Search

Evaluating the capabilities of Large Language Models (LLMs) to assist teachers and students in educational tasks is receiving increasing attention. In this paper, we assess ChatGPT's capacities to solve and grade real programming exams, from an accredited BSc degree in Computer Science, written in Spanish. Our findings suggest that this AI model is only effective for solving simple coding tasks. Its proficiency in tackling complex problems or evaluating solutions authored by others are far from effective. As part of this research, we also release a new corpus of programming tasks and the corresponding prompts for solving the problems or grading the solutions. This resource can be further exploited by other research teams.

Search engines have traditionally served as primary tools for information seeking. However, the new Large Language Models (LLMs) have recently demonstrated remarkable capabilities in multiple tasks and, specifically, their adoption as question answering systems is becoming increasingly prevalent. It is expected that LLM-based conversational systems and traditional web engines will continue to coexist in the future, supporting end users in various ways. But there is a need for more scientific research on the effectiveness of both types of systems in facilitating accurate information seeking. In this study, we focus on their merits in answering health questions. We conducted an extensive study comparing different web search engines, LLMs and retrieval-augmented (RAG) approaches. Our research reveals intriguing conclusions. For example, we observed that the quality of webpages potentially responding to a health question does not decline as we navigate further down the ranked lists. However, according to our evaluation, web engines are less accurate than LLMs in finding correct answers to health questions. On the other hand, LLMs are quite sensitive to the input prompts, and we also found out that RAG leads to highly effective information seeking methods.

The rapid advance of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. While current quantum processors have significant scalability problems due to the limited number of qubits on each chip, distributed quantum computing offers a promising solution by networking multiple Quantum Processing Units (QPUs). To support this paradigm, robust Intermediate Representations (IRs) are crucial for translating high-level quantum algorithms into executable instructions across distributed systems. This paper introduces NetQIR, an extension of Microsoft's Quantum Intermediate Representation (QIR), specifically designed for distributed quantum computing. NetQIR is designed to meet the specific needs of distributed quantum systems by incorporating functions to manage quantum and classical communications between QPUs. The main objective is to facilitate the development of new distributed compilers by improving the integration and execution of quantum programmes in a distributed infrastructure, taking advantage of modular architectures to improve scalability. By extending QIR to support distributed quantum computing, NetQIR aims to complement and add capabilities to an already supported quantum IR and, at the same time, take advantage of the tools previously created for QIR. Throughout this paper the specification of the intermediate representation is introduced, including the basic instructions necessary to enable distributed quantum computing in an abstract form independent of the target machine.

The ESO public survey VISTA Variables in the V\'ia L\'actea (VVV) surveyed the inner Galactic bulge and the adjacent southern Galactic disk from $2009-2015$. Upon its conclusion, the complementary VVV eXtended (VVVX) survey has expanded both the temporal as well as spatial coverage of the original VVV area, widening it from $562$ to $1700$ sq. deg., as well as providing additional epochs in $JHK_{\rm s}$ filters from $2016-2023$. With the completion of VVVX observations during the first semester of 2023, we present here the observing strategy, a description of data quality and access, and the legacy of VVVX. VVVX took $\sim 2000$ hours, covering about 4% of the sky in the bulge and southern disk. VVVX covered most of the gaps left between the VVV and the VISTA Hemisphere Survey (VHS) areas and extended the VVV time baseline in the obscured regions affected by high extinction and hence hidden from optical observations. VVVX provides a deep $JHK_{\rm s}$ catalogue of $\gtrsim 1.5\times10^9$ point sources, as well as a $K_{\rm s}$ band catalogue of $\sim 10^7$ variable sources. Within the existing VVV area, we produced a $5D$ map of the surveyed region by combining positions, distances, and proper motions of well-understood distance indicators such as red clump stars, RR Lyrae, and Cepheid variables. In March 2023 we successfully finished the VVVX survey observations that started in 2016, an accomplishment for ESO Paranal Observatory upon 4200 hours of observations for VVV+VVVX. The VVV+VVVX catalogues complement those from the Gaia mission at low Galactic latitudes and provide spectroscopic targets for the forthcoming ESO high-multiplex spectrographs MOONS and 4MOST., Comment: 17 pages, 11 figures (+ appendix). Accepted for publication in Astronomy and Astrophysics in section 14: Catalogs and data

