MCMC-driven importance samplers

Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptive importance sampling algorithms, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are employed to drive an underlying multiple importance sampling scheme. The modular nature of the layered adaptive importance sampling scheme allows for different possible implementations, yielding a variety of different performances and computational costs. In this work, we propose different enhancements of the classical layered adaptive importance sampling setting in order to increase the efficiency and reduce the computational cost, of both upper and lower layers. The different variants address computational challenges arising in real-world applications, for instance with highly concentrated posterior distributions. Furthermore, we introduce different strategies for designing cheaper schemes, for instance, recycling samples generated in the upper layer and using them in the final estimators in the lower layer. Different numerical experiments show the benefits of the proposed schemes, comparing with benchmark methods presented in the literature, and in several challenging scenarios

A probabilistic patient scheduling model for reducing the number of no-shows

No-shows in medical centres cause under-utilisation of resources and increase waiting times in specialty health care services. Although this problem has been addressed in literature, behavioural issues associated with the patient's socio-demographic characteristics and diagnosis have not been widely studied. In this article, we propose a model that includes such behavioural issues in order to reduce impact of no-shows in medical services. The objective is maximising the health centre's expected revenue by using show-up probabilities estimated for each combination of patient and appointment slot. Additionally, the model considers the requirements imposed by both the health centre's management and the health authorities. An extension of the model allows overbooking in some appointment slots. Experimental results show that the proposed model can reduce the waiting list length by 13%, and to attain an increase of about 5% in revenue, when comparing to a model that assigns patients to the first available slot

Optimal high-dimensional entanglement concentration in the bipartite scenario

Considering pure quantum states, entanglement concentration is the procedure where from $N$ copies of a partially entangled state, a single state with higher entanglement can be obtained. Getting a maximally entangled state is possible for $N=1$. However, the associated success probability can be extremely low while increasing the system's dimensionality. In this work, we study two methods to achieve a probabilistic entanglement concentration for bipartite quantum systems with a large dimensionality for $N=1$, regarding a reasonably good probability of success at the expense of having a non-maximal entanglement. Firstly, we define an efficiency function $\mathcal{Q}$ considering a tradeoff between the amount of entanglement (quantified by the I-Concurrence) of the final state after the concentration procedure and its success probability, which leads to solving a quadratic optimization problem. We found an analytical solution, ensuring that an optimal scheme for entanglement concentration can always be found in terms of $\mathcal{Q}$. Finally, a second method was explored, which is based on fixing the success probability and searching for the maximum amount of entanglement attainable. Both ways resemble the Procrustean method applied to a subset of the most significant Schmidt coefficients but obtaining non-maximally entangled states.Comment: 11 pages, 4 figure

Valoración naturalística del Corredor Verde del Río Guadiamar Andalucía, España)

En este trabajo aplicamos un método de valoración naturalística, concretamente el índice de interés para la conservación, a cada una de las unidades de un mapa de vegetación previamente realizado a escala 1:10.000. Para el cálculo de los distintos criterios de valoración se tienen en cuenta las comunidades vegetales (asociaciones) presentes en cada unidad, en vez de las especies como suele ser habitual. Las unidades del mapa están agrupadas en series de vegetación, por lo que también pueden extraerse conclusiones sobre el grado de conservación de cada una de ellas. Los resultados se plasman en un mapa de interés para la conservación. El escenario en el que se realizó el trabajo es el corredor verde del río Guadiamar, situado en el sur de España. La zona fue afectada por un vertido tóxico y lo que aquí mostramos son los resultados obtenidos tras una primera limpieza. Después se han iniciado trabajos de restauración, al cabo de los cuales podremos realizar un estudio comparativo con los resultados de este trabajo.In this paper we have applied a naturalistic evaluation strategy, more precisely, the conservation importance index, to each unit of a preplanned 1:10.000scale vegetation map. The estimation of the different evaluation criteria is based on the plant communities (associations) found in each unit rather than on species, as is usual. Since the map units are grouped in vegetation series, the conservation degree of each of them can also be assessed. The results are presented in a conservation interest map. The survey was undertaken in the Green Corridor of the Guadiamar River, in the south of Spain. The area was affected by a toxic spill and our data refl ect the results obtained after the fi rst stage of cleaning work. Subsequently, restoration work has been undertaken after which we will be able to carry out a comparative analysis with the results of this work.Junta de Andalucía. Consejería de Medio Ambiente

