A data driven equivariant approach to constrained Gaussian mixture modeling

Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods to bypass this obstacle are based on the fact that unboundedness is avoided if the eigenvalues of the covariance matrices are bounded away from zero. This can be done imposing some constraints on the covariance matrices, i.e. by incorporating a priori information on the covariance structure of the mixture components. The present work introduces a constrained equivariant approach, where the class conditional covariance matrices are shrunk towards a pre-specified matrix Psi. Data-driven choices of the matrix Psi, when a priori information is not available, and the optimal amount of shrinkage are investigated. The effectiveness of the proposal is evaluated on the basis of a simulation study and an empirical example

Analytical Modeling of Interference Aware Power Control for the Uplink of Heterogeneous Cellular Networks

Inter-cell interference is one of the main limiting factors in current Heterogeneous Cellular Networks (HCNs). Uplink Fractional Power Control (FPC) is a well known method that aims to cope with such limiting factor as well as to save battery live. In order to do that, the path losses associated with Mobile Terminal (MT) transmissions are partially compensated so that a lower interference is leaked towards neighboring cells. Classical FPC techniques only consider a set of parameters that depends on the own MT transmission, like desired received power at the Base Station (BS) or the path loss between the MT and its serving BS, among others. Contrary to classical FPC, in this paper we use stochastic geometry to analyze a power control mechanism that keeps the interference generated by each MT under a given threshold. We also consider a maximum transmitted power and a partial compensation of the path loss. Interestingly, our analysis reveals that such Interference Aware (IA) method can reduce the average power consumption and increase the average spectral efficiency. Additionally, the variance of the interference is reduced, thus improving the performance of Adaptive Modulation and Coding (AMC) since the interference can be better estimated at the MT.Comment: 13 pages, 1 table and 7 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

New Strategies to Control Brown Rot Caused by Monilinia spp. of Stone Fruit

The importance of brown rot caused by Monilinia spp. in all stone fruit growing areas is well recognized. The infection occurs in the field but the most dangerous fruit losses happen during storage and commercialization. The disease control depends mainly on integrated strategy based on cultural practices and fungicide spray programmes in the field. Among cultural practices, the sanitization of orchard by minimizing inoculum potential, reducing the risk of blossom and fruit infections is essential for brown rot management. However, the requirements in sustainable agriculture, integrated crop management and organic production are increased in the last few decades, resulting in the need to develop other methods than fungicide applications to disease control. Several studies focused on alternative strategies, defining three different approaches: i) biological control with microbial antagonists, ii) use of natural products and iii) use of physico-chemical methods. Significant progress has been achieved in the reduction of pesticide use with these methods; although a multidisciplinary methodology that integrates sanitary and alternative strategies has to be investigated more fully. The new strategy, starting from the field with agronomic interventions and the selection of cultivars tolerant to Monilinia spp. could be usefully integrated by postharvest treatments based on low risk chemical fungicides, natural antimicrobial substances and other physical means determining a sustainable approach to brown rot control

Triagulac[c]ión | About Legazpi Market in Madrid

The main contribution of municipal architects to Madrid's Rationalism must be investigated in the construction of public buildings. Markets play a fundamental part among them, thanks to the work of F. J. Ferrero Llusìa, public architect since 1921. With his markets, between 1931 and 1934, it has brought a real change in European industrial architecture in the XIX century. With the premise of hygiene, constructive austerity, structural trueness and the rejection of ornament in favor of pure form, he created a new hypothesis that was characterized by the guiding principles of Rationalism. Awareness of the construction culture of the time, through observation of the details and technical literature linked to the “Mercado Central de Frutas y Verduras” in Madrid, are the conditions for an intervention of conscious restoration. The paper presents a process of slow re-appropriation of the site by the citizens, as an alternative to the current project proposed by the municipality

Clinical, Histological and Trichoscopic Correlations in Scalp Disorders

Trichoscopy is the term coined for the dermoscopic imaging of scalp and hair. This diagnostic technique, simple and noninvasive, can be used as a handy bedside tool for the diagnosis and follow-up of hair and scalp disorders. It allows the recognition of morphologic structures not visible by the naked eye and provides the clinician with a range of dermoscopic findings necessary for differential diagnosis. Trichoscopy observation can be broadly grouped as interfollicular patterns and follicular patterns. Recently, a third mixed class, called the follicular plus interfollicular pattern, has been introduced. Some of these features are specific to a certain scalp disease, while others can be found in many hair disorders. Although studies suggest that the use of trichoscopy can improve clinical accuracy, further investigation is needed. This review provides update information on the trichoscopic features of the most common scalp disorders, striving to show a histopathological and clinical correlation

A two-step estimator for multilevel latent class analysis with covariates

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms

A two-step estimator for multilevel latent class analysis with covariates

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (i) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (ii) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.Comment: Manuscript version accepted for publication in Psychometrik