Abstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

Abstract
The last few decades have witnessed a significant evolution of technology in different domains, changing the way the world operates, which leads to an overwhelming amount of data generated in an open-ended way as streams. Over the past years, we observed the development of several machine learning algorithms to process big data streams. However, the accuracy of these algorithms is very sensitive to their hyper-parameters, which requires expertise and extensive trials to tune. Another relevant aspect is the high-dimensionality of data, which can causes degradation to computational performance. To cope with these issues, this paper proposes a stream k-nearest neighbors (kNN) algorithm that applies an internal dimension reduction to the stream in order to reduce the resource usage and uses an automatic monitoring system that tunes dynamically the configuration of the kNN algorithm and the output dimension size with big data streams. Experiments over a wide range of datasets show that the predictive and computational performances of the kNN algorithm are improved.

Abstract
This paper reports on recent activities by the Portugal Chapter of IEEE Education Society. It begins with a reflection on the work of a Chapter and on several aspects that may impact its performance. The major activities are also presented, as well as the policies developed by the Portugal Chapter in the last year, concluding with the presentation of the two awards received at the last FIE, namely the Chapter Achievement Award and the Distinguished Chapter Leadership Award.

Abstract
The generalized Schrodinger-Newton system of equations with both local and nonlocal nonlinearities is widely used to describe light propagating in nonlinear media under the paraxial approximation. However, its use is not limited to optical systems and can be found to describe a plethora of different physical phenomena, for example, dark matter or alternative theories for gravity. Thus, the numerical solvers developed for studying light propagating under this model can be adapted to address these other phenomena. Indeed, in this work we report the development of a solver for the HiLight simulations platform based on GPGPU supercomputing and the required adaptations for this solver to be used to test the impact of new extensions of the Theory of General Relativity in the dynamics of the systems. In this work we shall analyze theories with nonminimal coupling between curvature and matter. This approach in the study of these new models offers a quick way to validate them since their analytical analysis is difficult. The simulation module, its performance, and some preliminary tests are presented in this paper.

Abstract
The study of coastal boulder accumulations generated by extreme marine events, and of the energy and frequency involved in boulder transport, is of paramount importance in understanding the risk associated with extreme marine inundations. One of the frequently asked questions is whether the deposits are storm or tsunami-related, both events being characterized by different return periods. Boulder transport by storms was monitored on the west coast of Portugal. Significant changes were detected in boulders' position as a result of extreme inundation by the 2013/2014 winter storms. Results presented in this work indicate that the wave power associated with the "Christina" and "Nadja" storms occur once every three years. However, this interval is not supported by field observations of boulder displacement, which suggests that wave power over-predicts boulder movement in the study area. Furthermore, wave parameters from the "Christina" and "Nadja" storms were very similar, but have generated different impacts in the boulder accumulation described herein. Differences include the magnitude and direction of boulder movement, and are most likely associated with distinct tidal levels during the events. Higher tide levels generated an increase in the sea surface level and thus in the reach of waves, which generated displacement of larger boulders and consequent cross-shore contribution in boulder transport. Regardless, the combination of monitoring campaigns, wave data, and statistical modelling of extreme values indicate that boulder transport by storms is more frequent than initially expected. Based on recorded boulder movements, we present a conceptual model for boulder ridge formation and development and identify significant control of incoming flow by local geomorphological/topographical features. Storm events, not less frequent tsunamis, are identified as the events responsible for modulating this rocky coastline. These results question a direct attribution of coastal boulder deposits to tsunamis in coastal regions with a high risk of tsunami inundation.

Abstract
Given the plethora of Reinforcement Learning algorithms available in the literature, it can prove challenging to decide on the most appropriate one to use in order to solve a given Reinforcement Learning task. This work presents a benchmark study on the performance of several Reinforcement Learning algorithms for discrete learning environments. The study includes several deep as well as non-deep learning algorithms, with special focus on the Deep Q-Network algorithm and its variants. Neural Fitted Q-Iteration, the predecessor of Deep Q-Network as well as Vanilla Policy Gradient and a planner were also included in this assessment in order to provide a wider range of comparison between different approaches and paradigms. Three learning environments were used in order to carry out the tests, including a 2D maze and two OpenAI Gym environments, namely a custom-built Foraging/Tagging environment and the CartPole environment.