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A Fabry–Perot interferometer (FPI) installed at El Leoncito Observatory, Argentina (31.8^oS, 69.3^oW, 18^omag lat), provides data on neutral winds and temperature over southern South America, a region lacking ground-based thermospheric observations. We present the climatology of neutral winds obtained from 630.0 nm airglow emissions. Results are shown for different seasons and different levels of solar activity. Temperature results are shown only for medium-to-high solar activity conditions. The modeled neutral winds show better agreement during high solar activity conditions. The modeled temperatures underestimate the observed values and do not reproduce the midnight temperature maximum observations. These observations will help improve model predictions of thermospheric parameters for this region. 

This study investigates how high school-aged youth engage in algorithm auditing to identify and understand biases in artificial intelligence and machine learning (AI/ML) tools they encounter daily. With AI/ML technologies being increasingly integrated into young people’s lives, there is an urgent need to equip teenagers with AI literacies that build both technical knowledge and awareness of social impacts. Algorithm audits (also called AI audits) have traditionally been employed by experts to assess potential harmful biases, but recent research suggests that non-expert users can also participate productively in auditing. We conducted a two-week participatory design workshop with 14 teenagers (ages 14–15), where they audited the generative AI model behind TikTok’s Effect House, a tool for creating interactive TikTok filters. We present a case study describing how teenagers approached the audit, from deciding what to audit to analyzing data using diverse strategies and communicating their results. Our findings show that participants were engaged and creative throughout the activities, independently raising and exploring new considerations, such as age-related biases, that are uncommon in professional audits. We drew on our expertise in algorithm auditing to triangulate their findings as a way to examine if the workshop supported participants to reach coherent conclusions in their audit. Although the resulting number of changes in race, gender, and age representation uncovered by the teens were slightly different from ours, we reached similar conclusions. This study highlights the potential for auditing to inspire learning activities to foster AI literacies, empower teenagers to critically examine AI systems, and contribute fresh perspectives to the study of algorithmic harms. 

Today’s youth have extensive experience interacting with artificial intelligence and machine learning applications on popular social media platforms, putting youth in a unique position to examine, evaluate, and even challenge these applications. Algorithm auditing is a promising candidate for connecting youth’s everyday practices in using AI applications with more formal scientific literacies (i.e., syncretic designs). In this paper, we analyze high school youth participants’ everyday algorithm auditing practices when interacting with generative AI filters on TikTok, revealing thorough and extensive examinations, with youth rapidly testing filters with sophisticated camera variations and facial manipulations to identify filter limitations. In the discussion, we address how these findings can provide a foundation for developing designs that bring together everyday and more formal algorithm auditing. 

While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing—a method for understanding algorithmic systems’ opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study. 

Abstract The Mid‐latitude All‐sky‐imaging Network for Geophysical Observations (MANGO) employs a combination of two powerful optical techniques used to observe the dynamics of Earth's upper atmosphere: wide‐field imaging and high‐resolution spectral interferometry. Both techniques observe the naturally occurring airglow emissions produced in the upper atmosphere at 630.0‐ and 557.7‐nm wavelengths. Instruments are deployed to sites across the continental United States, providing the capability to make measurements spanning mid to sub‐auroral latitudes. The current instrument suite in MANGO has six all‐sky imagers (ASIs) observing the 630.0‐nm emission (integrated between ∼200 and 400 km altitude), six ASIs observing the 557.7‐nm emission (integrated between ∼90 and 100 km altitude), and four Fabry‐Perot interferometers measuring neutral winds and temperature at these wavelengths. The deployment of additional imagers is planned. The network makes unprecedented observations of the nighttime thermosphere‐ionosphere dynamics with the expanded field‐of‐view provided by the distributed network of instruments. This paper describes the network, the instruments, the data products, and first results from this effort. 

