Testing BOI and BOB algorithms for solving the Winner Determination

Eighth International Conference on Hybrid Intelligent Systems, 2008. HIS '08. Barcelona, 10-12 September 2008Combinatorial auctions are a promising auction format for allocating radio spectrum, as well as other goods. An important handicap of combinatorial auctions is determining the winner bids among many options, that is, solving the winner determination problem (WDP). This paper tackles this computational problem using two approaches in a combinatorial first-price sealed bid auction. The first one, is an A* based on items (BOI). The second one, is an A* based on bids (BOB). These two techniques are tested in several scenarios for allocating radio spectrum licenses. The results obtained reveal that the search algorithm A* with the BOB formulation outperforms the other and always finds the optimal solution very quickly

Freshwater Fish Biodiversity in a Large Mediterranean Basin (Guadalquivir River, S Spain): Patterns, Threats, Status and Conservation

The Guadalquivir River Basin is one of the largest in the Iberian Peninsula and has a remarkable freshwater biodiversity. Although many studies on hydrological regimes or water quality have been conducted in this basin the biodiversity of freshwater fish, as well as their distribution and conservation status, has never been globally addressed as in other Iberian basins. In this context, we synthesized information on freshwater fish using field procedures and a bibliographic search. Fish distribution patterns at different spatial scales and general environmental conditions were analyzed as well as the conservation status of the fish community. We documented the presence of 40 species (20 native and 20 exotic) in the basin during the 20th century until today. However, we only captured 18 species during the field sampling, with a prevalence for any native species of less than 23% (except Luciobarbus sclateri). The highest species richness was found in mid reaches, while the lower reaches had very low diversity values. Around 50% of species are threatened; according to the IUCN, several species are declining at an alarming rate and others are probably extinct and/or their current status is unknown. Human disturbances during the last few decades have caused serious changes in fish distribution and consequently to their conservation status. Hydrological alterations, intensive agriculture and introduced species are probably the principal reasons for Guadalquivir’s ichthyofauna imperilment. Our study indicates an urgent and real need to identify important areas for fish conservation to guarantee a minimum fish biodiversity conservation over the long term, as well as effective strategies for fish recovery where it still is possibleThis research was funded by the Junta de Andalucía, Convocatoria de Proyectos de Excelencia (P07-RNM-03309), and was carried out at the Centro Internacional de Estudios y Convenciones Ecológicas y Medioambientales (CIECEM) of the University of Huelva. We wish to thank everyone from the CIECEM for their invaluable help and logistic suppor

Distribución de peces continentales en una cuenca mediterránea altamente perturbada: bases ecológicas para la gestión y la conservación

