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Using a weightless neural network architecture WiSARD we propose a straightforward graph to retina codification to represent business process graph flows avoiding kernels, and we present how WiSARD outperforms the classification performance with small training sets in the process mining context.

This study proposes the usage of a method called VoteRank, created by Zhang et al. (2016), to identify influential nodes on patent citation networks. In addition, it proposes enhanced VoteRank algorithms, extending the Zhang et al. work. These novel algorithms comprise a reduction on the voting ability of the nodes affected by a chosen spreader if the nodes are distant from the spreader. One method uses a reduction factor that is linear regarding the distance from the spreader, which we called VoteRank-LRed. The other method uses a reduction factor that is exponential concerning the distance from the spreader, which we called VoteRank-XRed. By applying the methods to a citation network, we were able to demonstrate that VoteRank-LRed improved performance in choosing influence spreaders more efficiently than the original VoteRank on the tested citation network., Comment: 10 pages, 3 figures

In recent years, studies on public transport networks have intensified in the Social Networks field, especially in bus networks. This has occurred because of the relevance of urban mobility for the proper functioning of a city. The Rio de Janeiro city, Brazil, has undergone recent changes in the city of Rio de Janeiro, Brazil, has undergone recent changes in its municipal bus system, modifying several lines and bus stops. This paper proposes to analyze the structure of the bus transportation network of this city, comparing its topology in 2014 and 2016, before and after the change. For this, the properties of the bus system were investigated based on the topological models B\-space, P\-space, and C\-space. Some essential parameters were calculated, such as the giant component, distance, diameter, degree, closeness, and betweenness. The results showed that there was a reduction of 22.75\% of the lines and 5.19\% of the bus stops from 2014 to 2016. It was also verified that a maximum of four lines is required to move between any two bus stops within the city in both years. However, with three lines is possible to reach more than 99\% of the bus stops. Besides, this study also suggests exploring the C\-space network according to a minimum number of frequent bus stops that the lines had. Based on the component giant analysis of these networks with many common points, it is possible to detect possible expressway corridors., Comment: 22 pages, 15 figures

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging.

A new approximating technique is developed so as to study the quantum ferromagnetic spin-1 Blume-Capel model in the presence of a transverse crystal field in the square lattice. Our proposal consists of approaching the spin system by considering islands of finite clusters whose frontiers are surrounded by non-interacting spins that are treated by the effective-field theory. The resulting phase diagram is qualitatively correct, in contrast to most effective-field treatments, in which the first-order line exhibits spurious behavior by not being perpendicular to the anisotropy axis at low temperatures. The effect of the transverse anisotropy is also verified by the presence of quantum phase transitions. The possibility of using larger sizes constitutes an advantage to other approaches where the implementation of larger sizes is costly computationally., Comment: arXiv admin note: substantial text overlap with arXiv:1607.01270

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data, and computational methods, giving particular attention to an increased usage of machine learning methods. Besides, we also present a timeline of significant works that depicts relationships between flight delay prediction problems and research trends to address them. The published version of this paper is made available at \url{https://doi.org/10.1080/01441647.2020.1861123}. Please cite as: L. Carvalho, A. Sternberg, L. Maia Gon\c{c}alves, A. Beatriz Cruz, J.A. Soares, D. Brand\~ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of data science for flight delay research: a systematic review, Transport Reviews

A variational approach based on Bogoliubov inequality for the free energy is employed in order to treat the quantum spin-$1$ anisotropic ferromagnetic Heisenberg model in the presence of a crystal field. Within the Bogoliubov scheme an improved pair approximation has been used. The temperature dependent thermodynamic functions have been obtained and provide much better results than the previous simple mean-field scheme. In one dimension, which is still non-integrable for quantum spin-$1$, we get the exact results in the classical limit, or near-exact results in the quantum case, for the free energy, magnetization and quadrupole moment, as well for the transition temperature. In two and three dimensions the corresponding global phase diagrams have been obtained as a function of the parameters of the Hamiltonian. First-order transition lines, second-order transition lines, tricritical and tetracritical points, and critical endpoints have been located through the analysis of the minimum of the Helmholtz free energy and a Landau like expansion in the approximated free energy. Only first-order quantum transitions have been found at zero temperature. Limiting cases, such as isotropic Heisenberg, Blume-Capel and Ising models have been analyzed and compared to previous results obtained from other analytical approaches as well as from Monte Carlo simulations.