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Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high resolution spatial scales (higher than 4 km), where convection plays a significant role. Physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the WRF model as CPM to the use of different physics schemes on Andalusia, a complex orography region in southern Iberian Peninsula (IP). A set of 1-year WRF simulations was completed based on two one-way nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, a very wet year, were compared with observational datasets to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations. In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the best configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region.

Small rocky planets, as well as larger planets that suffered extensive volatile loss, tend to be drier and have thinner atmospheres as compared to Earth. Such planets probably outnumber worlds better endowed with volatiles, being the most common habitable planets. For the subgroup of fast rotators following eccentric orbits, atmospheres suffer radiative forcing and their heat capacity provides a method for gauging atmospheric thickness and surface conditions. We further explore the model presented in a previous paper and apply it to real and hypothetical exoplanets in the habitable zone of various classes of stars, simulating atmospheric and orbital characteristics. For planetary eccentricities e ~0.3, the forcing-induced hypothetical temperature variation would reach ~80 K for airless planets and ~10 K for planets with substantial atmospheres. For Kepler-186 f and Kepler-442 b, assuming e ~0.1, temperature variations can reach ~24 K. We also consider habitable exomoons in circular orbits around gas giants within the habitable zone, which suffer radiative forcing due to their epicyclic motion. We study several combinations of parameters for the characterization of planets (mass, eccentricity and semi-major axis) and exomoons (mass, orbital radius, albedo and atmospheric characteristics) for different stellar types. For e ~0.3, exomoon temperature varies up to ~90 K, while for ~0.6 variations can reach ~200 K. Such exomoons may plausibly retain their volatiles by continued volcanic activity fueled by tidal dissipation. Although currently undetectable, such effects might be within reach of future Extremely Large Telescope-class telescopes and space missions with mid-infrared and coronagraphic capabilities., Comment: 16 pages, 12 figures and 7 tables

Data analysis and machine learning are of preeminent importance in the legal domain, especially in tasks like clustering and text classification. In this study, we harnessed the power of natural language processing tools to enhance datasets meticulously curated by experts. This process significantly improved the classification workflow for legal texts using machine learning techniques. We considered the Sustainable Development Goals (SDGs) data from the United Nations 2030 Agenda as a practical case study. Data augmentation clustering-based strategy led to remarkable enhancements in the accuracy and sensitivity metrics of classification models. For certain SDGs within the 2030 Agenda, we observed performance gains of over 15%. In some cases, the example base expanded by a noteworthy factor of 5. When dealing with unclassified legal texts, data augmentation strategies centered around clustering prove to be highly effective. They provide a valuable means to expand the existing knowledge base without the need for labor-intensive manual classification efforts., Comment: 23 pages, 4 figures. submitted to Artificial Intelligence and Law Journal

Companies developing Web applications have faced an increasing demand for high-quality products with low cost and production time ever smaller. However, developing such applications is still considered a time-consuming and error-prone task, mainly due to the difficulty of promoting the reuse of features (or functionalities) and modules, and the heterogeneity of Web frameworks. Nowadays, companies must face ever-changing requirements. Software product lines emerged as an alternative to face this challenge by creating a collection of applications from a core of software assets. Despite the potential, the current literature lacks works that propose a product line for Web applications. This paper, therefore, presents WebSPL, a product line for Web applications that supports the main features found in Wed applications in real-world settings. The proposed WebSPL was evaluated by comparing it with a Web application developed based on a traditional approach. A case study that involves the development of two Web applications enabled data collection. Two Web applications were developed -- one with and another without the support of the proposed WebSPL. We compared these two applications using software design metrics, including complexity, size, duplicate lines, and technical debt. The initial results were encouraging and showed the potential for using WebSPL to support the development of Web applications., Comment: 6 figures, 3 tables

We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly ZTF Alert Stream via the ANTARES broker, identifying a manageable $\sim$1-5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host-galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host-galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency ($\sim$ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host-galaxy environments. We use analogs for data-driven discovery, characterization, (re-)classification, and imputation in retrospective and real-time searches. To date we have identified $\sim$50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to: SLSNe, TDEs, SNe IIn, SNe IIb, SNe Ia-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018-2021 and absent from public catalogs ($\sim$1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the "needle in the haystack" in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data., Comment: 44 pages (68 pages with Appendix), 15 figures, accepted to ApJ