Abstract Midlatitude thermospheric wind observations from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging on board the Ionospheric Connections Explorer (ICON/MIGHTI) and from the ground‐based Boulder, Urbana, Millstone Hill and Morocco Fabry‐Perot interferometers (FPIs) are used to study a distinct solar local time (SLT) evolution in the nighttime wind field around the December solstice period. Our results show, to the best of our knowledge for the first time, strong non‐migrating tides in midlatitude thermospheric winds using coincident from different observing platforms. These observations exhibited a structure of strong (∼50–150 m/s) eastward and southward winds in the pre‐midnight sector (20:00–23:00 SLT) and in the post‐midnight sector (02:00–03:00 SLT), with a strong suppression around midnight. Tidal analysis of ICON/MIGHTI data revealed that the signature before midnight was driven by diurnal (D0, DE1, DE2, DW2) and semidiurnal (SE2, SE3, SW1, SW4) tides, and that strong terdiurnal (TE2, TW1, TW2, TW5) and quatradiurnal (QW2, QW3, QW6) tides were important contributors in the mid‐ and post‐midnight sectors. ICON/MIGHTI tidal reconstructions successfully reproduced the salient structures observed by the FPI and showed a longitudinal dual‐peak variation with peak magnitudes around 200°–120°W and 30°W–60°E. The signature of the structure extended along the south‐to‐north direction from lower latitudes, migrated to earlier local times with increasing latitude, and strengthened above 30°N. Tidal analysis using historical FPI data revealed that these structures were often seen during previous December solstices, and that they are much stronger for lower solar flux conditions, consistent with an upward‐propagating tidal origin. 

Mindsets play an important role in persevering in computer science: while some learners perceive bugs as opportunities for learning, others become frustrated with failure and see it as a challenge to their abilities. Yet few studies and interventions take into account the motivational and emotional aspects of debugging and how learning environments can actively promote growth mindsets. In this paper, we discuss growth mindset practices that students exhibited in “Debugging by Design,” an intervention created to empower students in debugging—by designing e-textiles projects with bugs for their peers to solve. Drawing on observations of four student groups in a high school classroom over a period of eight hours, we examine the practices students exhibited that demonstrate the development of growth mindset, and the contexts where these practices emerged. We discuss how our design-focused, practice-first approach may be particularly well suited for promoting growth mindset in domains such as computer science. 

Abstract The ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report. 

Abstract A search for leptoquark pair production decaying into$$te^- \bar{t}e^+$$ $t{e}^{-}\overline{t}{e}^{+}$or$$t\mu ^- \bar{t}\mu ^+$$ $t{\mu }^{-}\overline{t}{\mu }^{+}$in final states with multiple leptons is presented. The search is based on a dataset ofppcollisions at$$\sqrt{s}=13~\text {TeV} $$ $\sqrt{s}=13\text{TeV}$recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$$^{-1}$$ $_{}^{-1}$. Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from ab-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into$$te^{-}$$ $t{e}^{-}$($$t\mu ^{-}$$ $t{\mu }^{-}$), the corresponding lower limit on the scalar mixed-generation leptoquark mass$$m_{\textrm{LQ}_{\textrm{mix}}^{\textrm{d}}}$$ $m_{{\text{LQ}}_{\text{mix}}^{\text{d}}}$is at 1.58 (1.59) TeV and on the vector leptoquark mass$$m_{{\tilde{U}}_1}$$ $m_{{\tilde{U}}_1}$at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario. 

Abstract A search is reported for long-lived dark photons with masses between 0.1 GeV and 15 GeV, from exotic decays of Higgs bosons produced via vector-boson-fusion. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are probed. This search uses the full LHC Run 2 (2015–2018) data sample collected in proton–proton collisions at$$\sqrt{s}=13$$ $\sqrt{s}=13$TeV, corresponding to an integrated luminosity of 139$$fb^{-1}$$ $f{b}^{-1}$. Dominant backgrounds from Standard Model processes and non-collision sources are estimated using data-driven techniques. The observed event yields in the signal regions are consistent with the expected background. Upper limits on the Higgs boson to dark photon branching fraction are reported as a function of the dark photon mean proper decay length or of the dark photon mass and the coupling between the Standard Model and the potential dark sector. This search is combined with previous ATLAS searches obtained in the gluon–gluon fusion andWHproduction modes. A branching fraction above 10% is excluded at 95% CL for a 125 GeV Higgs boson decaying into two dark photons for dark photon mean proper decay lengths between 173 and 1296 mm and mass of 10 GeV.