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Flexoelectricity is a property of all dielectric materials, where inhomogeneous strain induces electrical polarization. This effect becomes particularly prominent at the nanoscale where larger strain gradients can be obtained. While flexoelectric charges have been measured in mm-scale systems, direct measurements in nanoscale-thickness materials have not yet been achieved. Given that one of the most prominent applications of flexoelectricity is in nano-electro-mechanical systems (NEMS), confirming the presence and magnitude of the effect at these scales is essential. This study presents the first-ever measurements of flexoelectric-generated charges (direct effect) in nanoscale-thickness materials, using cantilevers with a 50 nm hafnium oxide layer. We confirm that the estimated flexoelectric coefficient from said measurements aligns with the values obtained from complementary experiments using the flexoelectric inverse effect. Additionally, by changing the cantilever geometry (modifying the width of the cantilevers), we demonstrate a 40% increase in the effective flexoelectric coefficient, explained by the interplay of different flexoelectric tensor components. These findings not only validate the presence of flexoelectric effects at the nanoscale but also open the possibility for full flexoelectric transduction of the motion in NEMS/MEMS devices.

Flexoelectricity is universal in all dielectrics, effective at high temperatures, and a promising transduction technique for nanoelectromechanical systems (NEMS). However, as flexoelectricity is still in its early stages, many aspects require further investigation. Understanding how flexoelectricity depends on material parameters like crystallographic phase and how temperature might affect it, is important for selecting and optimizing the right material for technological applications. This work studies the influence of high-temperature annealing (and the consequent crystallization) in the flexoelectricity of hafnium oxide (HfO2), a material with significant technological relevance. We measure the flexoelectric coefficient for amorphous (not annealed) and annealed (slightly crystalline) phases of HfO2, with samples annealed in nitrogen or oxygen atmospheres. Our results indicate that the amorphous phase of HfO2 exhibits the highest flexoelectric coefficient (105 $\pm$ 10 pC/m), while annealed samples show a significant decrease, with the lowest value in nitrogen-annealed samples (26 $\pm$ 4 pC/m). Samples annealed in an oxygen atmosphere improve flexoelectric properties (54 $\pm$ 6 pC/m) compared to those annealed in nitrogen. Using cross-sectional imaging, X-ray diffraction, resonance frequency characterization, and relative permittivity measurements, we find that annealing promotes crystallization into the tetragonal phase and increases internal stress within the HfO2 layer, while most other parameters remain constant. We attribute the differences in flexoelectricity from the annealed samples to the quantity of oxygen vacancies in hafnium oxide. These oxygen vacancies in hafnium oxide seem to negatively affect the flexoelectric coefficient. This finding can be applied to optimize materials to enhance their flexoelectric properties.

A fundamental issue about installation of photovoltaic solar power stations is the optimization of the energy generation and the fault detection, for which different techniques and methodologies have already been developed considering meteorological conditions. This fact implies the use of unstable and difficult predictable variables which may give rise to a possible problem for the plausibility of the proposed techniques and methodologies in particular conditions. In this line, our goal is to provide a decision support system for photovoltaic fault detection avoiding meteorological conditions. This paper has developed a mathematical mechanism based on fuzzy sets in order to optimize the energy production in the photovoltaic facilities, detecting anomalous behaviors in the energy generated by the facilities over time. Specifically, the incorrect and correct behaviors of the photovoltaic facilities have been modeled through the use of different membership mappings. From these mappings, a decision support system based on OWA operators informs of the performances of the facilities per day, by using natural language. Moreover, a state machine is also designed to determine the stage of each facility based on the stages and the performances from previous days. The main advantage of the designed system is that it solves the problem of "constant loss of energy production", without the consideration of meteorological conditions and being able to be more profitable. Moreover, the system is also scalable and portable, and complements previous works in energy production optimization. Finally, the proposed mechanism has been tested with real data, provided by Grupo Energ\'etico de Puerto Real S.A. which is an enterprise in charge of the management of six photovoltaic facilities in Puerto Real, C\'adiz, Spain, and good results have been obtained for faulting detection.

Flexoelectricity, inherent in all materials, offers a promising alternative to piezoelectricity for nanoscale actuation and sensing. However, its widespread application faces significant challenges: differentiating flexoelectric effects from those of piezoelectricity and other phenomena, verifying its universality across all material structures and thicknesses, and establishing a comprehensive database of flexoelectric coefficients across different materials. This work introduces a groundbreaking methodology that accurately isolates flexoelectricity from piezoelectric, electrostrictive and electrostatic effects, with a detection threshold extending below 1 fC/m. The robustness of this method is demonstrated through its application to amorphous hafnium oxide (HfO$_2$), successfully measuring a flexoelectric coefficient of 105 $\pm$ 10 pC/m. This measurement signifies the first measurement of flexoelectricity in hafnia, as well as in any amorphous material. Additionally, the study compiles a list of published flexoelectric coefficients, revealing an important insight. The relationship between the flexoelectric coefficient and the material's relative permittivity is better approximated by a quadratic proportionality. This challenges the traditional linear assumption proposed in Kogan's work and opens new avenues for future research in flexoelectric materials.

