Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.

In this paper, we propose a novel concept, "Emotionally Grounded Symbol Acquisition", which is an extended idea of well-studied concept, "Physically Grounded Symbol Acquisition". We think that it is important for an autonomous robot to ground symbols not only on the physical world but also on its emotions. Based on this concept, we propose an Emotionally Grounded Architecture for behavior control, which enables an autonomous robot such as pet-type robot to respond properly to external stimuli based on its own emotional evaluation.

In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time.

O câncer de rim representa 3% das doenças malignas do adulto, sendo o diagnóstico precoce uma ferramenta essencial. A segmentação automática tem como objetivo auxiliar o médico no processo de diagnóstico. O objetivo desse trabalho consiste em desenvolver e avaliação de uma rede neural para segmentar as imagens de tomografia computadorizada, das regiões dos rins e tumor, caso existam. Avaliamos a utilização de Pyramid Pooling Module em substituição de camadas convolucionais da rede U-Net para atingir o objetivo. A metodologia proposta atinge como resultado 0,91 de Iou para regiões do rim e 0,88 de Iou para regiões de tumor.