Multi-coloured jigsaw percolation on random graphs

The jigsaw percolation process, introduced by Brummitt, Chatterjee, Dey and Sivakoff, was inspired by a group of people collectively solving a puzzle. It can also be seen as a measure of whether two graphs on a common vertex set are "jointly connected". In this paper we consider the natural generalisation of this process to an arbitrary number of graphs on the same vertex set. We prove that if these graphs are random, then the jigsaw percolation process exhibits a phase transition in terms of the product of the edge probabilities. This generalises a result of Bollob\'as, Riordan, Slivken and Smith.Comment: 13 page

Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets

The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness

Germination and Seedlings Heterotrophic Growth of Cocksfoot (\u3cem\u3eDactylis glomerata\u3c/em\u3e L.) in Response to Temperature

In the context of climate change, grasslands are considered, similar to forest, as an important sink for atmospheric CO2. However, environmental change seems to go faster than species adaptation to survive on site. Germination and heterotrophic growth are key phases for plant, and consequently, communities’ establishment and structure. They are under genetic control and affected by temperature. The objective of this study was to analyze the intra-specific variability of six accessions of Dactylis glomerata in their responses to eight constant temperatures (5 to 40˚C) during germination and initial heterotrophic growth. The novelty of this work comes from the non-linear modeling of germination and growth velocities and the estimation of cardinal temperatures. High variability within temperatures and significant differences between accessions were observed for germination speed. No germination was observed at 40˚C for any accession. Further, seed germinated at 25˚C died soon after they were transferred to 40˚C for heterotrophic growth. The growth of the axes, whenever it existed, was negligible at 40˚C. The speed of heterotrophic growth of the roots and shoots showed asymmetric bell-shaped response curves to temperature. Base temperature for germination was fixed to 0˚C. After curve-fitting, optimum temperatures for germination were estimated to be between 21.5 and 26.3˚C. Those for heterotrophic growth were, up to 5˚C, higher. Contrariwise, the upper limits, for both processes, appeared between 34 and 40˚C. Overall, this study demonstrates that genetic variability does exist between accessions. For each accession, the response of the germination rate was different from the response of heterotrophic growth rate

Leaf Length Variation in Perennial Forage Grasses

Leaf length is a key factor in the economic value of different grass species and cultivars in forage production. It is also important for the survival of individual plants within a sward. The objective of this paper is to discuss the basis of within-species variation in leaf length. Selection for leaf length has been highly efficient, with moderate to high narrow sense heritability. Nevertheless, the genetic regulation of leaf length is complex because it involves many genes with small individual effects. This could explain the low stability of QTL found in different studies. Leaf length has a strong response to environmental conditions. However, when significant genotype × environment interactionshave been identified, their effects have been smaller than the main effects. Recent modelling-based research suggests that many of the reported environmental effects on leaf length and genotype × environment interactions could be biased. Indeed, it has been shown that leaf length is an emergent property strongly affected by the architectural state of the plant during significant periods prior to leaf emergence. This approach could lead to improved understanding of the factors affecting leaf length, as well as better estimates of the main genetic effects

Construcción social del riesgo de desastre, colonia Puerto Príncipe, Nueva Guinea, RACCS, 2013

In this investigation the process of social construction of the disaster risk in floods situations caused by intense rains in Puerto Príncipe Community was analyzed. It inquired about the knowledge that the population has about the subject of flood disasters and the ways in which the community perceives the socio-natural threats. It was reviewed the individual and collective actions that contribute to the development of social adaptation strategies to risk, the forms of response to risk in practice or in their imagination and the role of social organizations in the definition and implementation of community mechanisms of adaptation to floods. The results of the study showed the existence of different contexts of vulnerability that increase the risk of disaster in Puerto Príncipe Community.En esta investigación se analizó el proceso de construcción social del riesgo de desastre ante inundaciones provocadas por lluvias intensas en la colonia Puerto Príncipe. Se indagó acerca del conocimiento que posee la población sobre el tema de desastre por inundaciones y las formas en las cuales la comunidad percibe las amenazas socio-naturales. Se revisaron las acciones individuales y colectivas que contribuyen al desarrollo de estrategias de adaptación social al riesgo, las formas de respuesta al riesgo en la práctica o en su imaginación y el rol de las organizaciones sociales en la definición e implementación de mecanismos comunitarios de adaptación a las inundaciones. Los resultados del estudio evidenciaron la existencia de diferentes contextos de vulnerabilidad que incrementan el riesgo de desastre en la colonia Puerto Príncipe

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups

Efectividad de la experimentación como estrategia didáctica en la asignatura de Física en el contenido de la dilatación de los cuerpos sólidos, permite aprendizaje significativo en los discentes del onceavo grado del Colegio Maestro Calixto Moya, del municipio de Masatepe, Masaya, durante el año lectivo 2015

Este trabajo de investigación acción sobre la efectividad de la experimentación aplicada como estrategia didáctica en la asignatura de Física en el contenido de la dilatación de los cuerpos sólidos se llevó a efecto en el onceavo grado del Colegio Maestro Calixto Moya de la ciudad de Masatepe-Masaya, con el objeto de estudio de desarrollar en los estudiantes una serie de competencias vinculadas con la experimentación y el trabajo investigativo; que los lleven a explicar el por qué y el cómo ocurren los fenómenos físicos de su entorno y así despertar la curiosidad y creatividad en los discentes , lograr aprendizajes significativos y permanentes; con el objetivo de evaluar la efectividad de los experimentos aplicada como estrategia didáctica para relacionar la teoría con la práctica en el contenido de la dilatación de los cuerpos sólidos. El procedimiento metodológico de esta investigación constó de tres fases: diagnóstica (test aplicado a los estudiantes y entrevista al docente), tratamiento de la información (montaje de tres experimentos) y evaluación (resultado de los experimentos y prueba escrita posterior a la realización de éstos). En cada fase se recopiló la información obtenida y siguiendo los pasos del método científico; los datos se organizaron, se procesaron y se analizaron de forma descriptiva y cuantitativa. De una población de 92 estudiantes de tres secciones diferentes se seleccionó una con 32 discentes. En los resultados del diagnóstico el 100% de los discentes tenía la idea clara de lo que era temperatura y calor, el 28% tenían una idea definida de lo que era dilatación de los cuerpos en Física, un 34% sabían el factor del porqué los cuerpos se dilatan, un 47% conocían los tipos de dilataciones, un 75% manifestaron que el docente imparte su clase en forma de conferencia y el 100% de los discentes manifestaron que ningún experimento habían realizado sobre la dilatación de los cuerpos. Finalmente se concluye: que los experimentos tienen una gran efectividad en el proceso de enseñanza-aprendizaje, el modelo epistemológico aplicado por el docente de la disciplina de Física tiende a ser de enfoque teorisista-conductista mediante el cual el estudiante aprende a través de las acciones planeadas y ejecutadas por el docente, el docente no vincula la teoría con la práctica experimental para generar aprendizajes útiles para la vida. El docente no lleva a los estudiantes a explicar el por qué y él cómo ocurren los fenómenos de las dilataciones de los cuerpos u otros fenómenos físicos mediante actividades prácticas de laboratorio, la cantidad de horas clases estipuladas en el programa de estudio es suficiente para realizar las actividades experimentales de la dilatación de los cuerpos sólidos y se comprobó que con las prácticas de laboratorio se logra un aprendizaje significativo

Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: “probably you will like this film”, “almost certainly you will like this song”, etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields

Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems

Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided