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Is the Standard Model Charge-Parity (CP) violation ever enough to generate the observed baryon asymmetry? Yes! We introduce a mechanism of baryogenesis (and dark matter production) that can generate the entire observed baryon asymmetry of the Universe using $\textit{only}$ the CP violation within Standard Model systems -- a f\^ete which no other mechanism currently proposed can achieve. Baryogenesis proceeds through a Mesogenesis scenario but with well motivated additional dark sector dynamics: a $\textit{morphon}$ field generates present day mass contributions for the particle mediating the decay responsible for baryogenesis. The effect is an enhancement of baryon production whilst evading present day collider constraints. The CP violation comes entirely from Standard Model contributions to neutral meson systems. Meanwhile, the dark dynamics generate gravitational waves that may be searched for with current and upcoming Pulsar Timing Arrays, as we demonstrate with an example. This mechanism, $\textit{Mesogenesis with a Morphing Mediator}$, motivates probing a new parameter space as well as improving the sensitivity of existing Mesogenesis searches at hadron and electron colliders., Comment: 5+1 pages, 3 figures

In this work, we systematically study the electronic structure and stability of spin states of the [Fe-(tpy-ph)$_2$]$^{2+}$ molecule in both gas phase and on a Au(111) substrate using density functional theory +U (DFT+U) calculations. We find that the stability of the Fe$^{2+}$ ion's spin states is significantly influenced by the Hubbard U parameter. In the gas phase, the low-spin (LS, S=0) state is found to be energetically favorable for U(Fe) $\leq$ 3 eV, whereas the high-spin (HS, S=2) state is stabilized for U(Fe) > 3 eV. Interaction with the Au(111) substrate is found to elevate the critical U for the spin-state transition to 3.5 eV. Additionally, we perform L-edge X-ray absorption spectroscopy (XAS) calculations based on time-dependent DFT (TD-DFT) for both HS and LS states. The calculated XAS suggests that the HS state more closely aligns with the experimental observations, indicating the potential coexistence of the HS state as the initial state during the X-ray excitation process. These findings enrich our understanding of spin-state dynamics in [Fe-(tpy-ph)$_2$]$^{2+}$., Comment: 21 pages, 5 figures, 3 tables

Researchers in the political and social sciences often rely on classification models to analyze trends in information consumption by examining browsing histories of millions of webpages. Automated scalable methods are necessary due to the impracticality of manual labeling. In this paper, we model the detection of topic-related content as a binary classification task and compare the accuracy of fine-tuned pre-trained encoder models against in-context learning strategies. Using only a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. We compare multilingual and monolingual models, as well as zero and few-shot approaches, and investigate the impact of negative sampling strategies and the combination of URL & content-based features. Our results show that a small sample of annotated data is sufficient to train an effective classifier. Fine-tuning encoder-based models yields better results than in-context learning. Classifiers using both URL & content-based features perform best, while using URLs alone provides adequate results when content is unavailable.

The phase lag between an applied forcing and a response to that forcing is a fundamen tal parameter in geophysical signal processing. For solid deforming materials, the phase lag between an oscillatory applied stress and the resulting strain response encapsulates information about the dynamical behavior of materials and attenuation. The phase lag is not directly measured and must be extracted through multiple steps by carefully comparing two time-series signals. The extracted value of the phase lag is highly sensitive to the analysis method, and often there are no comparable values to increase confidence in the calculated results. In this study, we propose a method for extracting the phase lag between two signals when either one or both include an underlying nonlinear trend, which is very common when measuring attenuation in creeping materials. We demonstrate the robustness of the method by analyzing artificial signals with known phases and quantifying their absolute and relative errors. We apply the method to two experimental datasets and compare our results with those of previous studies

We resort to variational methods to evaluate the asymptotic behavior of fine metamaterials as a function of cell size. To zeroth order, the metamaterial behaves as a micropolar continuum with both displacement and rotation degrees of freedom, but exhibits linear-elastic fracture mechanics scaling and therefore no size effect. To higher order, the overall energetics of the metastructure can be characterized explicitly in terms of the solution of the zeroth-order continuum problem by the method of {\Gamma}-expansion. We present explicit expressions of the second-order correction for octet frames. As an application, we evaluate the compliance of double-cantilever octet specimens to second order and use the result to elucidate the dependence of the apparent toughness of the specimen on cell size. The analysis predicts the discreteness of the metamaterial lattice to effectively shield the crack-tip, a mechanism that we term lattice shielding. The theory specifically predicts anti-shielding, i. e., coarser is weaker, in agreement with recent experimental observations.

