The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.

The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez, Cano, and Garcia-Rodriguez, 2014).The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method outperforms classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 18946/JLI/13. This work has been funded by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF)

During the last few years, Convolutional Neural Networks are slowly but surely becoming the default method solve many computer vision related problems. This is mainly due to the continuous success that they have achieved when applied to certain tasks such as image, speech, or object recognition.

The magnetar Swift J1818.0–1607 was discovered in 2020 March when Swift detected a 9 ms hard X-ray burst and a long-lived outburst. Prompt X-ray observations revealed a spin period of 1.36 s, soon confirmed by the discovery of radio pulsations. We report here on the analysis of the Swift burst and follow-up X-ray and radio observations. The burst average luminosity was L _burst ∼ 2 × 10³⁹ erg s⁻¹ (at 4.8 kpc). Simultaneous observations with XMM-Newton and NuSTAR three days after the burst provided a source spectrum well fit by an absorbed blackbody ( = (1.13 ± 0.03) × 10²³ cm⁻² and kT = 1.16 ± 0.03 keV) plus a power law (Γ = 0.0 ± 1.3) in the 1–20 keV band, with a luminosity of ∼8 × 10³⁴ erg s⁻¹, dominated by the blackbody emission. From our timing analysis, we derive a dipolar magnetic field B ∼ 7 × 10¹⁴ G, spin-down luminosity erg s⁻¹, and characteristic age of 240 yr, the shortest currently known. Archival observations led to an upper limit on the quiescent luminosity <5.5 × 10³³ erg s⁻¹, lower than the value expected from magnetar cooling models at the source characteristic age. A 1 hr radio observation with the Sardinia Radio Telescope taken about 1 week after the X-ray burst detected a number of strong and short radio pulses at 1.5 GHz, in addition to regular pulsed emission; they were emitted at an average rate 0.9 min⁻¹ and accounted for ∼50% of the total pulsed radio fluence. We conclude that Swift J1818.0–1607 is a peculiar magnetar belonging to the small, diverse group of young neutron stars with properties straddling those of rotationally and magnetically powered pulsars. Future observations will make a better estimation of the age possible by measuring the spin-down rate in quiescence.