should be used for different packet sizes, error rates and error patterns.This research is funded in part by the ERDF and the Spanish Government through project TEC2016-79988-P and project PID2019-106808RA-I00, AEI/FEDER, EU.Peer ReviewedPostprint (author's final draft

Patrones de seguridad software en el contexto de la Arquitectura multicapa para la plataforma J2EE

Durante las últimas décadas la dependencia de los sistemas informáticos en todos los ámbitos de la sociedad ha crecido de forma exponencial. Hoy en día cualquier empresa depende de un sistema informático para almacenar información, gestionar su actividad comercial o tomar decisiones de negocio. Además, cualquier persona en su vida diaria realiza gestiones mediante sistemas informáticos, como la gestión de sus cuentas bancarias, el pago de los impuestos, compras en internet, etc. Debido a la rápida adopción de tecnologías en red han aparecido nuevas amenazas tales como interrupciones de servicio, accesos no autorizados, suplantación o robo de identidad, robo de datos confidenciales y muchos más riesgos de seguridad. Estas amenazas exponen la importancia de la seguridad e imponen a las empresas una responsabilidad legal y ética de almacenar de forma segura la información. Forzar los mecanismos de seguridad a todos los niveles asegura que la información es procesada, almacenada o transmitida de forma segura. En este proyecto hemos dotado de seguridad a todos los niveles a una aplicación en el contexto de la arquitectura multicapa para la plataforma J2EE. Mediante este proyecto no sólo hemos protegido a la aplicación de ataques como inyección SQL o acceso no autorizado, sino que también se han registrado todas las operaciones realizadas de forma segura, con el propósito de conocer al autor, la fecha y la operación realizada. Los mecanismos de seguridad implementados también incluyen, entre otros, la transmisión de datos de forma segura a través de la red mediante WS-Security y HTTPS, la monitorización de los servicios web y la intercepción de los mensajes enviados a través de la red para su autorización en función de distintos parámetros como la IP

Regulation of internal promoters in a zinc-responsive operon is influenced by transcription from upstream promoters

In the cyanobacterium Anabaena sp. strain PCC 7120 (also known as Nostoc sp. strain PCC 7120), a zinc-responsive operon (all4725-all4721) has been described, which contains 4 distinct promoters. The two most upstream ones bind Zur with high affinity, whereas the other two do not or do so with a very low affinity. In this paper, a detailed characterization of the four promoters is presented, showing that all four were induced by metal depletion, and they were constitutively derepressed in a zur mutant, despite the two downstream promoters not being direct targets for this regulator. Crucially, induction by metal depletion of the two downstream promoters was abrogated when transcription initiated at the upstream promoters was interrupted by a polar insertion midway in the operon. In contrast, insertion of a nitrogen-responsive promoter at a roughly similar position provoked the two downstream promoters to adopt a regulatory pattern mimicking that of the inserted promoter. Thus, regulation of the two downstream promoters is apparently influenced by transcription from promoters upstream. Evidence is presented indicating that the activity of the two downstream promoters is kept basal in Anabaena by repression. A regulatory model compatible with these results is proposed, where promoters controlled by repression in bacterial operons may be subjected to a hierarchical regulation depending on their position in the operon. According to this model, internal promoters may respond to stimuli governing the activity of promoters upstream by an indirect regulation and to specific stimuli by a direct regulation.Ministerio de Ciencia e Innovación y European Social Fund BFU2010-19544Junta de Andalucía y FEDER CVI2007-0316

Enfermedad Holandesa, el caso colombiano

Durante el periodo comprendido entre 2001 y 2010, la economía colombiana incrementó sus ingresos por exportación de petróleo como consecuencia del aumento en la producción nacional y en el aumento del precio del petróleo a nivel internacional. Adicionalmente, la moneda colombiana, el peso, presentó una fuerte revaluación durante este mismo periodo, siendo una de las monedas más apreciadas de la región. Es por esto que el presente trabajo de grado se hizo con el fin de determinar y demostrar los síntomas de la enfermedad holandesa, el caso colombiano.El Modelo Base. Revisión de Literatura. La Maldición de los recursos naturales. El modelo. Los Síntomas de la Enfermedad Holandesa. Apreciación del Tipo de Cambio Real. Crecimiento impulsado por el petróleo. Relativa Desindustrialización. Bases de Datos. Análisis cuantitativo. Análisis y características de las series. Ecuaciones de cointegración. Metodología de vectores autoregresivos (VAR). Metodología de vectores autoregresivos (VAR): Primer síntoma. Metodología de vectores autoregresivos (VAR): Segundo síntoma. Metodología de vectores autoregresivos (VAR): Tercer síntoma.Magíster en Finanzas CorporativasMaestrí

Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing

The aim of our research was to discover the effects of the long-term irrigation with saline reclaimed (RW) and transfer (TW) water and different irrigation strategies: control (C) and regulated deficit irrigation (RDI) on yield and fruit quality of grapefruit at harvest and during cold storage. TW-RDI treatment decreased tree canopy (TC) and crop load, resulting in a 21% reduction of fruit yield. Regarding fruit quality, RW notably decreased peel thickness at harvest (about 8%); however, this difference was not remained during cold storage. Sugar/acid ratio was mainly increased by RDI, but also by RW, due to an important increase in soluble solid content (11% of average value for TW-RDI, RW-C and RW-RDI). In addition, RDI combined with RW, significantly increased the number of fruits in small category 5 at the end of cold storage. Finally, neither ratio yield/TC nor irrigation water productivity were affected by any irrigation treatments.This study was supported by two CICYT (AGL2010-17553 and AGL2013-49047-C2-482 515 2-R) projects and SIRRIMED (KBBE-2009-1- 2-03, PROPOSAL N◦245159) 483 project. We are also grateful to SENECA–Excelencia Científica (19903/GERM/15) for 484 providing funds for this research