In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Climate change has had strong impacts on biodiversity, including well-documented shifts in the distributions and phenology of species. Reductions in body size represent a third pervasive biological response; it has garnered significantly less attention despite great ecological relevance. Theoretical frameworks-the temperature-size rule, Bergmann's rule, and James's rule-predict that warmer temperatures are associated with smaller body sizes in ectotherms. In contrast, empirical evidence concerning this pattern is mixed across both taxa and environments. In the present study, we applied computer vision techniques to historical data from two large butterfly museum collections, totaling 593 species across 10 terrestrial biomes over more than a century, in order to investigate long-term trends in butterfly body size. We measured forewing length through both manual image analyses and automated computer vision algorithms proxying body size and analyzed trends by using generalized additive models in order to consider a temporal, biome-specific pattern assessment. We have tested two hypotheses: that butterfly body size (1) declines over time, in conjunction with increasing ambient temperatures, in agreement with the temperature-size rule, and (2) its variation is more marked in warm, dry biomes. Results indicate a significant overall reduction in the body size of butterflies during the last century and that this reduction is indeed more pronounced in those biomes facing higher rises in temperature. These findings constitute large-scale evidence in support of the temperature-size rule and indicate a potential ecological impact of climate change on butterfly populations.