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NCORES was a large observing program on the ESO HARPS spectrograph, dedicated to measuring the masses of Neptune-like and smaller transiting planets discovered by the TESS satellite using the radial velocity technique. This paper presents an overview of the programme, its scientific goals and published results, covering 35 planets in 18 planetary systems. We present spectrally derived stellar characterisation and mass constraints for five additional TOIs where radial velocity observations found only marginally significant signals (TOI-510.01, $M_p=1.08^{+0.58}_{-0.55}M_\oplus$), or found no signal (TOIs 271.01, 641.01, 697.01 and 745.01). A newly detected non-transiting radial velocity candidate is presented orbiting TOI-510 on a 10.0d orbit, with a minimum mass of $4.82^{+1.29}_{-1.26}M_\oplus$, although uncertainties on the system architecture and true orbital period remain. Combining the NCORES sample with archival known planets we investigate the distribution of planet masses and compositions around and below the radius gap, finding that the population of planets below the gap is consistent with a rocky composition and ranges up to a sharp cut-off at $10M_\oplus$. We compare the observed distribution to models of pebble- and planetesimal-driven formation and evolution, finding good broad agreement with both models while highlighting interesting areas of potential discrepancy. Increased numbers of precisely measured planet masses in this parameter space are required to distinguish between pebble and planetesimal accretion., Comment: Accepted to MNRAS

In this Letter, we report the observation of electroluminescence (EL) at around 866 nm from n-i-n unipolar (electron-transporting) III-V GaAs nanoLEDs. The devices consist of nanopillars with top diameter of 166 nm, arranged in a 10x10 pillar array. Hole generation through impact ionization and Zener tunnelling is achieved by incorporating an AlAs/GaAs/AlAs double-barrier quantum well within the epilayer structure of the n-i-n diode. Time-resolved EL measurements reveal decay lifetimes larger than 300 ps, allowing us to estimate an internal quantum efficiency (IQE) higher than 2 per cent at sub-mA current injection. These results demonstrate the potential for a new class of n-type nanoscale light-emitting devices., Comment: 7 pages, 5 figures, 7 suplementary figures

Mutation testing may be used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy., Comment: Accept for publication in the Journal of System and Software - JSS. This work is partially supported by Brazilian Funding Agencies FAPESP (Grant n. 2019/23160-0 and 2023/00001-9), CAPES, and CNPq

Wind and solar generation constitute an increasing share of electricity supply globally. We find that this leads to shifts in the operational dynamics of thermal power plants. Using fixed effects panel regression across seven major U.S. balancing authorities, we analyze the impact of renewable generation on coal, natural gas combined cycle plants, and natural gas combustion turbines. Wind generation consistently displaces thermal output, while effects from solar vary significantly by region, achieving substantial displacement in areas with high solar penetration such as the California Independent System Operator but limited impacts in coal reliant grids such as the Midcontinent Independent System Operator. Renewable energy sources effectively reduce carbon dioxide emissions in regions with flexible thermal plants, achieving displacement effectiveness as high as one hundred and two percent in the California Independent System Operator and the Electric Reliability Council of Texas. However, in coal heavy areas such as the Midcontinent Independent System Operator and the Pennsylvania New Jersey Maryland Interconnection, inefficiencies from ramping and cycling reduce carbon dioxide displacement to as low as seventeen percent and often lead to elevated nitrogen oxides and sulfur dioxide emissions. These findings underscore the critical role of grid design, fuel mix, and operational flexibility in shaping the emissions benefits of renewables. Targeted interventions, including retrofitting high emitting plants and deploying energy storage, are essential to maximize emissions reductions and support the decarbonization of electricity systems.

