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This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government., Comment: 24 pages, 12 figures

The paper provides a survey of the development of machine-learning techniques for video analysis. The survey provides a summary of the most popular deep learning methods used for human activity recognition. We discuss how popular architectures perform on standard datasets and highlight the differences from real-life datasets dominated by multiple activities performed by multiple participants over long periods. For real-life datasets, we describe the use of low-parameter models (with 200X or 1,000X fewer parameters) that are trained to detect a single activity after the relevant objects have been successfully detected. Our survey then turns to a summary of machine learning methods that are specifically developed for working with a small number of labeled video samples. Our goal here is to describe modern techniques that are specifically designed so as to minimize the amount of ground truth that is needed for training and testing video analysis systems. We provide summaries of the development of self-supervised learning, semi-supervised learning, active learning, and zero-shot learning for applications in video analysis. For each method, we provide representative examples.

Oscura is a planned light-dark matter search experiment using Skipper-CCDs with a total active mass of 10 kg. As part of the detector development, the collaboration plans to build the Oscura Integration Test (OIT), an engineering test with 10% of the total mass. Here we discuss the early science opportunities with the OIT to search for millicharged particles (mCPs) using the NuMI beam at Fermilab. mCPs would be produced at low energies through photon-mediated processes from decays of scalar, pseudoscalar, and vector mesons, or direct Drell-Yan productions. Estimates show that the OIT would be a world-leading probe for mCPs in the MeV mass range., Comment: 13 pages, 10 figures

Oscura is a proposed multi-kg skipper-CCD experiment designed for a dark matter (DM) direct detection search that will reach unprecedented sensitivity to sub-GeV DM-electron interactions with its 10 kg detector array. Oscura is planning to operate at SNOLAB with 2070 m overburden, and aims to reach a background goal of less than one event in each electron bin in the 2-10 electron ionization-signal region for the full 30 kg-year exposure, with a radiation background rate of 0.01 dru. In order to achieve this goal, Oscura must address each potential source of background events, including instrumental backgrounds. In this work, we discuss the main instrumental background sources and the strategy to control them, establishing a set of constraints on the sensors' performance parameters. We present results from the tests of the first fabricated Oscura prototype sensors, evaluate their performance in the context of the established constraints and estimate the Oscura instrumental background based on these results.

Highly pixelated solid-state detectors offer outstanding capabilities in the identification and suppression of backgrounds from natural radioactivity. We present the background-identification strategies developed for the DAMIC experiment, which employs silicon charge-coupled devices to search for dark matter. DAMIC has demonstrated the capability to disentangle and measure the activities of every $\beta$ emitter from the $^{32}$Si, $^{238}$U and $^{232}$Th decay chains in the silicon target. Similar strategies will be adopted by the Selena Neutrino Experiment, which will employ hybrid amorphous $^{82}$Se/CMOS imagers to perform spectroscopy of $\beta\beta$ decay and solar neutrinos. We present the proposed experimental strategy for Selena to achieve zero-background in a 100-ton-year exposure., Comment: Proceedings for Low Radioactivity Techniques (LRT) 2022

We report on recent progress in the search for dark matter particles with masses from 1 MeV to 1 GeV. Several dark matter candidates in this mass range are expected to generate measurable electronic-recoil signals in direct-detection experiments. We focus on dark matter particles scattering with electrons in semiconductor detectors since they have fundamentally the highest sensitivity due to their low ionization threshold. Charge-coupled device (CCD) silicon detectors are the leading technology, with significant progress expected in the coming years. We present the status of the CCD program and briefly report on other efforts., Comment: Proceedings for the 14th International Conference on Identification of Dark Matter, Submission to SciPost

The DAMIC experiment employs large-area, thick charge-coupled devices (CCDs) to search for the interactions of low-mass dark matter particles in the galactic halo with silicon atoms in the CCD target. From 2017 to 2019, DAMIC collected data with a seven-CCD array (40-gram target) installed in the SNOLAB underground laboratory. We report dark-matter search results, including a conspicuous excess of events above the background model below 200 eV$_{\rm ee}$, whose origin remains unknown. We present details of the published spectral analysis, and update on the deployment of skipper CCDs to perform a more precise measurement by early 2023., Comment: Proceedings for the 14th International Conference on Identification of Dark Matter, Submission to SciPost

This article shows an It\^o-Wentzell type formula adapted to rough paths with $\alpha$-H\"older regularity $\alpha \in (\frac{1}{3},\frac{1}{2}]$. We improve previous results of R. Castrequini and P. Catuogno for the Young integral and C. Keller and J. Zhang for rough paths.