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models -- SegNet, 1D U-Net, and 2D U-Net -- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective method for identifying layer thickness in this class of materials., Comment: 21 pages, 6 figures

We study the interplay of attractive electron interactions and topological states in strained kagome lattices with spin-orbit coupling via a Hubbard Hamiltonian in the mean-field approximation. In the unstrained lattice, there is a topological phase transition from a quantum spin Hall state to a charge density wave (CDW) with increasing interaction strength. Upon applying a uniform uniaxial strain to the lattice, we find a new phase with coexisting CDWs and topological states. For increasing interaction strength or strain, the system is driven into a pure CDW, signaling topological phase transitions. The directionality (nematicity) of the CDW is controlled by the direction of the applied strain. When $s$ wave electronic pairing is allowed, the system develops a superconducting order beyond a threshold attraction, which is totally suppressed by the onset of a CDW with increasing interaction. Most interestingly, moderate strain allows the coexistence of superconductivity and CDWs for a range of interaction values. This illustrates how electronic interactions and single-particle topological structures compete to create unusual correlated phases in kagome systems.

We introduce a continuum framework for the energetics of particle-size segregation in bidisperse granular flows. Building on continuum segregation equations and a recent segregation flux model, the proposed framework offers general analytical expressions to study the physics of granular flows from a mechanical energy perspective. To demonstrate the framework's applicability, we examined the energetics in shear-driven flows. Numerical experiments with varying frictional coefficients and particle-size ratios revealed two distinct phases in the associated energetics with particle segregation and diffusive remixing, and that the potential energy to the kinetic energy ratio in the steady state follows the scaling relationship $\hat{E}^{(s)}_{gp} / \hat{E}^{(s)}_{k} \propto Pe^{-1/2}_{sr}$ for $0.4 \leq Pe_{sr} \leq 300$, the segregation-rheology P\'eclet number. Our findings hint that the bulk segregation-mixing state can be predicted and controlled using $Pe_{sr}$, determined from known system parameters, providing a impactful tool for engineering and geophysical applications., Comment: 10 pages, 3 figures

We study the physics of the strong-coupling Hubbard model in a kagome lattice ribbon under mechanical tension and half-filling. It is known that in the absence of strain, the lattice symmetry of the system and strong electronic interactions induce magnetic frustration. As uniaxial strain is applied, the ribbon exhibits various configurations with energy oscillations that depend on the direction of the strain axis. The ground states are obtained by density matrix enormalization group calculations. We find that the system is characterized by strong antiferromagnetic bonds distributed throughout the lattice along directions and patterns that depend on the strain directions and may coexist with easily polarizable sites that are only weakly correlated to their neighbors. We identify frustration and correlation measures that follow the strain and interaction dependence of the system well. These results illustrate that strain-dependent magnetic susceptibility could be explored experimentally to help probe the role of symmetry and interactions in these systems., Comment: 8 pages, 9 figures

This study investigates the anisotropic effects on a system of three qubits with chain and ring topology, described by the antiferromagnetic Heisenberg XXX model subjected to a homogeneous magnetic field. We explore the Stirling and Otto cycles and find that easy-axis anisotropy significantly enhances engine efficiency across all cases. At low temperatures, the ring configuration outperforms the chain on both work and efficiency during the Stirling cycle. Additionally, in both topologies, the Stirling cycle achieves Carnot efficiency with finite work at quantum critical points. In contrast, the quasistatic Otto engine also reaches Carnot efficiency at these points but yields no useful work. Notably, the Stirling cycle exhibits all thermal operational regimes engine, refrigerator, heater, and accelerator unlike the quasistatic Otto cycle, which functions only as an engine or refrigerator.