A new and detailed measurement of the cross section for hard exclusive neutral-pion muoproduction on the proton was performed in a wide kinematic region, with the photon virtuality $Q^2$ ranging from 1 to 8 (GeV/$c$)$^{\rm\, 2}$ and the Bjorken variable $x_{\rm Bj}$ ranging from 0.02 to 0.45. The data were collected at COMPASS at CERN using 160 GeV/$c$ longitudinally polarised $\mu^+$ and $\mu^-$ beams scattering off a 2.5 m long liquid hydrogen target. From the average of the measured $\mu^+$ and $\mu^-$ cross sections, the virtual-photon--proton cross section is determined as a function of the squared four-momentum transfer between the initial and final state proton in the range 0.08 (GeV/$c$)$^{\rm\, 2}$ $< |t| <$ 0.64 (GeV/$c$)$^{\rm\, 2}$. From its angular distribution, the combined contribution of transversely and longitudinally polarised photons are determined, as well as transverse--transverse and longitudinal--transverse interference contributions. They are studied as functions of four-momentum transfer $|t|$, photon virtuality $Q^2$ and virtual-photon energy $\nu$. The longitudinal--transverse interference contribution is found to be compatible with zero. The significant transverse--transverse interference contribution reveals the existence of a dominant contribution by transversely polarized photons. This provides clear experimental evidence for the chiral-odd GPD $\overline{E}_T$. In addition, the existence of a non-negligible contribution of longitudinally polarized photons is suggested by the $|t|$-dependence of the cross section at $x_{\rm Bj} < $ 0.1 . Altogether, these results provide valuable input for future modelling of GPDs and thus of cross sections for exclusive pseudo-scalar meson production. Furthermore, they can be expected to facilitate the study of next-to-leading order corrections and higher-twist contributions.

Very recently, the two-photon decay width of the $\eta_b$ meson was computed with lattice QCD methods. This decay has not yet been measured. The knowledge of this width allows for the calculation of the $\eta_b$ production cross section through photon-photon interactions in ultra-peripheral $PbPb$ collisions. In this work we present this calculation, which is the first application of the lattice result. Since UPCs are gaining an increasing attention of the heavy ion community, we take the opportunity to perform a comprehensive study of the different ways of defining ultra-peripheral collisions and of the different ways to treat the equivalent photon flux., Comment: 8 pages, 4 figures

Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election Day represents one such critical scenario, characterized by intensified market volatility, as the winning candidate's policies significantly impact various economic sectors and companies. To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model. Our approach leverages the contextual capabilities of large language models alongside specialized agents designed to analyze the political and economic consequences of elections. By building on a state-of-the-art architecture, we demonstrate that EDSMF improves the predictive performance of the S&P 500 during this uniquely volatile day.

Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions' effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images., Comment: \copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

The role of the unitary transformations in the Bell-CHSH inequality is highlighted, in both Quantum Mechanics and relativistic Quantum Field Theory. In the former case, we discuss, through a few examples, how the violation of the Bell-CHSH inequality can be encoded into unitary transformations acting on a given reference set of Bell's observables. In the latter case, the Bell-CHSH inequality for a real massive scalar field in the vacuum state is considered. After introducing suitable bounded Hermitian operators, we show that using unitary transformations can significantly improve our previous numerical algorithm, leading to an increase in the size of the violation., Comment: 15 pages, two figures

We prove that the equational theory of Kleene algebra with commutativity conditions on primitives (or atomic terms) is undecidable, thereby settling a longstanding open question in the theory of Kleene algebra. While this question has also been recently solved independently by Kuznetsov, our results hold even for weaker theories that do not support the induction axioms of Kleene algebra., Comment: Published at CSL 2025

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study systematically maps the literature on the use of LLMs for qualitative research, exploring their application contexts, configurations, methodologies, and evaluation metrics. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes traditionally requiring extensive human input. However, challenges such as reliance on prompt engineering, occasional inaccuracies, and contextual limitations remain significant barriers. This research highlights opportunities for integrating LLMs with human expertise, improving model robustness, and refining evaluation methodologies. By synthesizing trends and identifying research gaps, this study aims to guide future innovations in the application of LLMs for qualitative analysis., Comment: 8 pages, includes 1 figures and 3 tables. Submitted to the WSESE 2025 ICSE Workshop

