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Oxygen vacancies (OV) are pervasive in metal oxides and play a pivotal role in the switching behaviour of oxide-based memristive devices. In this work we address, through a combination of experiments and theoretical simulations, OV dynamics in $Pt/TaO_{2-h}/Ta_{2}O_{5-x}/TaO_${2-y}$/Pt$ devices. In particular, we focus on the RESET transition (from low to high resistance), induced by the application of electrical pulse(s), by choosing different initial OV profiles and studying their kinetics during the mentioned process. We demonstrate that by selecting specific OV profiles it is possible to tune the characteristic time-scale of the RESET. Finally, we show that the implementation of gradual RESETs, induced by applying many (small) successive pulses, allows estimating the activation energies involved in the OV electromigration process. Our results help paving the way for OV engineering aiming at optimizing key memristive figures such as switching speed or power consumption, which are highly relevant for neuromorphic or in-memory computing implementations., Comment: preprint 19 pages, 6 figures. Supp. Material under request

Cherenkov-type particle detectors or scintillators use as a fundamental element photomultiplier tubes, whose efficiency decreases when subjected to the Earth's magnetic field. This work develops a geomagnetic field compensation system based on coils for large scale cylindrical detectors. The effect of different parameters such as the size of the detector, the distance between coils or the magnetic field strength on the compensation using a basic coil system composed of circular and rectangular coils is studied. The addition of coils of very specific geometry and position to the basic configuration is proposed in order to address the compensation in the areas of the detector where it is more difficult to influence, in order to minimize the loss of efficiency. With such improvement, in the considered simulated system, more than 99.5% of the photomultiplier tubes in the detector experience an efficiency loss of less than 1% due to the effect of the magnetic fields., Comment: 22 pages, 31 figures

Objectives: This study analyzed adolescent physical activity, its link to overweight, and the social network structure in group sports participants, focusing on centrality measures. Setting: Conducted in 11 classrooms across 5 schools in Ponferrada, Spain. Participants: Included 235 adolescents (49.4% female), categorized as normal weight or overweight. Methods: The Physical Activity Questionnaire for Adolescents (PAQ-A) assessed physical activity levels. Social network analysis evaluated centrality in varying contact degrees. Results: 30.2% were overweight. Males scored higher in PAQ-A and were more likely to engage in group sports. No significant correlation was found between physical activity and weight in the total sample. However, overweight females reported higher exercise levels. Centrality analysis showed gender differences; women in group sports had lower centrality, whereas men had higher. Conclusions: The study highlights the importance of gender and social network centrality in designing future strategies, considering peer interaction intensity

Recent advances in image, video, text and audio generative techniques, and their use by the general public, are leading to new forms of content generation. Usually, each modality was approached separately, which poses limitations. The automatic sound recording of visual sequences is one of the greatest challenges for the automatic generation of multimodal content. We present a processing flow that, starting from images extracted from videos, is able to sound them. We work with pre-trained models that employ complex encoders, contrastive learning, and multiple modalities, allowing complex representations of the sequences for their sonorization. The proposed scheme proposes different possibilities for audio mapping and text guidance. We evaluated the scheme on a dataset of frames extracted from a commercial video game and sounds extracted from the Freesound platform. Subjective tests have evidenced that the proposed scheme is able to generate and assign audios automatically and conveniently to images. Moreover, it adapts well to user preferences, and the proposed objective metrics show a high correlation with the subjective ratings., Comment: 10 pages, in Spanish, Tecniac\'ustica

The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial neural network model that is able to detect these transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model, which was later tested, obtaining an accuracy of 99.02 % and an estimated error (loss function) of 0.03. These results, among others, helped to confirm that the 1D CNN is a good choice for working with non-phased-folded Mandel and Agol light curves with transits. It also reduces the number of light curves that have to be visually inspected to decide if they present transit-like signals and decreases the time needed for analyzing each (with respect to traditional analysis).

Fast quantum gates are of paramount importance for enabling efficient and error-resilient quantum computations. In the present work we analyze Landau-Zener-St\"uckelberg-Majorana (LSZM) strong driving protocols, tailored to implement fast gates with particular emphasis on small gap qubits. We derive analytical equations to determine the specific set of driving parameters for the implementation of single qubit and two qubit gates employing single period sinusoidal pulses. Our approach circumvents the need to scan experimentally a wide range of parameters and instead it allows to focus in fine-tuning the device near the analytically predicted values. We analyze the dependence of relaxation and decoherence on the amplitude and frequency of the pulses, obtaining the optimal regime of driving parameters to mitigate the effects of the environment. Our results focus on the study of the single qubit $X_{\frac{\pi}{2}}$, $Y_{\frac{\pi}{2}}$ and identity gates. Also, we propose the $\sqrt{\rm{bSWAP}}$ as the simplest two-qubit gate attainable through a robust LZSM driving protocol.

Neuromorphic computing aims to emulate the architecture and information processing mechanisms of the mammalian brain. This includes the implementation by hardware of neural networks. Oxide-based memristor arrays with cross-bar architecture appear as a possible physical implementation of neural networks.In this paper, we obtain experimental potentiation-depression (P-D) curves on different manganite-based memristive systems and simulate the learning process of perceptrons for character recognition. We analyze how the specific characteristics of the P-D curves affect the convergence time -- characterized by the EPOCHs-to-convergence (ETC) parameter -- of the network. Our work shows that ETC is reduced for systems displaying P-D curves with relatively low granularity and non-linear and asymmetric response. In addition, we also show that noise injection during the synaptic weight actualization further reduces the ETC. The results obtained here are expected to contribute to the optimization of hardware neural networks based on memristors cross-bar arrays., Comment: 10 pages published version