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Abstract: As a collaboration among the University of Nova Gorica (Slovenia), University of Craiova (Romania), and University of Udine (Italy), FEAL 2023 represents an interdisciplinary training opportunity for BA, MA and PhD students from linguistics, applied mathematics, psychology, and neuroscience. It addresses research topics in contemporary linguistics through formal, computational, and experimental approaches. In this training opportunity, we will explore some of the most fundamental General Linear Models (GLMs) that have a wide range of applications in various fields. GLMs provide a flexible framework for modeling a variety of data types, including continuous, binary, count, and categorical data, making them an essential tool for any data analyst. During the course, we will briefly cover some GLM models that have applications in diverse fields such as Psychology, Linguistics, Viticulture, Neuroscience, Economics, Biology, and beyond. We will review the basic concepts and tools in GLMs and will discuss how to choose the appropriate model for a given data type and how to interpret the model's output. This course will provide a hands-on learning experience, where you will have the opportunity to apply the concepts you learn to real-world datasets (using SPSS and Excel). We will go as long as time permits to cover as many topics as possible and ensure that you have a good understanding of GLM models and the skills to apply them to your own research projects.
Keywords: Experimental Linguistics, Statistical Models, Mathematical and Computational Linguistics, General Linear Models (GLMs)
Published in RUNG: 06.01.2025; Views: 485; Downloads: 6
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Abstract: The Pierre Auger Observatory is the world’s largest ultra-high energy cosmic ray observatory. Its hybrid detection technique combines the observation of the longitudinal development of extensive air showers and the lateral distribution of particles arriving at the ground. In this contribution, a review of the latest results on hadronic interactions using measurements from the Pierre Auger Observatory is given. In particular, we report on the self-consistency tests of the post-LHC models using measurements of the depth of the shower maximum and the main features of the muon component at the ground. The tensions between the model predictions and the data, considering different shower observables, are reviewed.
Keywords: ultra-high-energy cosmic rays, hadronic interactions, extensive air showers, maximum depth, EAS muon content, Pierre Auger Observatory, post-LHC hadronic interaction models
Published in RUNG: 04.10.2024; Views: 1037; Downloads: 8
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Abstract: We present a new method to explore simple ad-hoc adjustments to the predictions of hadronic interaction models to improve their consistency with observed two-dimensional distributions of the depth of shower maximum, Xmax, and signal at ground level, as a function of zenith angle. The method relies on the assumption that the mass composition is the same at all zenith angles, while the atmospheric shower development and attenuation depend on composition in a correlated way. In the present work, for each of the three leading LHC-tuned hadronic interaction models, we allow a global shift ΔXmax of the predicted shower maximum, which is the same for every mass and energy, and a rescaling R_Had of the hadronic component at ground level which depends on the zenith angle. We apply the analysis to 2297 events reconstructed by both fluorescence and surface detectors at the Pierre Auger Observatory with energies 10^18.5−10^19.0 eV. Given the modeling assumptions made in this analysis, the best fit reaches its optimum value when shifting the Xmax predictions of hadronic interaction models to deeper values and increasing the hadronic signal at both extreme zenith angles. The resulting change in the composition towards heavier primaries alleviates the previously identified model deficit in the hadronic signal (commonly called the muon deficit), but does not remove it. Because of the size of the required corrections ΔXmax and R_Had and the large number of events in the sample, the statistical significance of the corrections is large, greater than 5σstat even for the combination of experimental systematic shifts within 1σsys that are the most favorable for the models.
Keywords: Pierre Auger Observatory, indirect detection, fluorescence detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, Xmax, muon deficit, air-shower models
Published in RUNG: 04.10.2023; Views: 2223; Downloads: 10
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Keywords: ultra-high energy, cosmic rays, Pierre Auger Observatory, UHE photon flux upper limits, super-heavy dark matter models
Published in RUNG: 09.02.2023; Views: 2658; Downloads: 0
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Abstract: We investigated the effect of presenting items in a foreign language (L2) on scalar- implicatures computation. To ensure that L2 processing was more effortful than the pro- cessing of the native language (L1), participants were late learners of L2 immersed in an L1 environment and they were presented with oral stimuli under time constraints. If scalar- implicatures computation requires cognitive effort one should !nd that people are more likely to compute scalar implicatures in L1 than in L2. In two experiments, participants were asked to perform a Sentence Evaluation Task either Italian, their native language, or in a foreign language (English or Spanish). The task included underinformative statements such as “Some dogs are animals” that, if interpreted in a pragmatic way (i.e., “Some but not all dogs are animals”) should be rejected as false. In both experiments, we found more rejections in the native language condition than in the foreign language conditions. These results provide support for models that maintain that scalar-implicature computation is effortful.
Keywords: scalar implicatures, pragmatics, default models, non-default models, second-language comprehension
Published in RUNG: 17.09.2021; Views: 3170; Downloads: 0
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