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  1. Free, publicly-accessible full text available December 1, 2022
  2. Educational prediction games use the popularity and engagement of fantasy sports as a success model to promote learning in other domains. Fantasy sports motivate players to stay up-to-date with relevant news and explore large statistical data sets, thereby deepening their domain understanding while potentially honing their data analysis skills. We conducted a study of fantasy sports players, and discovered that while some participants performed sophisticated data analysis to support their gameplay, far more relied on news and published commentary. We used results from this study to design a prototype prediction game, Fantasy Climate, which helps players move from intuitions andmore »advice to consuming news and analyzing data by supporting a variety of activities essential to gameplay. Because news is a key component of Fantasy Climate, we evaluated two link-based interfaces to domain-related news, one geospatial and the other organized as a list. The evaluation revealed that news presentation has a strong effect on players' engagement and performance: players using the geospatial interface not only were more engaged in the game; they also made better predictions than players who used the list-based presentation.« less
  3. Has widespread news of abuse changed the public's perceptions of how user-contributed content from social networking sites like Facebook and LinkedIn can be used? We collected two datasets that reflect participants' attitudes about content ownership, privacy, and control, one in April 2018, while Cambridge Analytica was still in the news, and another in February 2019, after the event had faded from the headlines, and aggregated the data according to participants' awareness of the story, contrasting the attitudes of those who reported the greatest awareness with those who reported the least. Participants with the greatest awareness of the news story's detailsmore »have more polarized attitudes about reuse, especially the reuse of content as data. They express a heightened desire for data mobility, greater concern about networked privacy rights, increased skepticism of algorithmically targeted advertising and news, and more willingness for social media platforms to demand corrections of inaccurate or deceptive content.« less
  4. Free, publicly-accessible full text available February 3, 2023
  5. How do children’s visual concepts change across childhood, and how might these changes be reflected in their drawings? Here we investigate developmental changes in children’s ability to emphasize the relevant visual distinctions between object categories in their drawings. We collected over 13K drawings from children aged 2-10 years via a free-standing drawing station in a children’s museum. We hypothesized that older children would produce more recognizable drawings, and that this gain in recognizability would not be entirely explained by concurrent development in visuomotor control. To measure recognizability, we applied a pretrained deep convolutional neural network model to extract a high-levelmore »feature representation of all drawings, and then trained a multi-way linear classifier on these features. To measure visuomotor control, we developed an automated procedure to measure their ability to accurately trace complex shapes. We found consistent gains in the recognizability of drawings across ages that were not fully explained by children’s ability to accurately trace complex shapes. Furthermore, these gains were accompanied by an increase in how distinct different object categories were in feature space. Overall, these results demonstrate that children’s drawings include more distinctive visual features as they grow older.« less
  6. Abstract Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D 0 D 0 π + mass spectrum just below the D *+ D 0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalarmore »$${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + tetraquark with a quark content of $${{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}\overline{{{{{{\rm{u}}}}}}}\overline{{{{{{\rm{d}}}}}}}$$ c c u ¯ d ¯ and spin-parity quantum numbers J P  = 1 + . Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D *+ mesons is consistent with the observed D 0 π + mass distribution. To analyse the mass of the resonance and its coupling to the D * D system, a dedicated model is developed under the assumption of an isoscalar axial-vector $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state decaying to the D * D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state. In addition, an unexpected dependence of the production rate on track multiplicity is observed.« less
    Free, publicly-accessible full text available December 1, 2023
  7. How do children’s representations of object categories change as they grow older? As they learn about the world around them, they also express what they know in the drawings they make. Here, we examine drawings as a window into how children represent familiar object categories, and how this changes across childhood. We asked children (age 3-10 years) to draw familiar object categories on an iPad. First, we analyzed their semantic content, finding large and consistent gains in how well children could produce drawings that are recognizable to adults. Second, we quantified their perceptual similarity to adult drawings using a pre-trainedmore »deep convolutional neural network, allowing us to visualize the representational layout of object categories across age groups using a common feature basis. We found that the organization of object categories in older children’s drawings were more similar to that of adults than younger children’s drawings. This correspondence was strong in the final layers of the neural network, showing that older children’s drawings tend to capture the perceptual features critical for adult recognition. We hypothesize that this improvement reflects increasing convergence between children’s representations of object categories and that of adults; future work will examine how these age-related changes relate to children’s developing perceptual and motor capacities. Broadly, these findings point to drawing as a rich source of insight into how children represent object concepts.« less