Earth's aquatic environments are inherently stratified layered systems where interfaces between layers serve as ecological niches for microbial swimmers, forming colonies known as Active Carpet (AC). Previous theoretical studies have explored the hydrodynamic fluctuations exerted by ACs in semi-infinite fluid media, demonstrating their capability to enhance thermal diffusion and mass transport in aquatic systems. Yet, little is understood about the fluid dynamics and impact of ACs residing in confined layered environments, like slicks floating on water bodies. In this study, we report novel solutions for the hydrodynamic fluctuations induced by ACs geometrically confined between a free surface and a fluid-fluid interface characterized by a jump in fluid viscosity. Combining theory and numerical experiments, we investigate the topology of the biogenic hydrodynamic fluctuations in a confined, thin fluid environment. We reveal that within this thin layer, ACs gives shape to three characteristic regions: Region I is the closest zone to the AC and the fluid-fluid interface, where hydrodynamic fluctuations are dominantly vertical; Region II is further up from the AC and is characterized by isotropic hydrodynamic fluctuations; Region III is the furthest region, near the free surface and is dominated by horizontal flow fluctuations. We demonstrate that the extent of these regions depends strongly on the degree of confinement, i.e. the layer thickness and the strength of the viscosity jump. Lastly, we show that confinement fosters the emergence of large-scale flow structures within the layer housing the ACs--not previously reported. Our findings shed light on the complex interplay between confinement and hydrodynamics in floating viscous film biological systems, providing valuable insights with implications spanning from ecological conservation to bio-inspired engineering.

The inclusion of an additional $U(1)$ gauge $L_\mu-L_\tau$ symmetry would release the tension between the measured and the predicted value of the anomalous muon magnetic moment: this paradigm assumes the existence of a new, light $Z^\prime$ vector boson, with dominant coupling to $\mu$ and $\tau$ leptons and interacting with electrons via a loop mechanism. The $L_\mu-L_\tau$ model can also explain the Dark Matter relic abundance, by assuming that the $Z'$ boson acts as a "portal" to a new Dark Sector of particles in Nature, not charged under known interactions. In this work we present the results of the $Z'$ search performed by the NA64-$e$ experiment at CERN SPS, that collected $\sim 9\times10^{11}$ 100 GeV electrons impinging on an active thick target. Despite the suppressed $Z'$ production yield with an electron beam, NA64-$e$ provides the first accelerator-based results excluding the $g-2$ preferred band of the $Z'$ parameter space in the 1 keV $ < m_{Z'} \lesssim 2$ MeV range, in complementarity with the limits recently obtained by the NA64-$\mu$ experiment with a muon beam., Comment: 15 pages, 5 figures. New version with extended exclusion limits down to 1 keV in Z' mass

This article evaluates the quality of data collection in individual-level desktop information tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard of the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic tracking solution, WebTrack, an open source tracking tool maintained by a major European research institution. The design logic, the interfaces and the backend requirements for WebTrack, followed by a detailed examination of strengths and weaknesses of the tool, are discussed. Finally, using data from 1185 participants, the article empirically illustrates how an improvement in the data collection through WebTrack leads to new innovative shifts in the processing of tracking data. As WebTrack allows collecting the content people are exposed to on more than classical news platforms, we can strongly improve the detection of politics-related information consumption in tracking data with the application of automated content analysis compared to traditional approaches that rely on the list-based identification of news., Comment: 73 pages

This paper explores the role of generalized continuum mechanics, and the feasibility of model-free data-driven computing approaches thereof, in solids undergoing failure by strain localization. Specifically, we set forth a methodology for capturing material instabilities using data-driven mechanics without prior information regarding the failure mode. We show numerically that, in problems involving strain localization, the standard data-driven framework for Cauchy/Boltzmann continua fails to capture the length scale of the material, as expected. We address this shortcoming by formulating a generalized data-driven framework for micromorphic continua that effectively captures both stiffness and length-scale information, as encoded in the material data, in a model-free manner. These properties are exhibited systematically in a one-dimensional softening bar problem and further verified through selected plane-strain problems., Comment: Published version

