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Keywords: sentence processing, psycholinguistics, memory, feature assignment
Published in RUNG: 26.06.2024; Views: 1528; Downloads: 4
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Abstract: In Bulgarian, numerals such as pet ('five') assign a special 'count form' feature to the noun: this assignment takes place across any number of intervening modifier phrases, thus forming a long-distance syntactic dependency. In colloquial speech, speakers often erroneously substitute the count form for regular plurals. Previous corpus and psycholinguistic research established that the ratio of such errors correlates with the distance between numeral and the noun in terms of the number of intervening items. In this note we briefly review this line of inquiry and outline two possible explanations for the distance effect: (i) the cost of maintaining and/or retrieving the numeral in the working memory, and (ii) cumulative activation of the plural markings on the intervening adjectivals.
Keywords: numeral, syntactic dependency, language processing, working memory, activation, Bulgarian
Published in RUNG: 12.02.2024; Views: 2073; Downloads: 6
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Abstract: Aims: Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods: The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results: We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Keywords: astronomical databases, data analysis, statistics, image processing
Published in RUNG: 12.12.2023; Views: 2428; Downloads: 11
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Abstract: This study investigates the impact of language proficiency on the computation of scalar implicatures (e.g., ”some” implying ”not all”) and compares the Pragmatic Default Hypoth- esis and the Pragmatic Transfer Hypothesis. Six scalar terms were studied among native English speakers, native Slovenian speakers, and Slovenian second-language (L2) learners of English. The findings mostly support the Pragmatic Transfer Hypothesis, as the rate of scalar implicatures in the English-L2 group generally aligned with rates in their native language, Slovenian. This suggests that scalar implicature judgments in one’s L2 reflect pragmatic patterns in one’s first language.
Keywords: Second Language processing, Scalar implicatures, Scalar diversity
Published in RUNG: 03.10.2023; Views: 2136; Downloads: 7
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Abstract: Aims: With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods: We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results: Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method rapidly detects more sources not only in low and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Keywords: astronomical databases, data analysis, image processing
Published in RUNG: 23.01.2023; Views: 3112; Downloads: 0
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