Hyperparameter optimization for recommender systems through Bayesian optimization

AbstractRecommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search

AQP1 Is Not Only a Water Channel: It Contributes to Cell Migration through Lin7/Beta-Catenin

Background: AQP1 belongs to aquaporins family, water-specific, membrane-channel proteins expressed in diverse tissues. Recent papers showed that during angiogenesis, AQP1 is expressed preferentially by microvessels, favoring angiogenesis via the increase of permeability In particular, in AQP1 null mice, endothelial cell migration is impaired without altering their proliferation or adhesion. Therefore, AQP1 has been proposed as a novel promoter of tumor angiogenesis. Methods/Findings: Using targeted silencing of AQP1 gene expression, an impairment in the organization of F-actin and a reduced migration capacity was demonstrated in human endothelial and melanoma cell lines. Interestingly, we showed, for the first time, that AQP1 co-immunoprecipitated with Lin-7. Lin7-GFP experiments confirmed co-immunoprecipitation. In addition, the knock down of AQP1 decreased the level of expression of Lin-7 and b-catenin and the inhibition of proteasome contrasted partially such a decrease. Conclusions/Significance: All together, our findings show that AQP1 plays a role inside the cells through Lin-7/b-catenin interaction. Such a role of AQP1 is the same in human melanoma and endothelial cells, suggesting that AQP1 plays a global physiological role. A model is presented

A comprehensive view of intelligent transport systems for urban smart mobility

Information accuracy and decision-making speed are of paramount importance in managing today’s mobility of goods and people inside the city. Intelligent Transport Systems (ITS) can provide road users with updated information and forecasts on both traffic and weather conditions. The result is a higher efficiency in the use of resources and a better management of physical flows. The purpose of this paper is to conduct an in-depth review on the role of ITS supporting urban Smart Mobility, in order to identify the main gaps in the literature and propose future research streams. In total, 71 papers have been thoroughly analysed: they are mainly focused on technology, with limited attention to value creation. Even though some benefits have been examined, a general lack of quantitative models emerged. Eventually, there is a lack of contributions considering both people and freight transport, even if they are strongly related, especially in an urban environment