La cuenca del río Guadalquivir (S de España) es una de las mayores de la Península Ibérica y cuenta con una notable biodiversidad acuática. Aunque en esta cuenca se han realizado muchos estudios sobre el régimen hidrológico o sobre la calidad del agua, la biodiversidad de los peces continentales, así como su distribución y estado de conservación nunca se han abordado globalmente, como en otras cuencas ibéricas. En esta Tesis se sintetiza la información sobre los peces continentales a través de estudios de campo (285 localidades muestreadas) y otras fuentes de información (revisión bibliográfica, ciencia ciudadana, datos históricos). Examinamos los requisitos locales y regionales para estudiar las asociaciones de especies, así como la presencia de especies individuales a lo largo de toda esta cuenca altamente perturbada por efecto de acciones humanas. Se analizaron los patrones de distribución de los peces a diferentes escalas espaciales y temporales así como las condiciones ambientales generales y su estado de conservación. Documentamos la presencia de 40 especies (20 nativas y 20 exóticas) en la cuenca durante el siglo XX hasta la actualidad. Pero solo capturamos 18 especies durante el trabajo de campo, con una prevalencia de cualquier especie nativa inferior al 23% (excepto Luciobarbus sclateri). Destaca la presentación de nuevos datos de distribución de cuatro especies introducidas recientemente en esta cuenca: chanchito (Australoheros facetus Jenyns, 1842), pez gato negro (Ameiurus melas Rafinesque, 1820), siluro (Silurus glanis Linnaeus, 1758) y una especie de piscaro de origen desconocido (Phoxinus spp.). Se compilan otros registros para actualizar el rango de distribución de estas especies. La información obtenida refuerza la evidencia sobre el establecimiento y expansión de estas especies no nativas. La mayor riqueza de especies se encontró en los tramos medios, mientras que los tramos inferiores tuvieron valores de diversidad muy bajos. Alrededor del 50% de las especies están amenazadas, según la UICN, varias especies están disminuyendo a un ritmo alarmante y otras probablemente estén extintas y/o se desconoce su estado actual. Las perturbaciones humanas durante las últimas décadas han provocado cambios importantes en la distribución de los peces y, en consecuencia, en su estado de conservación. Las alteraciones hidrológicas, la agricultura intensiva y las especies introducidas son probablemente las principales amenazas para la ictiofauna del Guadalquivir. Este estudio señala la necesidad urgente y real de identificar áreas importantes para la conservación de los peces que garanticen una conservación mínima de la biodiversidad íctica a largo plazo, así como de estrategias efectivas para la recuperación de peces donde aún sea posible. También recomendamos nuevos muestreos de campo para identificar las vías de dispersión de las especies exóticas, especialmente de aquellas introducidas recientemente, y para aclarar su estado actual. Las fuentes históricas de datos se mostraron como una herramienta valiosa para el estudio de los cambios a largo plazo en las comunidades de peces. El análisis comparativo de la ictiofauna del Guadalquivir entre el s.XIX y XXI reveló un proceso asimétrico en la extinción de especies nativas y colonización de exóticas, vinculado principalmente a la historia natural de las especies y a la marcada asimetría ambiental y de perturbaciones existente en la cuenca. Las relaciones peces-hábitat son un factor clave para el diseño de estrategias de conservación y manejo fecundas, especialmente en áreas altamente perturbadas donde las comunidades de peces están sujetas a muchas presiones humanas. En este sentido, los estudios multi-escala ayudan a mejorar el conocimiento de los componentes espaciales e identifican variables clave locales (p. ej., anchura del cauce) y regionales (p. ej., altitud) en la distribución de las especies. En esta Tesis se consideraron quince variables ambientales a escala local y veinte a nivel regional. Para el análisis espacial se utilizó un total de 18 especies capturadas durante el muestreo de campo. La prevalencia global de especies introducidas fue del 25%, lo que puede considerarse un valor alto. Las especies introducidas más extendidas fueron la gambusia (Gambusia holbrooki) y el pez sol (Lepomis gibbosus), con una prevalencia en torno al 10%. Las escalas espaciales regional y local mostraron diferente relevancia según el nivel de enfoque del estudio (comunidad o especie). A nivel de comunidad, los componentes locales, regionales y compartidos revelaron una influencia similar sobre el conjunto de los peces, mientras que a nivel de especies individuales, el componente local fue el factor principal que explicó la presencia de la mayoría de las especies. Además, la interacción entre las variables fue escasamente seleccionada y casi ninguna distribución de las especies introducidas se vio afectada por la interacción con variable alguna. Nuestros resultados destacan el mal estado de conservación de la fauna autóctona de peces de la cuenca del río Guadalquivir, así como la importancia de analizar las relaciones peces-hábitat a diferentes escalas y enfoques. Estos resultados proporcionan información útil para evaluar y diseñar estrategias de conservación en cuencas de tipo mediterráneo.The Guadalquivir River Basin (S Spain) is one of the largest in the Iberian Peninsula and has a remarkable freshwater biodiversity. Although many studies on hydrological regimes or water quality have been conducted in this basin, the biodiversity of freshwater fish as well as their distribution and conservation status have never been globally addressed, as in other Iberian basins. In this context, we synthesized information on freshwater fish using field procedures (285 sampled sites) and other sources of information (bibliographic review, citizen science, historical data). We examined local and regional requirements to study freshwater fish assemblage and occurrence in this highly disturbed basin. Fish distribution patterns at different spatial and temporal scale were analysed, as well as general environmental conditions and the conservation status of the fish community. We documented the presence of 40 species (20 native and 20 exotic) in the basin during the 20th century until today. But we only captured 18 species during the field sampling, with the prevalence for any native species less than 23% (except Luciobarbus sclateri). We report new distribution data on four recently introduced species in this basin: chameleon cichlid (Australoheros facetus Jenyns, 1842), North American black bullhead (Ameiurus melas Rafinesque, 1820), European catfish (Silurus glanis Linnaeus, 1758) and a minnow species of unknown origin (Phoxinus spp.). A compilation of records is used to update the distribution range of these species. The information collected reinforces the evidence on the establishment and expansion of these non-native species. The highest species richness was found in mid reaches, while the lower reaches had very low diversity values. Around 50% of species are threatened, according to the IUCN, several species are declining at an alarming rate and others are probably extinct and/ or their current status is unknown. Human disturbances during the last few decades have caused serious changes in fish distribution and consequently in their conservation status. Hydrological alterations, intensive agriculture and introduced species are probably the principal reasons for the Guadalquivir’s ichthyofauna imperilment. Our study indicates an urgent and real need to identify important areas for fish conservation to guarantee a minimum fish biodiversity conservation over the long term, as well as effective strategies for fish recovery where it still is possible. We also recommend new field sampling to identify the dispersal pathways of the exotic species, especially those recently introduced, and to clarify their current statuses. Historical data sources proved to be a valuable tool for studying long-term changes in fish communities. The comparative analysis of the fish fauna of the Guadalquivir basin between the 19th and 21st centuries revealed an asymmetric process in the extinction of native species and colonization of exotic ones, mainly linked to the natural history of the species and the marked environmental asymmetry and disturbances in the basin. Fish–habitat relationships are a key element for conservation and management strategies, especially in highly disturbed areas where fish communities are subjected to many human pressures. In this regard, multiscale studies help to improve the knowledge of the spatial components and identify local (e.g. water width) and regional (e.g. elevation) key variables in species distribution. Fifteen environmental variables were considered at local scale and twenty at regional level. A total of 18 species captured during field sampling were used for spatial analysis. The global prevalence for introduced species was 25%, which can be considered a high value. The most extended introduced species were eastern mosquitofish (Gambusia holbrooki) and pumpkinseed (Lepomis gibbosus), with around 10% prevalence. Regional and local scales showed different relevance according to the level-study approach (community or species). At the community level, the local, regional and shared components revealed similar influence on the fish assemblage, while at individual species level the local component was the main factor to explain most of fish occurrences. Moreover, variables’ interaction was scarcely selected and almost no introduced species distribution was affected by the interaction of any variable. Our results highlight the poor conservation status of the native fish fauna of the Guadalquivir River Basin as well as the importance of analyzing fish–habitat relationships at different scales and approaches. These results provide useful information to assess and design conservation strategies in Mediterranean-type basin