Diarrhetic Shellfish Poisoning (DSP) is a global health threat arising from shellfish contaminated with toxins produced by dinoflagellates. The condition, with its widespread incidence, high morbidity rate, and persistent shellfish toxicity, poses risks to public health and the shellfish industry. High biomass of toxin-producing algae such as DSP are known as Harmful Algal Blooms (HABs). Monitoring and forecasting systems are crucial for mitigating HABs impact. Predicting harmful algal blooms involves a time-series-based problem with a strong historical seasonal component, however, recent anomalies due to changes in meteorological and oceanographic events have been observed. Stream Learning stands out as one of the most promising approaches for addressing time-series-based problems with concept drifts. However, its efficacy in predicting HABs remains unproven and needs to be tested in comparison with Batch Learning. Historical data availability is a critical point in developing predictive systems. In oceanography, the available data collection can have some constrains and limitations, which has led to exploring new tools to obtain more exhaustive time series. In this study, a machine learning workflow for predicting the number of cells of a toxic dinoflagellate, Dinophysis acuminata, was developed with several key advancements. Seven machine learning algorithms were compared within two learning paradigms. Notably, the output data from CROCO, the ocean hydrodynamic model, was employed as the primary dataset, palliating the limitation of time-continuous historical data. This study highlights the value of models interpretability, fair models comparison methodology, and the incorporation of Stream Learning models. The model DoME, with an average R2 of 0.77 in the 3-day-ahead prediction, emerged as the most effective and interpretable predictor, outperforming the other algorithms.

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected. At present, the closure of production areas is based on expert knowledge and the existence of a predictive model would help when conditions are complex and sampling is not possible. Although the concentration of toxin in meat is the method most commonly used by experts in the control of shellfish production areas, it is rarely used as a target by automatic prediction models. This is largely due to the irregularity of the data due to the established sampling programs. As an alternative, the activity status of production areas has been proposed as a target variable based on whether mollusc meat has a toxicity level below or above the legal limit. This new option is the most similar to the actual functioning of the control of shellfish production areas. For this purpose, we have made a comparison between hybrid machine learning models like Neural-Network-Adding Bootstrap (BAGNET) and Discriminative Nearest Neighbor Classification (SVM-KNN) when estimating the state of production areas. The study has been carried out in several estuaries with different levels of complexity in the episodes of algal blooms to demonstrate the generalization capacity of the models in bloom detection. As a result, we could observe that, with an average recall value of 93.41% and without dropping below 90% in any of the estuaries, BAGNET outperforms the other models both in terms of results and robustness.

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of a confirmatory sampling and the existence of risk factors, precautionary closures may be applied. These decisions are made by experts without the support or formalisation of the experience on which they are based. Therefore, this work proposes a predictive model capable of supporting the application of precautionary closures. Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and 0.75 respectively, the kNN algorithm has provided the best results. This allows the creation of a system capable of helping in complex situations where forecast errors are more common.

Context. Globular clusters are considered key objects for understanding the formation and evolution of the Milky Way. In this sense, their characterisation in terms of their chemical and orbital parameters can provide constraints to the chemical evolution models of the Galaxy. Aims. We use the heavy element abundances of globular clusters to trace their overall behaviour in the Galaxy, aiming to analyse potential relations between the hot H-burning and s-process elements. Methods. We measured the content of Cu I and s- and r-process elements (Y II, Ba II, La II, and Eu II) in a sample of 210 giant stars in 18 Galactic Globular Clusters from high-quality UVES spectra. The clusters span a large metallicity range, and the sample is the largest uniformly analysed for what concerns heavy elements in Globular Clusters. Results. Cu abundances did not show considerable spread in the sample nor correlation with Na, meaning that the Na nucleosynthesis process does not affect the Cu abundance. Most GCs closely follow the Cu, Y, Ba, La, and Eu field stars' distribution, revealing a similar chemical evolution. The Y abundances in mid-metallicity regime GCs (-1.10 dex <[Fe/H]<-1.80 dex) display a mildly significant correlation with the Na abundance, which should be further investigated. Finally, we did not find any significant difference between the n-capture abundances among GCs with Galactic and extragalactic origin., Comment: 23pages and 20 figures