Large language models (LLMs) have transformed natural language processing. Yet, their predominantly English-centric training has led to biases and performance disparities across languages. This imbalance marginalizes minoritized languages, making equitable access to NLP technologies more difficult for languages with lower resources, such as Galician. We present the first two generative LLMs focused on Galician to bridge this gap. These models, freely available as open-source resources, were trained using a GPT architecture with 1.3B parameters on a corpus of 2.1B words. Leveraging continual pretraining, we adapt to Galician two existing LLMs trained on larger corpora, thus mitigating the data constraints that would arise if the training were performed from scratch. The models were evaluated using human judgments and task-based datasets from standardized benchmarks. These evaluations reveal a promising performance, underscoring the importance of linguistic diversity in generative models., Comment: 12 pages, 1 figure

The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of current quantum systems. This paper presents a comprehensive survey of the current state of the art in the distributed quantum computing field, exploring its foundational principles, landscape of achievements, challenges, and promising directions for further research. From quantum communication protocols to entanglement-based distributed algorithms, each aspect contributes to the mosaic of distributed quantum computing, making it an attractive approach to address the limitations of classical computing. Our objective is to provide an exhaustive overview for experienced researchers and field newcomers.

We identified Active Galactic Nuclei (AGN) candidates as counterparts to unidentified gamma-ray sources (UGS) from the Fermi-LAT Fourth Source Catalogue at lower Galactic latitudes. Our methodology is based on the use of near- and mid-infrared photometric data from the VISTA Variables in the V\'ia L\'actea (VVV) and Wide-field Infrared Survey Explorer (WISE) surveys. The AGN candidates associated with the UGS occupy very different regions from the stars and extragalactic sources in the colour space defined by the VVV and WISE infrared colours. We found 27 near-infrared AGN candidates possibly associated with 14 Fermi-LAT sources using the VVV survey. We also found 2 blazar candidates in the regions of 2 Fermi-LAT sources using WISE data. There is no match between VVV and WISE candidates. We have also examined the K$_\mathrm{s}$ light curves of the VVV candidates and applied the fractional variability amplitude ($\mathrm{\sigma_{rms}}$) and the slope of variation in the K$_\mathrm{s}$ passband to characterise the near-infrared variability. This analysis shows that more than 85% of the candidates have slopes in the K$_\mathrm{s}$ passband $ > 10^{-4}$ mag/day and present $\mathrm{\sigma_{rms}}$ values consistent with a moderate variability. This is in good agreement with typical results seen from type-1 AGN. The combination of YJHK$_\mathrm{s}$ colours and K$_\mathrm{s}$ variability criteria was useful for AGN selection, including its use in identifying counterparts to Fermi $\gamma$-ray sources., Comment: 16 pages, 19 figures, 4 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

Computational methods for depression detection aim to mine traces of depression from online publications posted by Internet users. However, solutions trained on existing collections exhibit limited generalisation and interpretability. To tackle these issues, recent studies have shown that identifying depressive symptoms can lead to more robust models. The eRisk initiative fosters research on this area and has recently proposed a new ranking task focused on developing search methods to find sentences related to depressive symptoms. This search challenge relies on the symptoms specified by the Beck Depression Inventory-II (BDI-II), a questionnaire widely used in clinical practice. Based on the participant systems' results, we present the DepreSym dataset, consisting of 21580 sentences annotated according to their relevance to the 21 BDI-II symptoms. The labelled sentences come from a pool of diverse ranking methods, and the final dataset serves as a valuable resource for advancing the development of models that incorporate depressive markers such as clinical symptoms. Due to the complex nature of this relevance annotation, we designed a robust assessment methodology carried out by three expert assessors (including an expert psychologist). Additionally, we explore here the feasibility of employing recent Large Language Models (ChatGPT and GPT4) as potential assessors in this complex task. We undertake a comprehensive examination of their performance, determine their main limitations and analyze their role as a complement or replacement for human annotators.

The goal of this work is to search for Active Galactic Nuclei (AGN) in the Galactic disc at very low latitudes with |b| $<$ 2$^\circ$. For this, we studied the five sources from the VVV near-infrared galaxy catalogue that have also WISE counterparts and present variability in the VIrac VAriable Classification Ensemble (VIVACE) catalogue. In the near-infrared colour-colour diagrams, these objects have in general redder colours compared to the rest of the sources in the field. In the mid-infrared ones, they are located in the AGN region, however there is a source that presents the highest interstellar extinction and different mid-IR colours to be a young stellar object (YSO). We also studied the source variability using two different statistical methods. The fractional variability amplitude $\sigma_{rms}$ ranges from 12.6 to 33.8, being in concordance with previous results found for type-1 AGNs. The slopes of the light curves are in the range (2.6$-$4.7) $\times 10^{-4}$ mag/day, also in agreement with results reported on quasars variability. The combination of all these results suggest that four galaxies are type-1 AGN candidates whereas the fifth source likely a YSO candidate., Comment: 11 pages, 6 figures, 5 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