Reinforcement learning (RL) has been proven to be an effective and robust method for training neural machine translation systems, especially when paired with powerful reward models that accurately assess translation quality. However, most research has focused on RL methods that use sentence-level feedback, which leads to inefficient learning signals due to the reward sparsity problem -- the model receives a single score for the entire sentence. To address this, we introduce a novel approach that leverages fine-grained token-level reward mechanisms with RL methods. We use xCOMET, a state-of-the-art quality estimation system as our token-level reward model. xCOMET provides detailed feedback by predicting fine-grained error spans and their severity given source-translation pairs. We conduct experiments on small and large translation datasets to compare the impact of sentence-level versus fine-grained reward signals on translation quality. Our results show that training with token-level rewards improves translation quality across language pairs over baselines according to automatic and human evaluation. Furthermore, token-level reward optimization also improves training stability, evidenced by a steady increase in mean rewards over training epochs., Comment: 10 pages, work-in-progress

Variations in scaling behavior in the flux and emissions of gravitational lensed quasars can provide valuable information about the dynamics within the sources and their cosmological evolution with time. Here, we study the multifractal behavior of the light curves of 14 lensed quasars with multiple images in the $r$ band, with redshift ranging from 0.657 to 2.730, in the search for potential differences in nonlinearity between the signals of the quasar multiple images. Among these lensed systems, nine present two images, two present three images, and three present four images. To this end, we apply the wavelet transform-based multifractal analysis formalism called Wavelet Transform Modulus Maxima (WTMM). We identify strong multifractal signatures in the light curves of the images of all analyzed lensed quasar systems, independently of the number of images, with a significant difference between the degree of multifractality of all the images and combinations. We have also searched for a possible connection between the degree of multifractality and the characteristic parameters related to the quasar source and the lensing galaxy. These parameters include the Einstein ring radius and the accretion disk size and the characteristic timescales related to microlensing variability. The analysis reveals some apparent trends, pointing to a decrease in the degree of multifractality with the increase of the quasar's source size and timescale. Using a larger sample and following a similar approach, the present study confirms a previous finding for the quasar Q0957+561., Comment: 12 pages, 7 figures, 3 tables, accepted for publication in MNRAS

Social dilemmas where the good of a group is at odds with individual interests are usually considered as static -- the dilemma does not change over time. In the COVID-19 pandemic, social dilemmas occurred in the mitigation of epidemic spread: Should I reduce my contacts or wear a mask to protect others? In the context of respiratory diseases, which are predominantly spreading during the winter months, some of these situations re-occur seasonally. We couple a game theoretical model, where individuals can adjust their behavior, to an epidemiological model with seasonal forcing. We find that social dilemmas can occur annually and that behavioral reactions to them can either decrease or increase the peaks of infections in a population. Our work has not only implications for seasonal infectious diseases, but also more generally for oscillatory social dilemmas: A complex interdependence between behavior and external dynamics emerges. To be effective and to exploit behavioral dynamics, intervention measures to mitigate re-occuring social dilemmas have to be timed carefully.

It is shown how spin one vector matter fields can be coupled to a Yang-Mills theory. Such matter fields are defined as belonging to a representation $R$ of this Yang-Mills gauge algebra $\mathfrak{g}$. It is also required that these fields together with the original gauge fields be the gauge fields of an embedding total gauge algebra $\mathfrak{g}_{\rm tot}$. The existence of a physically consistent Yang-Mills action for the total algebra is finally required. These conditions are rather restrictive, as shown in some examples: non-trivial solutions may or may not exist depending on the choice of the original algebra $\mathfrak{g}$ and of the representation $R$. The case of the initial algebra $\mathfrak{g}$ = $\mathfrak{u}(1)\oplus\mathfrak{su}(2)$ is treated in more detail., Comment: Latex, 21 pages, 2 ancillary files: "Example_G=U1xSU2.nb" and "Example_G=U1xSU2.pdf". Added references, added commentaries in the Introduction and the Conclusion, small changes in the text, results unchanged

The multiplicities of positive and negative pions, kaons and unidentified hadrons produced in deep-inelastic scattering are measured in bins of the Bjorken scaling variable $x$, the relative virtual-photon energy $y$ and the fraction of the virtual-photon energy transferred to the final-state hadron $z$. Data were obtained by the COMPASS Collaboration using a 160 GeV muon beam of both electric charges and a liquid hydrogen target. These measurements cover the kinematic domain with photon virtuality $Q^2 > 1$ (GeV/$c)^2$, $0.004 < x < 0.4$, $0.1 < y < 0.7$ and $0.2 < z < 0.85$, in accordance with the kinematic domain used in earlier published COMPASS multiplicity measurements with an isoscalar target. The calculation of radiative corrections was improved by using the Monte Carlo generator DJANGOH, which results in up to 12\% larger corrections in the low-$x$ region., Comment: 19 pages, 29 figures