Recently, GaAsSb/GaAsN type II short-period superlattices (SLs) have been proposed as suitable structures to be implemented in the optimal design of monolithic multi-junction solar cells. However, due to strong surface Sb segregation, experimental Sb composition profiles differ greatly from the nominal square-wave design. In this work, the improvement of the interface quality of these SLs in terms of compositional abruptness and surface roughness has been evaluated by implementing different growth interruption times under Sb4/As4 (soaking) and As4 (desorption) overpressure conditions before and after the growth of GaAsSb layers, respectively. The combined effects of both processes enhance Sb distribution, achieving squarer compositional profiles with reduced surface roughness interfaces. It has been found that the improvement in compositional abruptness is quantitatively much higher at the lower interface, during soaking, than at the upper interface during desorption. Conversely, a larger decrease in surface roughness is achieved at the upper interface than at the lower interface. Fitting of the Sb segregation profiles using the 3-layer kinetic fluid model has shown that the increase in Sb incorporation rate is due to the decrease in segregation energy, presumably to changes in the surface reconstruction of the floating layer at the surface., Comment: 7 pages, 5 figures

Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards -- including erosion and the undermining of urban infrastructure -- consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments., Comment: Published to IEEE OCEANS 2022

Communities of swimming microorganisms often thrive near liquid-air interfaces. We study how such `active carpets' shape their aquatic environment by driving biogenic transport in the water column beneath them. The hydrodynamic stirring that active carpets generate leads to diffusive upward fluxes of nutrients from deeper water layers, and downward fluxes of oxygen and carbon. Combining analytical theory and simulations, we examine the biogenic transport by studying fundamental metrics, including the single and pair diffusivity, the first passage time for particle pair encounters, and the rate of particle aggregation. Our findings reveal that the hydrodynamic fluctuations driven by active carpets have a region of influence that reaches orders of magnitude further in distance than the size of the organisms. These nonequilibrium fluctuations lead to a strongly enhanced diffusion of particles, which is anisotropic and space-dependent. Fluctuations also facilitate encounters of particle pairs, which we quantify by analysing their velocity pair correlation functions as a function of distance between the particles. We found that the size of the particles plays a crucial role in their encounter rates, with larger particles situated near the active carpet being more favourable for aggregation. Overall, this research broadens our comprehension of aquatic systems out of equilibrium and how biologically driven fluctuations contribute to the transport of fundamental elements in biogeochemical cycles.

Researchers rely on academic web search engines to find scientific sources, but search engine mechanisms may selectively present content that aligns with biases embedded in the queries. This study examines whether confirmation-biased queries prompted into Google Scholar and Semantic Scholar will yield skewed results. Six queries (topics across health and technology domains such as "vaccines" or "internet use") were analyzed for disparities in search results. We confirm that biased queries (targeting "benefits" or "risks") affect search results in line with the bias, with technology-related queries displaying more significant disparities. Overall, Semantic Scholar exhibited fewer disparities than Google Scholar. Topics rated as more polarizing did not consistently show more skewed results. Academic search results that perpetuate confirmation bias have strong implications for both researchers and citizens searching for evidence. More research is needed to explore how scientific inquiry and academic search engines interact.