Imaging sensors made from an ionization target layer of amorphous selenium (aSe) coupled to a silicon complementary metal-oxide-semiconductor (CMOS) active pixel array for charge readout are a promising technology for neutrino physics. The high spatial resolution in a solid-state target provides unparalleled rejection of backgrounds from natural radioactivity in the search for neutrinoless $\beta\beta$ decay and for solar neutrino spectroscopy with $^{82}$Se. We present results on the ionization response of aSe measured from the photoabsorption of 122 keV $\gamma$ rays in a single-pixel device. We report on the progress in the fabrication and testing of the first prototype imaging sensors based on the Topmetal-II$^{\rm -}$ pixelated CMOS charge readout chip. We explore the scientific reach of a large neutrino detector with the proposed technology based on our experimental understanding of the sensor performance., Comment: Proceedings for the 17th International Conference on Topics in Astroparticle and Underground Physics (TAUP 2021)

We performed a measurement of the ionization response of 200 $\mu$m-thick amorphous selenium (aSe) layers under drift electric fields of up to 50 V/$\mu$m. The aSe target was exposed to ionizing radiation from a $^{57}$Co radioactive source and the ionization pulses were recorded with high resolution. Using the spectral line from the photoabsorption of 122 keV $\gamma$ rays, we measured the charge yield in aSe and the line width as a function of drift electric field. From a detailed microphysics simulation of charge generation and recombination in aSe, we conclude that the strong dependence of recombination on the ionization track density provides the dominant contribution to the energy resolution in aSe. These results provide valuable input to estimate the sensitivity of a proposed next-generation search for the neutrinoless $\beta\beta$ decay of $^{82}$Se that aims to employ imaging sensors with an active layer of aSe. We estimate the RMS line width of the integrated ionization signal from neutrinoless $\beta\beta$ decay events (of deposited energy 3 MeV) to be 2.0% for a drift field of 50 V/$\mu$m. The energy resolution can be improved to 1% by correcting for the charge yield as a function of ionization density along the imaged electron tracks., Comment: 15 pages, 11 figures

Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, we compared it to other machine learning techniques and statistical models. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most informative ones, a more parsimonious but equally effective and explainable model can be obtained..., Comment: Submitted to IEEE Access Journal

The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We show good performance for predicting one-year mortality with an average AUC of 0.85 from a model cross-validated on 1,775,926 12-lead resting ECGs, that were collected over a 34-year period in a large regional health system. Even within the large subset of ECGs interpreted as 'normal' by a physician (n=297,548), the model performance to predict one-year mortality remained high (AUC=0.84), and Cox Proportional Hazard model revealed a hazard ratio of 6.6 (p<0.005) for the two predicted groups (dead vs alive one year after ECG) over a 30-year follow-up period. A blinded survey of three cardiologists suggested that the patterns captured by the model were generally not visually apparent to cardiologists even after being shown 240 paired examples of labeled true positives (dead) and true negatives (alive). In summary, deep learning can add significant prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as 'normal' by physicians., Comment: An updated version of this paper is now published with Nature Medicine (2020)

We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data. Our approach builds on prior multi-modal machine learning models by now enabling visualization of how individual factors contribute to the overall outcome risk, assuming other factors remain constant, which was previously impossible. We demonstrate the value of this approach using a large multi-modal clinical dataset including both EHR data and 31,278 echocardiographic videos of the heart from 26,793 patients. We generated separate models for (i) clinical data only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric variables derived from the videos, which we call echocardiography-derived measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable multi-modal model maintained performance compared to non-interpretable models (Random Forest, XGBoost), and also performed significantly better than a model using a single modality (average AUC=0.82). Clinically relevant insights and multi-modal variable importance rankings were also facilitated by the new model, which have previously been impossible., Comment: Submitted to IEEE JBHI