Electronic and optical properties of a mesoscopic heterostructure of a two-dimensional quantum ring composed of Gallium Arsenide (GaAs) semiconductors are investigated. Using the confinement potential proposed by Tan-Inkson to describe the system under analysis, we conducted a numerical study of the photoionization cross section for a 2D quantum ring with and without rotation effects. The interior of the quantum ring is traversed by an AB flux. Our research aims to investigate how this mesoscopic structure's electronic and optical properties respond to variations in the following parameters: average radius, AB flux, angular velocity, and incident photon energy. Under these conditions, we establish that optical transitions occur from the ground state to the next excited state in the conduction sub-band, following a specific selection rule. One of the fundamental objectives of this study is to analyze how these rules can influence the general properties of two-dimensional quantum rings. To clarify the influence of rotation on the photoionization process within the system, we offer findings that illuminate the effects of the pertinent physical parameters within the described model. We emphasize that, although this is a review, it provides critical commentary, analysis, and new perspectives on existing research. Some results presented in this paper can be compared with those in the literature; however, new physical parameters and quantum ring configurations are used., Comment: 38 pages, 21 figures

This study describes and evaluates i-GRIP, a novel movement intention estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upper limb impairments. Operating within a collaborative grasping control paradigm, the users naturally move their hand towards an object they wish to grasp and i-GRIP identifies the target of the movement and selects an appropriate grip for the assistive device to perform. In an experimental study involving 11 healthy participants, i-GRIP exhibited promising estimation performances and responsiveness. The proposed approach paves the way towards more intuitive control of grasping assistive device for individuals with upper limb impairments., Comment: International Conference on Humanoid Robots, IEEE-RAS, Nov 2024, Nancy, France

Thanks to their short orbital periods and hot extended atmospheres, hot Jupiters are ideal candidates for atmosphere studies with high-resolution spectroscopy. New stable spectrographs help improve our understanding of the evolution and composition of those types of planets. By analyzing two nights of observations using the ESPRESSO high-resolution spectrograph, we studied the architecture and atmosphere of hot Jupiter WASP-122b (KELT-14b). By analyzing the Rossiter-McLaughlin (RM) effect, we measured the spin-orbit angle of the system to be lambda = 0.09 +0.88/-0.90 deg. This result is in line with literature obliquity measurements of planetary systems around stars with effective temperatures cooler than 6500 K. Using the transmission spectroscopy, we studied the atmosphere of the planet. Applying both the single-line analysis and the cross-correlation method, we looked for Ca I, Cr I, FeH, Fe I, Fe II, H2O, Li I, Mg I, Na I, Ti I, TiO, V I, VO, and Y I. Our results show no evidence of any of these species in WASP-122b's atmosphere. The lack of significant detections can be explained by either the RM effect covering the regions where the atmospheric signal is expected and masking it, along with the low signal-to-noise ratio (S/N) of the observations or the absence of the relevant species in its atmosphere., Comment: Accepted for publication in A&A, 19 pages, 17 figures, 5 tables

This study introduces i-GRIP, an innovative movement goal estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upperlimb impairments. The algorithm operates within a collaborative control paradigm, eliminating the need for specific user actions apart from naturally moving their hand toward a desired object. i-GRIP analyzes the hand's movement in an object-populated scene to determine its target and select an appropriate grip. In an experimental study involving 11 healthy participants, i-GRIP showed promising goal estimation performances and responsiveness and the potential to facilitate the daily use of assistive devices for individuals with upper-limb impairments in the future.

