Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. The existing spatiotemporal correlation as well as the diversity of influential factors, pose important challenges in accurately modeling these trends.

An analysis of twitter as a relevant human mobility proxy: A comparative approach in spain during the COVID-19 pandemic.

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents.

Early Detection of Alzheimer's Disease: Detecting Asymmetries with a Return Random Walk Link Predictor.

Alzheimer's disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage.