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3D sensors, also known as RGB-D sensors, utilize depth images where each pixel measures the distance from the camera to objects, using principles like structured light or time-of-flight. Advances in artificial vision have led to affordable 3D cameras capable of real-time object detection without object movement, surpassing 2D cameras in information depth. These cameras can identify objects of varying colors and reflectivities and are less affected by lighting changes. The described prototype uses RGB-D sensors for bidirectional people counting in venues, aiding security and surveillance in spaces like stadiums or airports. It determines real-time occupancy and checks against maximum capacity, crucial during emergencies. The system includes a RealSense D415 depth camera and a mini-computer running object detection algorithms to count people and a 2D camera for identity verification. The system supports statistical analysis and uses C++, Python, and PHP with OpenCV for image processing, demonstrating a comprehensive approach to monitoring venue occupancy., Comment: 8 pages, in Spanish language, 8 figures

Gravity modes (g modes), mixed gravito-acoustic modes (mixed modes), and gravito-inertial modes (gi modes) possess unmatched properties as probes for stars with radiative interiors. The structural and dynamical constraints that they are able to provide cannot be accessed by other means. While they provide precious insights into the internal dynamics of evolved stars as well as massive and intermediate-mass stars, their non-detection in main sequence (MS) solar-type stars make them a crucial missing piece in our understanding of angular momentum transport in radiative zones and stellar rotational evolution. In this work, we aim to apply certain analysis tools originally developed for helioseismology in order to look for g-mode signatures in MS solar-type stars. We select a sample of the 34 most promising MS solar-type stars with Kepler four-year long photometric time series. All these stars are well-characterised late F-type stars with thin convective envelopes, fast convective flows, and stochastically excited acoustic modes (p modes). For each star, we compute the background noise level of the Fourier power spectrum to identify significant peaks at low frequency. After successfully detecting individual peaks in 12 targets, we further analyse four of them and observe distinct patterns of surrounding peaks with a low probability of being noise artifacts. Comparisons with the predictions from reference models suggest that these patterns are compatible with the presence of non-asymptotic low-order pure g modes, pure p modes, and mixed modes. Given their sensitivity to both the convective core interface stratification and the coupling between p- and g-mode resonant cavities, such modes are able to provide strong constraints on the structure and evolutionary states of the related targets. [abridged], Comment: 19 pages, 19 figures, accepted for publication in A&A

We explore different generalizations of the classical concept of independent families on $\omega$ following the study initiated by Fisher and Montoya. We show that under $\diamondsuit^*(\kappa)$ we can get strongly independent families on $\kappa$ of size $2^\kappa$ and present an equivalence of $\mathsf{GCH}$ in terms of strongly independent families. We merge the two natural ways of generalizing independent families through a filter or an ideal and we focus on the $\mathcal{C}$-independent families, where $\mathcal{C}$ is the club filter. Also we show a relationship between the existence of $\mathcal{J}$-independent families and the saturation of the ideal $\mathcal{J}$.

Missing time-series data is a prevalent problem in many prescriptive analytics models in operations management, healthcare and finance. Imputation methods for time-series data are usually applied to the full panel data with the purpose of training a prescriptive model for a downstream out-of-sample task. For example, the imputation of missing asset returns may be applied before estimating an optimal portfolio allocation. However, this practice can result in a look-ahead-bias in the future performance of the downstream task, and there is an inherent trade-off between the look-ahead-bias of using the entire data set for imputation and the larger variance of using only the training portion of the data set for imputation. By connecting layers of information revealed in time, we propose a Bayesian consensus posterior that fuses an arbitrary number of posteriors to optimize the variance and look-ahead-bias trade-off in the imputation. We derive tractable two-step optimization procedures for finding the optimal consensus posterior, with Kullback-Leibler divergence and Wasserstein distance as the dissimilarity measure between posterior distributions. We demonstrate in simulations and in an empirical study the benefit of our imputation mechanism for portfolio allocation with missing returns., Comment: This work merges and supersedes arXiv:2102.12736

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

Missing time-series data is a prevalent practical problem. Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task. For example, in finance, imputation of missing returns may be applied prior to training a portfolio optimization model. Unfortunately, this practice may result in a look-ahead-bias in the future performance on the downstream task. There is an inherent trade-off between the look-ahead-bias of using the full data set for imputation and the larger variance in the imputation from using only the training data. By connecting layers of information revealed in time, we propose a Bayesian posterior consensus distribution which optimally controls the variance and look-ahead-bias trade-off in the imputation. We demonstrate the benefit of our methodology both in synthetic and real financial data., Comment: This paper has been superseded by arXiv:2202.00871

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.