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Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes -- particularly, multi-instance learning (MIL) and classical machine learning formulations -- to model student behavior. Besides, explainable artificial intelligence (XAI) is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2500 submissions from roughly 90 different students from a programming-related course in a computer science degree. The results obtained validate the proposal: The model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioral pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.

Infinite layer nickelates provide a new route beyond copper oxides to address outstanding questions in the field of unconventional superconductivity. However, their synthesis poses considerable challenges, largely hindering experimental research on this new class of oxide superconductors. That synthesis is achieved in a two-step process that yields the most thermodynamically stable perovskite phase first, then the infinite-layer phase by topotactic reduction, the quality of the starting phase playing a crucial role. Here, we report on reliable synthesis of superconducting infinite-layer nickelate films after successive topochemical reductions of a parent perovskite phase with nearly optimal stoichiometry. Careful analysis of the transport properties of the incompletely reduced films reveals an improvement of the strange metal behaviour of their normal state resistivity over subsequent topochemical reductions, offering insight into the reduction process., Comment: 26 pages; 25 figures. Results unchanged; supplemental figures added; references added

Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, $k$NN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately and their results are compared to those obtained using equivalent methods on a state-of-the-art CNN. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases, and that the selection of the technique may vary depending on the amount of data available and the level of imbalance., Comment: Submited to Pattern Analysis and Applications

Efficient optical quantum memories are a milestone required for several quantum technologies including repeater-based quantum key distribution and on-demand multi-photon generation. We present an efficiency optimization of an optical electromagnetically induced transparency (EIT) memory experiment in a warm cesium vapor using a genetic algorithm and analyze the resulting waveforms. The control pulse is represented either as a Gaussian or free-form pulse, and the results from the optimization are compared. We see an improvement factor of 3(7)\% when using optimized free-form pulses. By limiting the allowed pulse energy in a solution, we show an energy-based optimization giving a 30% reduction in energy, with minimal efficiency loss.

Methods. We performed measurements of the \MgII, \MgI, \FeIIa, \FeIIb, and \FeIIc\ spectral lines present in the spectra of the selected sample to determine the EW and velocity of the flows observed in the star-forming galaxies. Subsequently, we conducted $10^7$ bootstrap simulations using Spearman's rank correlation coefficient ($\rho_s$) to explore correlations with galaxy properties. Furthermore, we calculated the covering factor, gas density, and optical depth for the measured \ion{Fe}{II} doublets. Results. Our analysis revealed strong correlations between the EW of \ion{Mg}{II} lines and both $M_{*}$ ($\rho_s=0.43$, 4.5$\sigma$) and SFR ($\rho_s=0.42$, 4.4$\sigma$). For the \ion{Fe}{II} lines, we observed strong correlations between the EW and SFR ($\rho_s\sim0.65$, $>3.9\sigma$), with a weaker correlation for $M_{*}$ ($\rho_s\sim0.35$, $>1.9\sigma$). No notable correlations were found between velocity measurements of \ion{Mg}{II} line and $M_{*}$, SFR, or sSFR of the objects ($\rho_s\sim0.1)$. However, a negative strong correlation was found between the velocity of the \ion{Fe}{II} lines and the SFR of the galaxies ($\rho_s\sim-0.45$, $\sim3\sigma$). Our results align with previous studies but studying FIR-selected objects. Finally, we detected a candidate \textit{loitering outflow}, a recently discovered subtype of FeLoBAL quasar, at redshift of $z=1.4399$, exhibiting emission in \ion{C}{III}] and low line velocities ($|v|\lesssim$ 200 km/s)., Comment: 25 pages, 31 figures