Ultra hot Jupiters (gas giants, Teq>2000 K) are intriguing exoplanets due to their extreme atmospheres. Their torrid daysides can be characterised using ground-based high-resolution emission spectroscopy. We search for signatures of neutral and singly ionised iron (Fe I and Fe II) in the dayside of the ultra hot Jupiter WASP-76 b, as these species were detected via transmission spectroscopy in this exoplanet. Furthermore, we aim to confirm the existence of a thermal inversion layer, which has been reported in previous studies, and attempt to constrain its properties. We observed WASP-76 b on four epochs with ESPRESSO at the VLT, at orbital phases shortly before and after the secondary transit, when the dayside is in view. We present the first analysis of high-resolution optical emission spectra for this exoplanet. We compare the data to synthetic templates from petitRADTRANS, using cross-correlation function techniques. We detect a blueshifted (-4.7+-0.3 km/s) Fe I emission signature on the dayside of WASP-76 b at 6.0-sigma. The signal is detected independently both before and after the eclipse, and blueshifted in both cases. The presence of iron emission features confirms the existence of a thermal inversion layer. Fe II was not detected, possibly because this species is located in the upper layers of the atmosphere, which are more optically thin. Thus the Fe II signature on the dayside of WASP-76 b is too weak to be detected with emission spectroscopy. We propose that the blueshifted Fe I signature is created by material rising from the hot spot to the upper layers of the atmosphere, and discuss possible scenarios related to the position of the hotspot. This work unveils some of the dynamic processes ongoing on the dayside of WASP-76 b through the analysis of the Fe I signature from its atmosphere, and complements previous knowledge obtained from transmission studies., Comment: 15 pages, 8 figures + 2 figures in appendix. Abstract slightly abridged. Published in Astronomy & Astrophysics

In this paper, we investigate the spacetime containing both small-scale structures (wiggles) and spatial dislocation, forming a wiggly cosmic dislocation. We study the combined effects of these features on the dynamics of massive and massless particles. Our results show that while wiggles alone lead to bound states and dislocation introduces angular momentum corrections, their coupling produces more complex effects, influencing both particle motion and wave propagation. Notably, this coupling significantly modifies radial solutions and eigenvalues, with the direction of motion or propagation becoming a critical factor in determining the outcomes. Numerical solutions reveal detailed aspects of particle dynamics as functions of dislocation and string parameters, including plots of trajectories, radial probability densities, and energy levels. These findings deepen our understanding of how a wiggly cosmic dislocation shapes particle dynamics, suggesting new directions for theoretical exploration in cosmological models., Comment: 12 pages, 1 table, 9 figures

As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations. The integration of renewable energy sources is particularly challenging due to the operational difficulties of managing their power generation, which is highly variable compared to fossil fuel sources, delaying the availability of clean energy. To mitigate this, we quantify the impact of increasing intermittent generation from wind and solar on thermal power plants in Chile and introduce a hybrid wind speed forecasting methodology which combines two custom ML models for Chile. The first model is based on TiDE, an MLP-based ML model for short-term forecasts, and the second is based on a graph neural network, GraphCast, for medium-term forecasts up to 10 days. Our hybrid approach outperforms the most accurate operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts and can directly lower the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile.

The accuracy of high precision and fundamental measurements of atomic transition frequencies via laser spectroscopy depends upon fitting the spectral data with a lineshape. With atomic hydrogen and antihydrogen 1S-2S two-photon spectroscopy, computer intensive Monte-Carlo simulations have been used to compute the optical Bloch equations in order to match and interpret the experimental spectra. For the highest resolutions, one tries to minimize saturation effects going to regimes of low excitation probability, where perturbation theory can provide reliable results. Here we describe an analytical approach to the lineshape based on perturbation theory accounting for the AC-Stark shift and ionization. The expressions can be used for beam experiments or integrated over the magnetic field profile for a trapped sample. Theses lineshapes, providing fast results, allow for studies of many systematic effects that influence the accuracy of the determination of the central frequency. This development has relevance to hydrogen beam experiments and to trapped hydrogen and antihydrogen, as developed by the ALPHA collaboration at CERN, for tests of the CPT-symmetry and the highest accuracy measurement on antimatter., Comment: 11 pages, 5 figures

We propose two new kinds of infinite resistor networks based on the Fibonacci sequence: a serial association of resistor sets connected in parallel (type 1) or a parallel association of resistor sets connected in series (type 2). We show that the sequence of the network's equivalent resistance converges uniformly in the parameter $\alpha=\frac{r_2}{r_1} \in [0,+\infty)$, where $r_1$ and $r_2$ are the first and second resistors in the network. We also show that these networks exhibit self-similarity and scale invariance, which mimics a self-similar fractal. We also provide some generalizations, including resistor networks based on high-order Fibonacci sequences and other recursive combinatorial sequences.