To maintain the desired quality of a product or service it is necessary to monitor the process that results in the product or service. This monitoring method is called Statistical Process Management, or Statistical Process Control. It is in widespread usage in industry. Extensive statistical methodology has been developed to make it possible to detect when a process goes out of control while allowing for natural variability that occurs when the process is in control. This paper introduces nonparametric methods for monitoring data, whether it is univariate or multivariate, and whether the interest is in detecting a change of location or scale or both. These methods, based on sequential normal scores, are much simpler than the most popular nonparametric methods currently in use and have good power for detecting out of control observations. Sixteen new statistical tests are introduced for the first time in this paper, with 17 examples, 33 tables, and 48 figures to complete the instructions for their application., Comment: 92 pages, 33 tables and 48 figures

Quantile estimation is a problem presented in fields such as quality control, hydrology, and economics. There are different techniques to estimate such quantiles. Nevertheless, these techniques use an overall fit of the sample when the quantiles of interest are usually located in the tails of the distribution. Regression Approach for Quantile Estimation (RAQE) is a method based on regression techniques and the properties of the empirical distribution to address this problem. The method was first presented for the problem of capability analysis. In this paper, a generalization of the method is presented, extended to the multiple sample scenario, and data from real examples is used to illustrate the proposed approaches. In addition, theoretical framework is presented to support the extension for multiple homogeneous samples and the use of the uncertainty of the estimated probabilities as a weighting factor in the analysis., Comment: 21 pages, 4 figures

The sequential analysis of series often requires nonparametric procedures, where the most powerful ones frequently use rank transformations. Re-ranking the data sequence after each new observation can become too intensive computationally. This led to the idea of sequential ranks, where only the most recent observation is ranked. However, difficulties finding, or approximating, the null distribution of the statistics may have contributed to the lack of popularity of these methods. In this paper, we propose transforming the sequential ranks into sequential normal scores which are independent, and asymptotically standard normal random variables. Thus original methods based on the normality assumption may be used. A novel approach permits the inclusion of a priori information in the form of quantiles. It is developed as a strategy to increase the sensitivity of the scoring statistic. The result is a powerful convenient method to analyze non-normal data sequences. Also, four variations of sequential normal scores are presented using examples from the literature. Researchers and practitioners might find this approach useful to develop nonparametric procedures to address new problems extending the use of parametric procedures when distributional assumptions are not met. These methods are especially useful with large data streams where efficient computational methods are required., Comment: 39 pages, 8 figures

The growth and saturation of Buneman-type instabilities is examined with a particle-in-cell (PIC) simulation for parameters that are representative for the foreshock region of fast supernova remnant (SNR) shocks. A dense ion beam and the electrons correspond to the upstream plasma and a fast ion beam to the shock-reflected ions. The purpose of the 2D simulation is to identify the nonlinear saturation mechanisms, the electron heating and potential secondary instabilities that arise from anisotropic electron heating and result in the growth of magnetic fields. We confirm that the instabilities between both ion beams and the electrons saturate by the formation of phase space holes by the beam-aligned modes. The slower oblique modes accelerate some electrons, but they can not heat up the electrons significantly before they are trapped by the faster beam-aligned modes. Two circular electron velocity distributions develop, which are centred around the velocity of each ion beam. They develop due to the scattering of the electrons by the electrostatic wave potentials. The growth of magnetic fields is observed, but their amplitude remains low., Comment: 18 pages, 11 figures, accepted for publication in Plasma Physics and Controlled Fusion

The discovery of a gravitational lens candidate is reported. The quasars SDSS J111611.73+411821.5 and SDSS J111610.68+411814.4 are recognized as two images of the same object, being strongly lensed by the closer galaxy SDSS J111611.03+411820.9. The source is located at a redshift of z~3, while the redshift of the lens galaxy is z~0.25. The separation of the images is large,~13 arcsec. Commonly used models of the mass distribution for the lens galaxy with values of the parameters in the expected range describe the positions and fluxes of the images., Comment: 14 pages, 3 figures