Stars formed with initial mass over 50 Msun are very rare today, but they are thought to be more common in the early universe. The fates of those early, metal-poor, massive stars are highly uncertain. Most are expected to directly collapse to black holes, while some may explode as a result of rotationally powered engines or the pair-creation instability. We present the chemical abundances of J0931+0038, a nearby low-mass star identified in early followup of SDSS-V Milky Way Mapper, which preserves the signature of unusual nucleosynthesis from a massive star in the early universe. J0931+0038 has relatively high metallicity ([Fe/H] = -1.76 +/- 0.13) but an extreme odd-even abundance pattern, with some of the lowest known abundance ratios of [N/Fe], [Na/Fe], [K/Fe], [Sc/Fe], and [Ba/Fe]. The implication is that a majority of its metals originated in a single extremely metal-poor nucleosynthetic source. An extensive search through nucleosynthesis predictions finds a clear preference for progenitors with initial mass > 50 Msun, making J0931+0038 one of the first observational constraints on nucleosynthesis in this mass range. However the full abundance pattern is not matched by any models in the literature. J0931+0038 thus presents a challenge for the next generation of nucleosynthesis models and motivates study of high-mass progenitor stars impacted by convection, rotation, jets, and/or binary companions. Though rare, more examples of unusual early nucleosynthesis in metal-poor stars should be found in upcoming large spectroscopic surveys., Comment: 11 pages + 22 page appendix, accepted to ApJL

In the quest of new absorbent for hydrogen storage, we investigate the capacities of slit pores formed by two BC3 sheets decorated with Li atoms. Their hydrogen storage capacities are determined using density-functional theory in conjunction with a quantum-thermodynamic model that allows to simulate real operating conditions, i.e., finite temperatures and different loading and depletion pressures applied to the adsorbent in the charge-delivery cycles. We show that the capacities of the adsorbed hydrogen phase of Li-decorated BC3 slit pores are larger than those reported recently for graphene and Li-decorated borophene slit pores. On the other hand, the usable volumetric and gravimetric capacities of Li-decorated BC3 slit pores can meet the targets stipulated by the U.S. Department of Energy (DOE) for onboard hydrogen storage at moderate temperatures and loading pressures well below those used in the tanks employed in current technology. In particular, the usable volumetric capacity for pore widths of about 10 {\AA} meets the DOE target at a loading pressure of 6.6 MPa when depleting at ambient pressure. Our results highlight the important role played by the rotational degree of freedom of the H2 molecule in determining the confining potential within the slip pores and their hydrogen storage capacities., Comment: Article itself: 26 pages, 12 figures, 13 equations, 1 table, 78 cited articles; Supporting information: 8 pages, 6 figures, 6 equations

Researchers in natural science need reliable methods for quantifying animal behavior. Recently, numerous computer vision methods emerged to automate the process. However, observing wild species at remote locations remains a challenging task due to difficult lighting conditions and constraints on power supply and data storage. Event cameras offer unique advantages for battery-dependent remote monitoring due to their low power consumption and high dynamic range capabilities. We use this novel sensor to quantify a behavior in Chinstrap penguins called ecstatic display. We formulate the problem as a temporal action detection task, determining the start and end times of the behavior. For this purpose, we recorded a colony of breeding penguins in Antarctica for several weeks and labeled event data on 16 nests. The developed method consists of a generator of candidate time intervals (proposals) and a classifier of the actions within them. The experiments show that the event cameras' natural response to motion is effective for continuous behavior monitoring and detection, reaching a mean average precision (mAP) of 58% (which increases to 63% in good weather conditions). The results also demonstrate the robustness against various lighting conditions contained in the challenging dataset. The low-power capabilities of the event camera allow it to record significantly longer than with a conventional camera. This work pioneers the use of event cameras for remote wildlife observation, opening new interdisciplinary opportunities. https://tub-rip.github.io/eventpenguins/, Comment: 13 pages, 8 figures, 12 tables, Project page: https://tub-rip.github.io/eventpenguins/