With the expansion of cities over time, URT (Urban Rail Transit) networks have also grown significantly. Demand prediction plays an important role in supporting planning, scheduling, fleet management, and other operational decisions. In this study, we propose an Origin-Destination (OD) demand prediction model called Multi-Graph Inductive Representation Learning (mGraphSAGE) for large-scale URT networks under operational uncertainties. Our main contributions are twofold: we enhance prediction results while ensuring scalability for large networks by relying simultaneously on multiple graphs, where each OD pair is a node on a graph and distinct OD relationships, such as temporal and spatial correlations; we show the importance of including operational uncertainties such as train delays and cancellations as inputs in demand prediction for daily operations. The model is validated on three different scales of the URT network in Copenhagen, Denmark. Experimental results show that by leveraging information from neighboring ODs and learning node representations via sampling and aggregation, mGraphSAGE is particularly suitable for OD demand prediction in large-scale URT networks, outperforming reference machine learning methods. Furthermore, during periods with train cancellations and delays, the performance gap between mGraphSAGE and other methods improves compared to normal operating conditions, demonstrating its ability to leverage system reliability information for predicting OD demand under uncertainty., Comment: 18 pages, 3 figures

Translating written sentences from oral languages to a sequence of manual and non-manual gestures plays a crucial role in building a more inclusive society for deaf and hard-of-hearing people. Facial expressions (non-manual), in particular, are responsible for encoding the grammar of the sentence to be spoken, applying punctuation, pronouns, or emphasizing signs. These non-manual gestures are closely related to the semantics of the sentence being spoken and also to the utterance of the speaker's emotions. However, most Sign Language Production (SLP) approaches are centered on synthesizing manual gestures and do not focus on modeling the speakers expression. This paper introduces a new method focused in synthesizing facial expressions for sign language. Our goal is to improve sign language production by integrating sentiment information in facial expression generation. The approach leverages a sentence sentiment and semantic features to sample from a meaningful representation space, integrating the bias of the non-manual components into the sign language production process. To evaluate our method, we extend the Frechet Gesture Distance (FGD) and propose a new metric called Frechet Expression Distance (FED) and apply an extensive set of metrics to assess the quality of specific regions of the face. The experimental results showed that our method achieved state of the art, being superior to the competitors on How2Sign and PHOENIX14T datasets. Moreover, our architecture is based on a carefully designed graph pyramid that makes it simpler, easier to train, and capable of leveraging emotions to produce facial expressions.

Wind and solar electricity generation account for 14% of total electricity generation in the United States and are expected to continue to grow in the next decades. In low carbon systems, generation from renewable energy sources displaces conventional fossil fuel power plants resulting in lower system-level emissions and emissions intensity. However, we find that intermittent generation from renewables changes the way conventional thermal power plants operate, and that the displacement of generation is not 1 to 1 as expected. Our work provides a method that allows policy and decision makers to continue to track the effect of additional renewable capacity and the resulting thermal power plant operational responses.

[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE's practical application and its perception among practitioners. [Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry. [Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures. [Results] Our findings highlight distinct RE implementation aspects in Brazil's ML projects. For instance, (i) RE-related tasks are predominantly conducted by data scientists; (ii) the most common techniques for eliciting requirements are interviews and workshop meetings; (iii) there is a prevalence of interactive notebooks in requirements documentation; (iv) practitioners report problems that include a poor understanding of the problem to solve and the business domain, low customer engagement, and difficulties managing stakeholders expectations. [Conclusion] These results provide an understanding of RE-related practices in the Brazilian ML industry, helping to guide research toward improving the maturity of RE for ML-enabled systems., Comment: arXiv admin note: substantial text overlap with arXiv:2310.06726

For the binary regression, the use of symmetrical link functions are not appropriate when we have evidence that the probability of success increases at a different rate than decreases. In these cases, the use of link functions based on the cumulative distribution function of a skewed and heavy tailed distribution can be useful. The most popular choice is some scale mixtures of skew-normal distribution. This family of distributions can have some identifiability problems, caused by the so-called direct parameterization. Also, in the binary modeling with skewed link functions, we can have another identifiability problem caused by the presence of the intercept and the skewness parameter. To circumvent these issues, in this work we proposed link functions based on the scale mixtures of skew-normal distributions under the centered parameterization. Furthermore, we proposed to fix the sign of the skewness parameter, which is a new perspective in the literature to deal with the identifiability problem in skewed link functions. Bayesian inference using MCMC algorithms and residual analysis are developed. Simulation studies are performed to evaluate the performance of the model. Also, the methodology is applied in a heart disease data.

