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Context. Gaia18cjb is one of the Gaia-alerted eruptive young star candidates which has been experiencing a slow and strong brightening during the last 13 years, similar to some FU Orionis-type objects. Aims. The aim of this work is to derive the young stellar nature of Gaia18cjb, determine its physical and accretion properties to classify its variability. Methods. We conducted monitoring observations using multi-filter optical and near-infrared photometry, as well as near-infrared spectroscopy. We present the analysis of pre-outburst and outburst optical and infrared light curves, color-magnitude diagrams in different bands, the detection of near-IR spectral lines, and estimates of both stellar and accretion parameters during the burst. Results. The optical light curve shows an unusually long (8 years) brightening event of 5 mag in the last 13 years, before reaching a plateau indicating that the burst is still on-going, suggesting a FUor-like nature. The same outburst is less strong in the infrared light curves. The near-infrared spectra, obtained during the outburst, exhibit emission lines typical of highly accreting low-intermediate mass young stars with typical EXor features. The spectral index of Gaia18cjb SED classifies it as a Class I in the pre-burst stage and a Flat Spectrum young stellar object (YSO) during the burst. Conclusions. Gaia18cjb is an eruptive YSO which shows FUor-like photometric features (in terms of brightening amplitude and length of the burst) and EXor-like spectroscopic features and accretion rate, as V350 Cep and V1647 Ori, classified as objects in between FUors and EXors, Comment: 21 pages, 13 figures

This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5. Shared task 5 targeted the normalization of adverse drug event mentions in Twitter to standard concepts of the Medical Dictionary for Regulatory Activities terminology. Our system hinges on a two-stage approach: BERT fine-tuning for entity recognition, followed by zero-shot normalization using sentence transformers and reciprocal-rank fusion. The approach yielded a precision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformed the median performance in shared task 5 by 10% and demonstrated the highest performance among all participants. These results substantiate the effectiveness of our approach and its potential application for adverse drug event normalization in the realm of social media text mining., Comment: Peer-reviewed and accepted for presentation at the #SMM4H 2023 Workshop

Artificial Intelligence (AI) is becoming more and more popular as time passes, allowing to perform tasks that were difficult to do in the past. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, AI in programming teaching is something that still has to be explored, since in this area we usually find assessment tools that allow grading the students work, but we can not find many tools aimed towards providing feedback to the students in the process of creating their program. In this work we present an AI based intelligent tutor that answers students programming questions. The tool has been tested by university students at the URJC along a whole course. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses., Comment: Paper was used in order to participate in a call for projects. The project was denied so this paper does not make any sense

We investigate the star formation history of the IC1396 region by studying its kinematics and completing the population census. We use multiwavelength data, combining optical spectroscopy (to identify and classify new members), near-infrared photometry (to trace shocks, jets, and outflows and the interactions between the cluster members and the cloud), along with Gaia EDR3 to identify new potential members in the multidimensional proper motion/parallax space. The revised Gaia EDR3 distance is 925$\pm$73 pc, slightly closer than previously obtained with DR2. The Gaia data reveal four distinct subclusters in the region. These subclusters are consistent in distance but display differences in proper motion. This, with their age differences, hints towards a complex and varied star formation history. Gaia data also unveil the intermediate-mass objects that tend to evade spectroscopic and disk surveys. Our analysis allows us to identify 334 new members. We estimate an average age of $\sim$4 Myr, confirming previous age estimates. With the new members added to our study, we estimate a disk fraction of 28\%, lower than previous values, due to our method detecting mainly new, diskless intermediate-mass stars. We find age differences between the subclusters, which evidences a complex star formation history with different episodes of star formation., Comment: A&A in press

Task-based programming models have risen in popularity as an alternative to traditional fork-join parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task DAG once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the taskiter, a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple immediate successor locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the taskiter and the immediate successor heuristic in 8 iterative benchmarks. Using small task granularities, we obtain an average speedup of 3.7x over the reference OmpSs-2 implementation and an average of 5x and 7.46x speedup over the LLVM and GCC OpenMP runtimes, respectively., Comment: 11 pages, 6 figures