This paper proposes an interpretation of RLAIF as Bayesian inference by introducing distilled Self-Critique (dSC), which refines the outputs of a LLM through a Gibbs sampler that is later distilled into a fine-tuned model. Only requiring synthetic data, dSC is exercised in experiments regarding safety, sentiment, and privacy control, showing it can be a viable and cheap alternative to align LLMs. Code released at \url{https://github.com/vicgalle/distilled-self-critique}., Comment: Accepted to ICLR 2024 (TinyPapers track)

In this paper, we delve into the intricacies of boundary stabilization for the Linearized KP-II Equation within the constraints of a bounded domain. Our primary aim is to engineer a parameterized set of feedback laws that not only lead to the existence of solutions originating from small enough initial data but also facilitate their uniform exponential decay within the energy space. As far as we know, the present work is the first result on boundary feedback stabilization related to the Kadomtsev-Petviashvili II equation posed in a rectangle $\Omega=(0,L)\times (0,L)$, $L>0$., Comment: 14 pages, preprint

We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional cameras struggle with high-speed motion capture due to bandwidth and dynamic range limitations. Event cameras are ideal sensors to solve this problem because they encode compressed change information at high temporal resolution. In this work, we tackle the problem of event-based continuous color video decompression, pairing single static color frames and event data to reconstruct temporally continuous videos. Our approach combines continuous long-range motion modeling with a neural synthesis model, enabling frame prediction at arbitrary times within the events. Our method only requires an initial image, thus increasing the robustness to sudden motions, light changes, minimizing the prediction latency, and decreasing bandwidth usage. We also introduce a novel single-lens beamsplitter setup that acquires aligned images and events, and a novel and challenging Event Extreme Decompression Dataset (E2D2) that tests the method in various lighting and motion profiles. We thoroughly evaluate our method by benchmarking color frame reconstruction, outperforming the baseline methods by 3.61 dB in PSNR and by 33% decrease in LPIPS, as well as showing superior results on two downstream tasks.

We use instruction-tuned Large Language Models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.

Outflows are believed to be ubiquitous in all AGNs, although their presence in low luminosity AGNs, in particular, for LINERs, has only started to be explored. Their properties (geometry, mass and energetics) are still far from being properly characterised. We use integral field spectroscopic data from the MEGARA instrument, at GTC, to analyse a small sample of nine LINERs, candidates of hosting ionised gas outflows. We aim to study the main emission lines in the optical to identify their properties and physical origin. We obtained data cubes at the lowest (R$\sim$6000) and highest (R$\sim$20000) spectral resolution of MEGARA. We modelled and subtracted the stellar continuum to obtain the ionised gas contribution, and then fitted the emission lines to extract their kinematics (velocity and velocity dispersion). We identified outflows as a secondary component in the emission lines. The primary component of the emission lines was typically associated to gas in the galactic disc. For some objects, there is an enhanced-$\sigma$ region co-spatial with the secondary component. We associated it to turbulent gas produced due to the interaction with the outflows. We find signatures of outflows in six LINERs, with mass outflow rates ranging from 0.004 to 0.4 M$_{sun}$yr$^{-1}$ and energy rates from $\sim$10$^{38}$ to $\sim$10$^{40}$ erg s$^{-1}$. Their mean electronic density is 600cm$^{-3}$, extending to distances of $\sim$400 pc at an (absolute) velocity of $\sim$340 km s$^{-1}$ (on average). They tend to be compact and unresolved, although for some sources they are extended with a bubble-like morphology. Our results confirm the existence of outflows in the best LINER candidates identified using previous long-slit spectroscopic and imaging data. These outflows do not follow the scaling relations obtained for more luminous AGNs. For some objects we discuss jets as the main drivers of the outflows, Comment: 31 pages, 15 figures. Accepted for publication in Astronomy & Astrophysics