In news recommendation systems, reducing popularity bias is essential for delivering accurate and diverse recommendations. This paper presents POPK, a new method that uses temporal-counterfactual analysis to mitigate the influence of popular news articles. By asking, "What if, at a given time $t$, a set of popular news articles were competing for the user's attention to be clicked?", POPK aims to improve recommendation accuracy and diversity. We tested POPK on three different language datasets (Japanese, English, and Norwegian) and found that it successfully enhances traditional methods. POPK offers flexibility for customization to enhance either accuracy or diversity, alongside providing distinct ways of measuring popularity. We argue that popular news articles always compete for attention, even if they are not explicitly present in the user's impression list. POPK systematically eliminates the implicit influence of popular news articles during each training step. We combine counterfactual reasoning with a temporal approach to adjust the negative sample space, refining understanding of user interests. Our findings underscore how POPK effectively enhances the accuracy and diversity of recommended articles while also tailoring the approach to specific needs.

In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by the rise of recommender systems. Our research indicates that recommender systems should consider avoidance as a fundamental factor. We argue that news articles can be characterized by three principal elements: exposure, relevance, and avoidance, all of which are closely interconnected. To address these challenges, we introduce AWRS, an Avoidance-Aware Recommender System. This framework incorporates avoidance awareness when recommending news, based on the premise that news article avoidance conveys significant information about user preferences. Evaluation results on three news datasets in different languages (English, Norwegian, and Japanese) demonstrate that our method outperforms existing approaches., Comment: SIAM International Conference on Data Mining (SDM25)

Multi-controlled gates are fundamental components in the design of quantum algorithms, where efficient decompositions of these operators can enhance algorithm performance. The best asymptotic decomposition of an n-controlled X gate with one borrowed ancilla into single qubit and CNOT gates produces circuits with degree 3 polylogarithmic depth and employs a divide-and-conquer strategy. In this paper, we reduce the number of recursive calls in the divide-and-conquer algorithm and decrease the depth of n-controlled X gate decomposition to a degree of 2.799 polylogarithmic depth. With this optimized decomposition, we also reduce the depth of n-controlled SU(2) gates and approximate n-controlled U(2) gates. Decompositions described in this work achieve the lowest asymptotic depth reported in the literature. We also perform an optimization in the base of the recursive approach. Starting at 52 control qubits, the proposed n-controlled X gate with one borrowed ancilla has the shortest circuit depth in the literature. One can reproduce all the results with the freely available open-source code provided in a public repository., Comment: 6 pages, 8 figures

We here report on the progress of the HHH Workshop, that took place in Dubrovnik in July 2023. After the discovery of a particle that complies with the properties of the Higgs boson of the Standard Model, all SM parameters are in principle determined. However, in order to verify or falsify the model, the full form of the potential has to be determined. This includes the measurement of the triple and quartic scalar couplings. We here report on ongoing progress of measurements for multi scalar final states, with an emphasis on three SM-like scalar bosons at 125 GeV, but also mentioning other options. We discuss both experimental progress and challenges as well as theoretical studies and models that can enhance such rates with respect to the SM predictions., Comment: 117 pages, 56 figures; Whitepaper resulting from HHH Workshop in Dubrovnik 2023, https://indico.cern.ch/event/1232581/; v2: small typos corrected