The Simons Array (SA) project is a ground-based Cosmic Microwave Background (CMB) polarization experiment. The SA observes the sky using three telescopes, and POLARBEAR-2A (PB-2A) is the receiver system on the first telescope. For the ground-based experiment, atmospheric fluctuation is the primary noise source that could cause polarization leakage. In the PB-2A receiver system, a continuously rotating half-wave plate (HWP) is used to mitigate the polarization leakage. However, due to the rapid modulation of the polarization signal, the uncertainty in the time constant of the detector results in an uncertainty in the polarization angle. For PB-2A, the time constant of each bolometer needs to be calibrated at the sub-millisecond level to avoid introducing bias to the polarization signal. We have developed a new calibrator system that can be used to calibrate the time constants of the detectors. In this study, we present the design of the calibration system and the preliminary results of the time constant calibration for PB-2A., Comment: Proceedings of the 15th Asia Pacific Physics Conference (APPC15)

The growing interaction between traditional knowledge and formal sciences emerges as a relevant field of research. In this perspective, the aim of this article is to investigate the intersections between Physics and traditional knowledge in the ceramics practices of Vila Cuera, in Bragan\c{c}a, state of Para. Using an ethnographic approach, complemented by qualitative analysis, we realize that ceramists use their sensitive intuition and, masterfully, apply thermodynamic principles in the production of handmade ceramics. The results highlight the value of Ethnophysics, underlining that, long before the formal structuring of science as we know it, traditional societies already practiced and understood natural phenomena in tune with contemporary physical principles. We conclude that there is an inherent link between Physics and traditional knowledge with regard to the traditional ceramics practice, grounded on empirical observations and ancestral wisdom, that dialogues with science in a complementary relationship. The study highlights the importance of valuing and recognizing the knowledge of traditional populations by highlighting the knowledge doings of cultures and institutionalized science., Comment: in Portuguese

This work provides the first convergence analysis for the Randomized Block Coordinate Descent method for minimizing a function that is both H\"older smooth and block H\"older smooth. Our analysis applies to objective functions that are non-convex, convex, and strongly convex. For non-convex functions, we show that the expected gradient norm reduces at an $O\left(k^{\frac{\gamma}{1+\gamma}}\right)$ rate, where $k$ is the iteration count and $\gamma$ is the H\"older exponent. For convex functions, we show that the expected suboptimality gap reduces at the rate $O\left(k^{-\gamma}\right)$. In the strongly convex setting, we show this rate for the expected suboptimality gap improves to $O\left(k^{-\frac{2\gamma}{1-\gamma}}\right)$ when $\gamma>1$ and to a linear rate when $\gamma=1$. Notably, these new convergence rates coincide with those furnished in the existing literature for the Lipschitz smooth setting.

Clustering data objects into homogeneous groups is one of the most important tasks in data mining. Spectral clustering is arguably one of the most important algorithms for clustering, as it is appealing for its theoretical soundness and is adaptable to many real-world data settings. For example, mixed data, where the data is composed of numerical and categorical features, is typically handled via numerical discretization, dummy coding, or similarity computation that takes into account both data types. This paper explores a more natural way to incorporate both numerical and categorical information into the spectral clustering algorithm, avoiding the need for data preprocessing or the use of sophisticated similarity functions. We propose adding extra nodes corresponding to the different categories the data may belong to and show that it leads to an interpretable clustering objective function. Furthermore, we demonstrate that this simple framework leads to a linear-time spectral clustering algorithm for categorical-only data. Finally, we compare the performance of our algorithms against other related methods and show that it provides a competitive alternative to them in terms of performance and runtime.

We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early-time ($\delta t < 2$ days) spectra show transient, narrow emission lines from shock ionization of confined ($r < 10^{15}$ cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of HI, He I/II, C III/IV, and N III/IV/V from the CSM persist on a characteristic timescale ($t_{\rm IIn}$) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early-time IIn-like features in addition to 35 "comparison" SNe with no evidence of early-time IIn-like features, all with ultraviolet observations. The total sample consists of 50 unpublished objects with 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and both $t_{\rm IIn}$ and the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through matching of peak multiband absolute magnitudes, rise times, $t_{\rm IIn}$ and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium (nLTE) radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: $t_{\rm IIn} \approx 3.8[\dot{M}/(0.01 \textrm{M}_{\odot} \textrm{yr}^{-1})]$ days., Comment: 58 pages, 24 figures, submitted to ApJ. Supplementary figures available on Github (https://github.com/wynnjacobson-galan/Flash_Spectra_Sample). Data release following publication