The ground-based gamma-ray observatory VERITAS (Very Energetic Radiation Imaging Telescope Array System) is sensitive to photons of astrophysical origin with energies in the range between $\approx 85$ GeV to $\approx 30$ TeV. The instrument consists of four 12-m diameter imaging Cherenkov telescopes operating at the Fred Lawrence Whipple Observatory (FLWO) in southern Arizona. VERITAS started four-telescope operations in 2007 and collects about 1100 hours of good-weather data per year. The VERITAS collaboration has published over 100 journal articles since 2008 reporting on gamma-ray observations of a large variety of objects: Galactic sources like supernova remnants, pulsar wind nebulae, and binary systems; extragalactic sources like star forming galaxies, dwarf-spheroidal galaxies, and highly-variable active galactic nuclei. This note presents VTSCat: the catalog of high-level data products from all VERITAS publications., Comment: Version with corrected author list

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench., Comment: Robotics and Automation Letters (RA-L), 2022, 8 pages, 6 figures

One of the most important issues in the path to the convergence of HPC and Big Data is caused by the differences in their software stacks. Despite some research efforts, the interoperability between their programming models and languages is still limited. To deal with this problem we introduce a new computing framework called IgnisHPC, whose main objective is to unify the execution of Big Data and HPC workloads in the same framework. IgnisHPC has native support for multi-language applications using JVM and non-JVM-based languages. Since MPI was used as its backbone technology, IgnisHPC takes advantage of many communication models and network architectures. Moreover, MPI applications can be directly executed in a efficient way in the framework. The main consequence is that users could combine in the same multi-language code HPC tasks (using MPI) with Big Data tasks (using MapReduce operations). The experimental evaluation demonstrates the benefits of our proposal in terms of performance and productivity with respect to other frameworks such as Apache Spark. IgnisHPC is publicly available for the Big Data and HPC research community., Comment: 18 pages, 22 Figures, 5 Tables, submitted to Future Generation Computer Systems journal

The results of gamma-ray observations of the binary system HESS J0632+057 collected during 450 hours over 15 years, between 2004 and 2019, are presented. Data taken with the atmospheric Cherenkov telescopes H.E.S.S., MAGIC, and VERITAS at energies above 350 GeV were used together with observations at X-ray energies obtained with Swift-XRT, Chandra, XMM-Newton, NuSTAR, and Suzaku. Some of these observations were accompanied by measurements of the H{\alpha} emission line. A significant detection of the modulation of the VHE gamma-ray fluxes with a period of 316.7+-4.4 days is reported, consistent with the period of 317.3+-0.7 days obtained with a refined analysis of X-ray data. The analysis of data of four orbital cycles with dense observational coverage reveals short timescale variability, with flux-decay timescales of less than 20 days at very high energies. Flux variations observed over the time scale of several years indicate orbit-to-orbit variability. The analysis confirms the previously reported correlation of X-ray and gamma-ray emission from the system at very high significance, but can not find any correlation of optical H{\alpha} parameters with X-ray or gamma-ray energy fluxes in simultaneous observations. The key finding is that the emission of HESS J0632+057 in the X-ray and gamma-ray energy bands is highly variable on different time scales. The ratio of gamma-ray to X-ray flux shows the equality or even dominance of the gamma-ray energy range. This wealth of new data is interpreted taking into account the insufficient knowledge of the ephemeris of the system, and discussed in the context of results reported on other gamma-ray binary systems., Comment: Accepted for publication by The Astrophysical Journal

We present a spectrophotometer (optical density meter) combined with electromagnets dedicated to the analysis of suspensions of magnetotactic bacteria. The instrument can also be applied to suspensions of other magnetic cells and magnetic particles. We have ensured that our system, called MagOD, can be easily reproduced by providing the source of the 3D prints for the housing, electronic designs, circuit board layouts, and microcontroller software. We compare the performance of our system to existing adapted commercial spectrophotometers. In addition, we demonstrate its use by analyzing the absorbance of magnetotactic bacteria as a function of their orientation with respect to the light path and their speed of reorientation after the field has been rotated by 90 degrees. We continuously monitored the development of a culture of magnetotactic bacteria over a period of five days, and measured the development of their velocity distribution over a period of one hour. Even though this dedicated spectrophotometer is relatively simple to construct and cost-effective, a range of magnetic field-dependent parameters can be extracted from suspensions of magnetotactic bacteria. Therefore, this instrument will help the magnetotactic research community to understand and apply this intriguing micro-organism.