The boron nitride (BN) analogue of 8-16-4 graphyne, termed SBNyne, is proposed for the first time. Its physical properties were explored using first-principles calculations and classical molecular dynamics (MD) simulations. Thermal stability assessments reveal that SBNyne maintains structural integrity up to 1000 K. We found that SBNyne exhibits a wide indirect bandgap of 4.58 eV using HSE06 and 3.20 eV using PBE. It displays strong optical absorption in the ultraviolet region while remaining transparent in the infrared and visible regions. Additionally, SBNyne exhibits significantly lower thermal conductivity compared to h-BN. Phonon spectrum analysis indicates that out-of-plane phonons predominantly contribute to the vibrational density of states only at very low frequencies, explaining its low thermal conductivity. These findings expand the knowledge of BN-based 2D materials and open new avenues for their design and advanced technological applications., Comment: We have reviewed the thermal stability calculation and found that the SBNyne structure is metastable and undergoes a transition to a new phase. We are currently investigating this new phase, and to avoid misunderstandings, we need to remove the preprint

The Yang-Mills-Chern-Simons theory in three-dimensional Minkowski space-time is studied in a gauge-fixing scheme which interpolates between the covariant gauge and light-cone gauge, the interpolating gauge-fixing. The ultraviolet finiteness of the theory is proved via the Becchi-Rouet-Stora (BRS) algebraic renormalization procedure, which allows us to demonstrate the vanishing of all $\beta$-functions and all anomalous dimensions to all orders in perturbation theory., Comment: 6 pages

We search for atmospheric constituents for the UHJ WASP-178 b with two ESPRESSO transits using the narrow-band and cross-correlation techniques, focusing on the detections of NaI, H$\alpha$, H$\beta$, H$\gamma$, MgI, FeI and FeII. Additionally, we show parallel photometry used to obtain updated and precise stellar, planetary and orbital parameters. We report the resolved line detections of NaI (5.5 and 5.4 $\sigma$), H$\alpha$ (13 $\sigma$), H$\beta$ (7.1 $\sigma$), and tentatively MgI (4.6 $\sigma$). In cross-correlation, we confirm the MgI detection (7.8 and 5.8 $\sigma$) and additionally report the detections of FeI (12 and 10 $\sigma$) and FeII (11 and 8.4 $\sigma$), on both nights separately. The detection of MgI remains tentative, however, due to the differing results between both nights, as well as compared with the narrow-band derived properties. None of our resolved spectral lines probing the mid- to upper atmosphere show significant shifts relative to the planetary rest frame, however H$\alpha$ and H$\beta$ exhibit line broadenings of 39.6 $\pm$ 2.1 km/s and 27.6 $\pm$ 4.6 km/s, respectively, indicating the onset of possible escape. WASP-178 b differs from similar UHJ with its lack of strong atmospheric dynamics in the upper atmosphere, however the broadening seen for FeI (15.66 $\pm$ 0.58 km/s) and FeII (11.32 $\pm$ 0.52 km/s) could indicate the presence of winds in the mid-atmosphere. Future studies on the impact of the flux variability caused by the host star activity might shed more light on the subject. Previous work indicated the presence of SiO cloud-precursors in the atmosphere of WASP-178 b and a lack of MgI and FeII. However, our results suggest that a scenario where the planetary atmosphere is dominated by MgI and FeII is more likely. In light of our results, we encourage future observations to further elucidate these atmospheric properties., Comment: 18 pages, 24 figures, accepted for publication in A&A

This study delves into the fundamental properties of graphene and boron nitride (BN) nanostructures, exploring their torsional energy characteristics within the framework of Teleparallel Equivalent of General Relativity (TEGR). By constructing nanocones with disclination defects in these materials, we investigate the linear dependence of torsional energy on the disclination angle, as predicted by TEGR. The qualitative validation of TEGR's energy expression is supported by our simulations, which show a strong correlation between the torsional energy and the disclination angle, consistent with the theoretical predictions. Furthermore, we propose a quantitative analysis by estimating the coupling constant $k$ associated with TEGR through molecular simulations and Density Functional Theory (DFT) calculations. Our results suggest that $k$ reflects the interatomic forces within the materials, providing insights into the nature of spacetime and gravitational interactions on a microscopic scale. These findings not only contribute to our understanding of material physics but also offer implications for the precision and validity of TEGR in describing gravitational phenomena., Comment: 13 pages, 6 figures. Comments are welcome

We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D S\'ersic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys., Comment: 25 pages, 22 figures, accepted for publication in MNRAS