Las instituciones científico-medicas en la Murcia del XVIII : un intento fracasado de renovación de la formación médica

Presentamos un estudio sobre dos intentos que, en la Murcia del siglo XVIII, pretendieron mejorar y actualizar la formación que recibían los profesionales sanitarios. Ambos fracasaron, aparentemente por falta de acuerdo entre sus promotores, si bien pensamos que las causas fueron más profundas y habría que buscarlas en la escasa actividad científica que se desarrolló en la ciudad, lo que hacía innecesaria la existencia de instituciones docentes o de otras como las Academias de Medicina

Reseñas

Josep BERNABEU MESTRE, Enfermedad y población. Introducción a los problemas y métodos de la epidemiología históric

Reseñas

ACTAS II Congreso de la Asociación de Demografía Histórica (1991), Beitia ediciones de Histori

A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called "deep learning", which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google's TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset.This project was partially funded by the European Union’s CIP (Competitiveness and Innovation Framework Programme) (ICT-PSP-2012) under Grant Agreement No. 325146 (Social Ecosystem for Antiaging, Capacitation and Wellbeing—SEACW project). It is also supported by the Spanish Ministry of Education, Culture and Sport through the FPU (University Faculty Training) fellowship (FPU13/03917).Publicad

A survey of handwritten character recognition with MNIST and EMNIST

This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning.This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST's. In this paper, EMNIST is explained and some results are surveyed

Feature set optimization for physical activity recognition using genetic algorithms

Proceeding of: Genetic and Evolutionary Computation Conference (GECCO 2015)Physical activity is recognized as one of the key factors for a healthy life due to its beneficial effects. The range of physical activities is very broad, and not all of them require the same effort to be performed nor have the same effects on health. For this reason, automatically recognizing the physical activity performed by a user (or patient) turns out to be an interesting research field, mainly because of two reasons: (1) it increases personal awareness about the activity being performed and its consequences on health, allowing to receive proper credit (e.g. social recognition) for the effort; and (2) it allows doctors to perform continuous remote patient monitoring. This paper proposes a new approach for improving activity recognition by describing an activity recognition chain (ARC) that is optimized by means of genetic algorithms.This optimization process determines the most suitable and informative set of features that turns out into higher recognition accuracy while reducing the total number of sensors required to track the user activity. These improvements can be translated into lower costs in hardware and less intrusive devices for the patients. In this work, for the assessment of the proposed approach versus other techniques and for replication purposes, a publicly available dataset on physical activity (PAMAP2) has been used. Experiments are designed and conducted to evaluate the proposed ARC by using leave-one-subject-out cross validation and results are encouraging, reaching an average classification accuracy of about 94%.This research was partially funded by European Union’s CIP Programme (ICT-PSP-2012) under grant agreement no. 325146 (SEACW project)

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.This research is partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917