Desarrollo de adsorbente para captura de CO² a partir de un residuo agroindustrial

Se desarrollaron carbones activados (CAs), a partir de palos de yerba mate, abundante residuo agroindustrial, mediante el proceso de activación química empleando KOH como agente activante. Se llevó a cabo la caracterización química de los CAs obtenidos mediante análisis próximo y elemental. La caracterización textural se realizó mediante fisisorción de N2 a 77 K y CO2 a 273 K. Se obtuvieron imágenes de microscopía electrónica de barrido para estudiar la morfología del adsorbente. Se evaluó la capacidad de los CAs sintetizados para la remoción de CO2 de corrientes gaseosas, simulando condiciones de post-combustión, mediante ensayos gravimétricos de adsorción y análisis de curvas de ruptura determinadas en un adsorbedor de lecho fijo para distintos caudales y concentraciones de CO2. Los CAs obtenidos resultaron aptos para la remoción de CO2 de corrientes gaseosas vinculándose su alta efectividad a la acotada distribución de diámetros de microporos (< 2nm) que caracterizan sus estructuras porosas.Activated carbons (ACs) were developed from yerba mate twigs, an abundant agroindustrial residue, via the chemical activation process using KOH as activating agent. Chemical characterization of the resulting ACs was carried out by proximate and elemental analyses. Textural characterization was conducted through physisorption of N2 at 77K and CO2 at 273K. Scanning electronic microscopy images were obtained in order to examine the adsorbent’s morphology. The capacity of the developed ACs in CO2 removal from gaseous streams, mimicking post-combustion conditions, was evaluated through gravimetric assays and analysis of breakthrough curves determined in a fixed-bed adsorption unit for different gas flow rates and CO2 concentrations. The activated carbons obtained were found suitable for CO2 capture from gaseous effluents, their high effectiveness being related to the narrow distribution of micropores (< 2nm) characterizing their porous structures.Tema 9: Nuevas Tecnologías.Facultad de Arquitectura y Urbanism

Desarrollo de adsorbente para captura de CO² a partir de un residuo agroindustrial

Se desarrollaron carbones activados (CAs), a partir de palos de yerba mate, abundante residuo agroindustrial, mediante el proceso de activación química empleando KOH como agente activante. Se llevó a cabo la caracterización química de los CAs obtenidos mediante análisis próximo y elemental. La caracterización textural se realizó mediante fisisorción de N2 a 77 K y CO2 a 273 K. Se obtuvieron imágenes de microscopía electrónica de barrido para estudiar la morfología del adsorbente. Se evaluó la capacidad de los CAs sintetizados para la remoción de CO2 de corrientes gaseosas, simulando condiciones de post-combustión, mediante ensayos gravimétricos de adsorción y análisis de curvas de ruptura determinadas en un adsorbedor de lecho fijo para distintos caudales y concentraciones de CO2. Los CAs obtenidos resultaron aptos para la remoción de CO2 de corrientes gaseosas vinculándose su alta efectividad a la acotada distribución de diámetros de microporos (< 2nm) que caracterizan sus estructuras porosas.Activated carbons (ACs) were developed from yerba mate twigs, an abundant agroindustrial residue, via the chemical activation process using KOH as activating agent. Chemical characterization of the resulting ACs was carried out by proximate and elemental analyses. Textural characterization was conducted through physisorption of N2 at 77K and CO2 at 273K. Scanning electronic microscopy images were obtained in order to examine the adsorbent’s morphology. The capacity of the developed ACs in CO2 removal from gaseous streams, mimicking post-combustion conditions, was evaluated through gravimetric assays and analysis of breakthrough curves determined in a fixed-bed adsorption unit for different gas flow rates and CO2 concentrations. The activated carbons obtained were found suitable for CO2 capture from gaseous effluents, their high effectiveness being related to the narrow distribution of micropores (< 2nm) characterizing their porous structures.Tema 9: Nuevas Tecnologías.Facultad de Arquitectura y Urbanism

Desarrollo de adsorbente para captura de CO² a partir de un residuo agroindustrial