Young stars show variability on different time-scales from hours to decades, with a range of amplitudes. We studied two young stars, which triggered the Gaia Science Alerts system due to brightenings on a time-scale of a year. Gaia20bwa brightened by about half a magnitude, whereas Gaia20fgx brightened by about two and half magnitudes. We analyzed the Gaia light curves, additional photometry, and spectra taken with the Telescopio Nazionale Galileo and the Gran Telescopio Canarias. Several emission lines were detected toward Gaia20bwa, including hydrogen lines from H$\alpha$ to H$\delta$, Pa$\beta$, Br$\gamma$, and lines of Ca II, O I, and Na I. The H$\alpha$ and Br$\gamma$ lines were detected toward Gaia20fgx in emission in its bright state, with additional CO lines in absorption, and the Pa$\beta$ line with an inverse P Cygni profile during its fading. Based on the Br$\gamma$ lines the accretion rate was $(2.4-3.1)\times10^{-8}$ $M_\odot$ yr$^{-1}$ for Gaia20bwa and $(4.5-6.6)\times10^{-8}$ $M_\odot$ yr$^{-1}$ for Gaia20fgx during their bright state. The accretion rate of Gaia20fgx dropped by almost a factor of 10 on a time-scale of half a year. The accretion parameters of both stars were found to be similar to those of classical T Tauri stars, lower than those of young eruptive stars. However, the amplitude and time-scale of these brightenings place these stars to a region of the parameter space, which is rarely populated by young stars. This suggests a new class of young stars, which produce outbursts on a time-scale similar to young eruptive stars, but with smaller amplitudes., Comment: Accepted to MNRAS

The use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. The 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training: using kinematic gait signals and using gait descriptors. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly. The accuracy of the 3D camera was measured before and after training. In both training approaches, an improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best. Therefore, we believe that the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis., Comment: This manuscript is in a review process in the Journal of Artificial Intelligence in Medicine, Elsevier

Future Exascale systems will feature massive parallelism, many-core processors and heterogeneous architectures. In this scenario, it is increasingly difficult for HPC applications to fully and efficiently utilize the resources in system nodes. Moreover, the increased parallelism exacerbates the effects of existing inefficiencies in current applications. Research has shown that co-scheduling applications to share system nodes instead of executing each application exclusively can increase resource utilization and efficiency. Nevertheless, the current oversubscription and co-location techniques to share nodes have several drawbacks which limit their applicability and make them very application-dependent. This paper presents co-execution through system-wide scheduling. Co-execution is a novel fine-grained technique to execute multiple HPC applications simultaneously on the same node, outperforming current state-of-the-art approaches. We implement this technique in nOS-V, a lightweight tasking library that supports co-execution through system-wide task scheduling. Moreover, nOS-V can be easily integrated with existing programming models, requiring no changes to user applications. We showcase how co-execution with nOS-V significantly reduces schedule makespan for several applications on single node and distributed environments, outperforming prior node-sharing techniques., Comment: 12 pages, 10 figures. Submitted to the Journal of Parallel and Distributed Computing

We present follow-up photometric and spectroscopic observations, and subsequent analysis of Gaia20eae. This source triggered photometric alerts during 2020 after showing a $\sim$3 mag increase in its brightness. Its Gaia Alert light curve showed the shape of a typical eruptive young star. We carried out observations to confirm Gaia20eae as an eruptive young star and classify it. Its pre-outburst spectral energy distribution shows that Gaia20eae is a moderately embedded Class II object with $L_\mathrm{bol} = 7.22$ L$_\odot$. The color-color and color-magnitude diagrams indicate that the evolution in the light curve is mostly gray. Multiple epochs of the H$\alpha$ line profile suggest an evolution of the accretion rate and winds. The near-infrared spectra display several emission lines, a feature typical of EXor-type eruptive young stars. We estimated the mass accretion rate during the dimming phase to be $\dot{M} = 3-8 \times 10^{-7}$ M$_\odot$ yr$^{-1}$, higher than typical T Tauri stars of similar mass and comparable to other EXors. We conclude Gaia20eae is a new EXor-type candidate., Comment: Accepted for publication in ApJ. 20 pages, 11 figures

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks., Comment: 14 pages, 11 figures. Published in the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP'21)

Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates., Comment: Accepted for ICLR2020

Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems build a direct mapping between linguistic and waveform domains, like SampleRNN. This way, possible signal naturalness losses are avoided as intermediate acoustic representations are discarded. Another important dimension of study apart from naturalness is their adaptability to generate voice from new speakers that were unseen during training. In this paper we first propose the use of problem-agnostic speech embeddings in a multi-speaker acoustic model for TTS based on SampleRNN. This way we feed the acoustic model with speaker acoustically dependent representations that enrich the waveform generation more than discrete embeddings unrelated to these factors. Our first results suggest that the proposed embeddings lead to better quality voices than those obtained with discrete embeddings. Furthermore, as we can use any speech segment as an encoded representation during inference, the model is capable to generalize to new speaker identities without retraining the network. We finally show that, with a small increase of speech duration in the embedding extractor, we dramatically reduce the spectral distortion to close the gap towards the target identities., Comment: Published at 10th ISCA Speech Synthesis Workshop