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1.
Speakers' errors in the use of the 'count form' in Bulgarian numeral phrases : possible sources of the distance effect
Penka Stateva, Julie Franck, Arthur Stepanov, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: numeral, syntactic dependency, language processing, working memory, activation, Bulgarian
Objavljeno v RUNG: 12.02.2024; Ogledov: 389; Prenosov: 3
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2.
Audio tampering identification : phase portrait analysis as a promising tool for audio forensics
S. S. Aswathy, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2023, objavljeni povzetek znanstvenega prispevka na konferenci

Ključne besede: audio forensics, tampering, signal processing
Objavljeno v RUNG: 09.01.2024; Ogledov: 371; Prenosov: 2
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3.
AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information
F. Stoppa, Saptashwa Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, Gabrijela Zaharijas, S. Bloemen, G. Principe, D. Malyshev, Veronika Vodeb, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: astronomical databases, data analysis, statistics, image processing
Objavljeno v RUNG: 12.12.2023; Ogledov: 444; Prenosov: 4
.pdf Celotno besedilo (10,31 MB)
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4.
AutoSourceID-FeatureExtractor : optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, Saptashwa Bhattacharyya, S. Caron, S. Bloemen, Gabrijela Zaharijas, G. Principe, Veronika Vodeb, P. J. Groot, E. Cator, G. Nelemans, 2023, izvirni znanstveni članek

Opis: Aims: In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods: The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results: We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
Ključne besede: data analysis, image processing, astronomical databases
Objavljeno v RUNG: 08.11.2023; Ogledov: 446; Prenosov: 7
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5.
Scalar diversity and second-language processing: the Pragmatic Transfer Hypothesis
Federica Longo, Bob Van Tiel, Penka Stateva, Greta Mazzaggio, objavljeni povzetek znanstvenega prispevka na konferenci

Opis: 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.
Ključne besede: Second Language processing, Scalar implicatures, Scalar diversity
Objavljeno v RUNG: 03.10.2023; Ogledov: 572; Prenosov: 3
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AutoSourceID-Light : Fast optical source localization via U-Net and Laplacian of Gaussian
F. Stoppa, P. Vreeswijk, S. Bloemen, Saptashwa Bhattacharyya, S Caron, G. Jóhannesson, R. Ruiz de Austri, C. Van den Oetelaar, Gabrijela Zaharijas, P.J. Groot, E. Cator, G. Nelemans, 2022, izvirni znanstveni članek

Opis: 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.
Ključne besede: astronomical databases, data analysis, image processing
Objavljeno v RUNG: 23.01.2023; Ogledov: 1130; Prenosov: 0
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8.
Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek

Opis: The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.
Ključne besede: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Objavljeno v RUNG: 30.06.2022; Ogledov: 1052; Prenosov: 0
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9.
Pitch processing in L1 and L2: German and Italian
Alessandra Zappoli, Francesco Vespignani, Stefan Bauman, Martine Grice, Petra Schumacher, predavanje na tuji univerzi

Ključne besede: Pitch processing, German, Italian, Deaccentuation, ERP, EEG, N400, Late Positivity, Given Information
Objavljeno v RUNG: 14.12.2021; Ogledov: 1474; Prenosov: 0
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10.
Pitch as common thread: semantic and pragmatic information conveyed through voice modulation
Alessandra Zappoli, prispevek na konferenci brez natisa

Ključne besede: Pitch processing, pragmatic integration, segmental and suprasegmental information
Objavljeno v RUNG: 14.12.2021; Ogledov: 1368; Prenosov: 0
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