The Sloan Digital Sky Survey (SDSS) is one of the largest international astronomy organizations. We present demographic data based on surveys of its members from 2014, 2015 and 2016, during the fourth phase of SDSS (SDSS-IV). We find about half of SDSS-IV collaboration members were based in North America, a quarter in Europe, and the remainder in Asia and Central and South America. Overall, 26-36% are women (from 2014 to 2016), up to 2% report non-binary genders. 11-14% report that they are racial or ethnic minorities where they live. The fraction of women drops with seniority, and is also lower among collaboration leadership. Men in SDSS-IV were more likely to report being in a leadership role, and for the role to be funded and formally recognized. SDSS-IV collaboration members are twice as likely to have a parent with a college degree, than the general population, and are ten times more likely to have a parent with a PhD. This trend is slightly enhanced for female collaboration members. Despite this, the fraction of first generation college students (FGCS) is significant (31%). This fraction increased among collaboration members who are racial or ethnic minorities (40-50%), and decreased among women (15-25%). SDSS-IV implemented many inclusive policies and established a dedicated committee, the Committee on INclusiveness in SDSS (COINS). More than 60% of the collaboration agree that the collaboration is inclusive; however, collaboration leadership more strongly agree with this than the general membership. In this paper, we explain these results in full, including the history of inclusive efforts in SDSS-IV. We conclude with a list of suggested recommendations based on our findings, which can be used to improve equity and inclusion in large astronomical collaborations, which we argue is not only moral, but will also optimize their scientific output., Comment: 30 pages, 9 figures, accepted in PASP

Lightcurves of many classical novae deviate from the canonical "fast rise - smooth decline" pattern and display complex variability behavior. We present the first TESS-space-photometry-based investigation of this phenomenon. We use TESS Sector 41 full-frame images to extract a lightcurve of the slow Galactic nova V606 Vul that erupted nine days prior to the start of the TESS observations. The lightcurve covers the first of two major peaks of V606 Vul that was reached 19 days after the start of the eruption. The nova reached its brightest visual magnitude V=9.9 in its second peak 64 days after the eruption onset, following the completion of Sector 41 observations. To increase the confidence level of the extracted lightcurve, we performed the analysis using four different codes implementing the aperture photometry (Lightkurve, VaST) and image subtraction (TESSreduce, tequila_shots) and find good agreement between them. We performed ground-based photometric and spectroscopic monitoring to complement the TESS data. The TESS lightcurve reveals two features: periodic variations (0.12771d, 0.01mag average peak-to-peak amplitude) that disappeared when the source was within 1mag of peak optical brightness and a series of isolated mini-flares (with peak-to-peak amplitudes of up to 0.5mag) appearing at seemingly random times. We interpret the periodic variations as the result of azimuthal asymmetry of the photosphere engulfing the nova-hosting binary that was distorted by and rotating with the binary. Whereas we use spectra to associate the two major peaks in the nova lightcurve with distinct episodes of mass ejection, the origin of mini-flares remains elusive., Comment: 31 pages, 10 figures, submitted to ApJ; comments welcome

Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques require both high spatial and temporal resolution cameras, which impose bright illumination and expensive computation limitations. Event cameras offer potential advantages (high dynamic range, high temporal resolution, and data efficiency) to overcome such limitations due to their bio-inspired sensing principle. This paper presents a novel technique for perceiving air convection using events and frames by providing the first theoretical analysis that connects event data and schlieren. We formulate the problem as a variational optimization one combining the linearized event generation model with a physically-motivated parameterization that estimates the temporal derivative of the air density. The experiments with accurately aligned frame- and event camera data reveal that the proposed method enables event cameras to obtain on par results with existing frame-based optical flow techniques. Moreover, the proposed method works under dark conditions where frame-based schlieren fails, and also enables slow-motion analysis by leveraging the event camera's advantages. Our work pioneers and opens a new stack of event camera applications, as we publish the source code as well as the first schlieren dataset with high-quality frame and event data. https://github.com/tub-rip/event_based_bos, Comment: Accepted at IEEE T-PAMI