In this study, we consider the four-dimensional Maxwell electrodynamics extended with CPT-even Myers-Pospelov Lorentz-violating dimension-six operators to investigate the associated two-dimensional properties in the context of quantum vacuum fluctuation effects, namely, the Casimir effect. Upon projecting out the 4D theory down to a 2D theory we obtain analogs of these operators leading to a modified dispersion relation in a Lorentz invariance violation (LIV) scalar model equivalent to the electromagnetic theory. By making use of the modified dispersion relation, we derive exact analytic expressions for the Casimir energy and force induced by imposing Dirichlet boundary conditions on the scalar field. In the regime where the LIV parameter becomes very small, we recover known results for the Casimir energy and force plus correction terms due to the LIV., Comment: 9 pages, 4 figures

Resource-constrained robotic platforms are particularly useful for tasks that require low-cost hardware alternatives due to the risk of losing the robot, like in search-and-rescue applications, or the need for a large number of devices, like in swarm robotics. For this reason, it is crucial to find mechanisms for adapting reinforcement learning techniques to the constraints imposed by lower computational power and smaller memory capacities of these ultra low-cost robotic platforms. We try to address this need by proposing a method for making imitation learning deployable onto resource-constrained robotic platforms. Here we cast the imitation learning problem as a conditional sequence modeling task and we train a decision transformer using expert demonstrations augmented with a custom reward. Then, we compress the resulting generative model using software optimization schemes, including quantization and pruning. We test our method in simulation using Isaac Gym, a realistic physics simulation environment designed for reinforcement learning. We empirically demonstrate that our method achieves natural looking gaits for Bittle, a resource-constrained quadruped robot. We also run multiple simulations to show the effects of pruning and quantization on the performance of the model. Our results show that quantization (down to 4 bits) and pruning reduce model size by around 30\% while maintaining a competitive reward, making the model deployable in a resource-constrained system., Comment: 10 pages, 4 figures

In their early, formative stages star clusters can undergo rapid dynamical evolution leading to strong gravitational interactions and ejection of "runaway" stars at high velocities. While O/B runaway stars have been well studied, lower-mass runaways are so far very poorly characterised, even though they are expected to be much more common. We carried out spectroscopic observations with MAG2-MIKE to follow-up 27 high priority candidate runaways consistent with having been ejected from the Orion Nebula Cluster (ONC) > 2.5 Myr ago, based on Gaia astrometry. We derive spectroscopic youth indicators (Li & H${\alpha}$) and radial velocities, enabling detection of bona fide runaway stars via signatures of youth and 3D traceback. We successfully confirmed 11 of the candidates as low-mass Young Stellar Objects (YSOs) on the basis of our spectroscopic criteria and derived radial velocities (RVs) with which we performed 3D traceback analysis. Three of these confirmed YSOs have kinematic ejection ages > 4 Myr, with the oldest being 4.7 Myr. Assuming that these stars indeed formed in the ONC and were then ejected, this yields an estimate for the overall formation time of the ONC to be at least ${\sim}$ 5 Myr, i.e., about 10 free-fall times, and with a mean star formation efficiency per free-fall time of ${\bar{\epsilon}_{ff}} {\lesssim}$ 0.05. These results favor a scenario of slow, quasi-equilibrium star cluster formation, regulated by magnetic fields and/or protostellar outflow feedback., Comment: Accepted for publication in MNRAS

Generative adversarial networks (GANs) generate photorealistic faces that are often indistinguishable by humans from real faces. While biases in machine learning models are often assumed to be due to biases in training data, we find pathological internal color and luminance biases in the discriminator of a pre-trained StyleGAN3-r model that are not explicable by the training data. We also find that the discriminator systematically stratifies scores by both image- and face-level qualities and that this disproportionately affects images across gender, race, and other categories. We examine axes common in research on stereotyping in social psychology.

Designing and developing web-enabled remote laboratories for pedagogical purposes is not an easy task. Often, developers (generally, educators who know the subjects they teach but lack of the technical and programming skills required to build Internet-based educational applications) end up discarding the idea of exploring these new teaching and learning experiences mainly due to the technical issues that must be mastered. To tackle this problem, authors present a novel technique that allows developers to create remote labs in a quick, didactical, and straightforward way. This framework is based on the use of two well-known software tools in the scope of engineering education, Easy Java Simulations and LabVIEW. The development exploits a new feature of Easy Java Simulations known as EJS-elements that enables Java developers to create and integrate their own authoring libraries (elements) into EJS, thus increasing its application possibilities. Particularly, the EJS element here presented allows to LabVIEW programs be controlled from EJS applications through a communication network. This paper presents the element creation details and how this can be used to create web-enabled experimentation environments for educational purposes. A step by step example of development of a remote lab for automatic control education is described.