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model performance.

Image registration is a research field in which images must be compared and aligned independently of the point of view or camera characteristics. In some applications (such as forensic biometrics, satellite photography or outdoor scene identification) classical image registration systems fail due to one of the images compared represents a tiny piece of the other image. For instance, in forensics palmprint recognition, it is usual to find only a small piece of the palmprint, but in the database, the whole palmprint has been enrolled. The main reason of the poor behaviour of classical image registration methods is the gap between the amounts of salient points of both images, which is related to the number of points to be considered as outliers. Usually, the difficulty of finding a good match increases when the image that represents the tiny part of the scene has been drastically rotated. Again, in the case of palmprint forensics, it is difficult to decide a priori the orientation of the found tiny palmprint image. We present a rotation invariant registration method that explicitly considers that the image to be matched is a small piece of a larger image. We have experimentally validated our method in two different scenarios; palmprint identification and outdoor image registration.

In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Thus, the prediction model is unreliable although the overall model accuracy can be acceptable. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class. However, their effectiveness depends on several factors mainly related to data intrinsic characteristics, such as imbalance ratio, dataset size and dimensionality, overlapping between classes or borderline examples. In this work, the impact of these factors is analyzed through a comprehensive comparative study involving 40 datasets from different application areas. The objective is to obtain models for automatic selection of the best resampling strategy for any dataset based on its characteristics. These models allow us to check several factors simultaneously considering a wide range of values since they are induced from very varied datasets that cover a broad spectrum of conditions. This differs from most studies that focus on the individual analysis of the characteristics or cover a small range of values. In addition, the study encompasses both basic and advanced resampling strategies that are evaluated by means of eight different performance metrics, including new measures specifically designed for imbalanced data classification. The general nature of the proposal allows the choice of the most appropriate method regardless of the domain, avoiding the search for special purpose techniques that could be valid for the target data., Comment: Kraiem, M.S., S\'anchez-Hern\'andez, F., Moreno-Garc\'ia, M.N. Selecting the Suitable Resampling Strategy for Imbalanced Data Classification Regarding Dataset Properties. An Approach Based on Association Models. Appl. Sci. 2021, 11(18), 8546, 2021

Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have a vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their use in machine learning applications has not been explored due to the complexity of their design process: Current phononic metamaterials are restricted to simple geometries (e.g. periodic, tapered), and hence do not possess sufficient expressivity to encode machine learning tasks. We design and fabricate a non-periodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity; hence demonstrating that phononic metamaterials are a viable avenue towards zero-power smart devices., Comment: 13 pages, 11 figures

Music listening preferences at a given time depend on a wide range of contextual factors, such as user emotional state, location and activity at listening time, the day of the week, the time of the day, etc. It is therefore of great importance to take them into account when recommending music. However, it is very difficult to develop context-aware recommender systems that consider these factors, both because of the difficulty of detecting some of them, such as emotional state, and because of the drawbacks derived from the inclusion of many factors, such as sparsity problems in contextual pre-filtering. This work involves the proposal of a method for the detection of the user contextual state when listening to music based on the social tags of music items. The intrinsic characteristics of social tagging that allow for the description of items in multiple dimensions can be exploited to capture many contextual dimensions in the user listening sessions. The embeddings of the tags of the first items played in each session are used to represent the context of that session. Recommendations are then generated based on both user preferences and the similarity of the items computed from tag embeddings. Social tags have been used extensively in many recommender systems, however, to our knowledge, they have been hardly used to dynamically infer contextual states., Comment: 15th ACM Conference on Recommender Systems-Workshop on Context-Aware Recommender Systems (RECSYS 2021-CARS)

In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further improve the performance in the segmentation task, we develop an ensemble system which combines various deep learning architectures. We propose a two-layer ensemble of deep learning models for the segmentation of medical images. The prediction for each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weights-based scheme in which each model contributes differently to the combined result. The weights are found by solving linear regression problems. Experiments conducted on two popular medical datasets namely CAMUS and Kvasir-SEG show that the proposed method achieves better results concerning two performance metrics (Dice Coefficient and Hausdorff distance) compared to some well-known benchmark algorithms., Comment: 8 pages, 4 figures

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature, while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users' streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problem., Comment: Accepted in Complexity (ISSN: 1099-0526)