Se desarrollaron carbones activados (CAs), a partir de palos de yerba mate, abundante residuo agroindustrial, mediante el proceso de activación química empleando KOH como agente activante. Se llevó a cabo la caracterización química de los CAs obtenidos mediante análisis próximo y elemental. La caracterización textural se realizó mediante fisisorción de N2 a 77 K y CO2 a 273 K. Se obtuvieron imágenes de microscopía electrónica de barrido para estudiar la morfología del adsorbente. Se evaluó la capacidad de los CAs sintetizados para la remoción de CO2 de corrientes gaseosas, simulando condiciones de post-combustión, mediante ensayos gravimétricos de adsorción y análisis de curvas de ruptura determinadas en un adsorbedor de lecho fijo para distintos caudales y concentraciones de CO2. Los CAs obtenidos resultaron aptos para la remoción de CO2 de corrientes gaseosas vinculándose su alta efectividad a la acotada distribución de diámetros de microporos (< 2nm) que caracterizan sus estructuras porosas.Activated carbons (ACs) were developed from yerba mate twigs, an abundant agroindustrial residue, via the chemical activation process using KOH as activating agent. Chemical characterization of the resulting ACs was carried out by proximate and elemental analyses. Textural characterization was conducted through physisorption of N2 at 77K and CO2 at 273K. Scanning electronic microscopy images were obtained in order to examine the adsorbent’s morphology. The capacity of the developed ACs in CO2 removal from gaseous streams, mimicking post-combustion conditions, was evaluated through gravimetric assays and analysis of breakthrough curves determined in a fixed-bed adsorption unit for different gas flow rates and CO2 concentrations. The activated carbons obtained were found suitable for CO2 capture from gaseous effluents, their high effectiveness being related to the narrow distribution of micropores (< 2nm) characterizing their porous structures.Tema 9: Nuevas Tecnologías.Facultad de Arquitectura y Urbanism

Feeding entrainment of locomotor activity rhythms, digestive enzymes and neuroendocrine factors in goldfish

©2007. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Journal of PHYSIOL BEHAV. To access the final edited and published work see http://doi: 10.1016/j.physbeh.2006.10.017L.M. VERA, N. DE PEDRO, E. GÓMEZ-MILÁN, M.J. DELGADO, M.J. SÁNCHEZ MUROS, J.A. MADRID, F.J. SÁNCHEZ-VÁZQUEZ. Feeding entrainment of locomotor activity, digestive enzymes and neuroendocrine factors in goldfish. PHYSIOL BEHAV 90 (2-3) 518-524, 2007. The existence of food anticipatory activity (FAA) in animals subjected to daily feeding schedules seems to be mediated by a feeding-entrainable oscillator (FEO). Such an FEO may help in anticipating meal time and so optimizing food acquisition and nutrient utilization. In this study we investigated the existence of FAA and whether digestive enzymes, plasma cortisol, hypothalamic NPY and gastrointestinal tract (GIT) and plasma melatonin were entrained by periodic feeding in goldfish. We observed that periodically fed goldfish showed FAA in locomotor activity as well as in amylase and NPY. Alkaline protease and GIT melatonin were higher after feeding, whereas plasma cortisol levels were reduced. Plasma melatonin remained unmodified before and after meal time. These results suggested that scheduled feeding entrained both behavioral and certain physiological patterns in goldfish, FAA being of adaptive value to anticipate a meal and prepare the digestive physiology of fish

Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach

The deep lasso algorithm (dlasso) is introduced as a neural version of the statistical linear lasso algorithm that holds benefits from both methodologies: feature selection and automatic optimization of the parameters (including the regularization parameter). This last property makes dlasso particularly attractive for feature selection on small samples. In the two first conducted experiments, it was observed that dlasso is capable of obtaining better performance than its non-neuronal version (traditional lasso), in terms of predictive error and correct variable selection. Once that dlasso performance has been assessed, it is used to determine whether it is possible to predict the severity of symptoms in children with ADHD from four scales that measure family burden, family functioning, parental satisfaction, and parental mental health. Results show that dlasso is able to predict parents' assessment of the severity of their children's inattention from only seven items from the previous scales. These items are related to parents' satisfaction and degree of parental burden