With the unprecedented increase of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network trained on synthetic clusters to estimate the age, metallicity, extinction, and distance of $Gaia$ open clusters. We implement a novel technique to extract features from the colour-magnitude diagram of clusters by means of the QuadTree tool and we adopt a multi-band approach. We obtain reliable parameters for $\sim 5400$ clusters. We demonstrate the effectiveness of our methodology in accurately determining crucial parameters of $Gaia$ open clusters by performing a comprehensive scientific validation. In particular, with our analysis we have been able to reproduce the Galactic metallicity gradient as it is observed by high-resolution spectroscopic surveys. This demonstrates that our method reliably extracts information on metallicity from colour-magnitude diagrams (CMDs) of stellar clusters. For the sample of clusters studied, we find an intriguing systematic older age compared to previous analyses present in the literature. This work introduces a novel approach to feature extraction using a QuadTree algorithm, effectively tracing sequences in CMDs despite photometric errors and outliers. The adoption of ANNs, rather than Convolutional Neural Networks, maintains the full positional information and improves performance, while also demonstrating the potential for deriving clusters' parameters from simultaneous analysis of multiple photometric bands, beneficial for upcoming telescopes like the Vera Rubin Observatory. The implementation of ANN tools with robust isochrone fit techniques could provide further improvements in the quest for open clusters' parameters., Comment: 24 pages, 15 figures, Accepted in The Astronomical Journal. Temporally, data produced in this work are available at https://phisicslollo0.github.io/cavallo23.html

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called UMAP is used. This method manages to reduce the dimensions while preserving part of the local and global information of the original data. K-Means is used as the clustering algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF statistics, Topic Diversity, and Topic Coherence to get the meaning of the words on the clusters. The results of the process show good values, so the topic modeling of this process is a viable option for classifying or clustering texts without labels.

This work combined different audio features to obtain a more robust fingerprint to be used in a music recommendation process. The combination of these methods resulted in a high-dimensional vector. To reduce the number of values, PCA was applied to the set of resulting fingerprints, selecting the number of principal components that corresponded to an explained variance of $95\%$. Finally, with these PCA-fingerprints, the similarity matrix of each fingerprint with the entire data set was calculated. The process was applied to 200 songs from a personal music library; the songs were tagged with the artists' corresponding genres. The recommendations (fingerprints of songs with the closest similarity) were rated successful if the recommended songs' genre matched the target songs' genre. With this procedure, it was possible to obtain an accuracy of $89\%$ (successful recommendations out of total recommendation requests).

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N=1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT- 2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but do not find that gender or income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

We present the results of a missing-energy search for Light Dark Matter which has a new interaction with ordinary matter transmitted by a vector boson, called dark photon $A^\prime$. For the first time, this search is performed with a positron beam by using the significantly enhanced production of $A^\prime$ in the resonant annihilation of positrons with atomic electrons of the target nuclei, followed by the invisible decay of $A^\prime$ into dark matter. No events were found in the signal region with $(10.1 \pm 0.1)~\times~10^{9}$ positrons on target with 100 GeV energy. This allowed us to set new exclusion limits that, relative to the collected statistics, prove the power of this experimental technique. This measurement is a crucial first step toward a future exploration program with positron beams, whose estimated sensitivity is here presented., Comment: Version submitted to PRL

This paper presents a multi-temporal formulation for simulating elastoplastic solids under cyclic loading. We leverage the proper generalized decomposition (PGD) to decompose the displacements into multiple time scales, separating the spatial and intra-cyclic dependence from the inter-cyclic variation. In contrast with the standard incremental approach, which solves the (non-linear and computationally intensive) mechanical balance equations at every time step, the proposed PGD approach allows the mechanical balance equations to be solved exclusively for the small-time intra-cyclic response, while the large-time inter-cyclic response is described by simple scalar algebraic equations. Numerical simulations exhibiting complex cyclic responses, including a 2D problem and an application to a monopile foundation, demonstrate that PGD solutions with a limited number of space-time degrees of freedom may be obtained numerically, only requiring a few modes to accurately capture the reference response., Comment: Version accepted for publication in Computer Methods in Applied Mechanics and Engineering

Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources can be found on our GitHub repository https://github.com/soundclim/anuraset.

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: https://sites.google.com/view/incipient-slip-detection.