dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , density and optical depth) of the broad-line region and the scattering medium responsible for the polarization of the Hα emission line (and continuum); and (iv) model the Stokes parameters using the polarization radiative transfer code stokes. Methods. Using the FORS2 instrument at the European Southern Observatory's (ESO) Very Large Telescope, We performed spectropolarimetric observations of three NLSy1: Mrk 1044, SDSS J080101. 41+184840. 7, and IRAS 04416+1215. We used the ESO Reflex workflow to perform a standard data reduction and extract the natural and polarized spectra. We then modeled the Hα region in the reduced spectra using iraf spectral fitting procedures and estimated the Stokes parameters and the viewing angles of the three sources. We modeled the Stokes parameters, inferred the properties of the scattering media located in the equatorial and polar regions, and simulated the spectra observed both in natural light and in polarized light using the polarization radiative transfer code stokes.
(2022)  showed that the best-fitting model in the X-ray band is composed of a soft excess, three ionized outflows, neutral absorption, and a reflection component. Prominent soft X-ray excess supports the claim for a super-Eddington accretion rate in this source. Through a dedicated monitoring campaign using the Lijiang 2. 4 m telescope under the SEAMBH Project,  Du et al. (2016)  reported the measurements of the Hβ FWHMs for the three sources (see Table  5  in their paper): Mrk 1044 (1178 ± 22 km s -1 ), SDSS J080101. 41+184840. 7 (1930 ± 18 km s -1 ), and IRAS 04416+1215 (1522 ± 44 km s -1 ). This paper is organized as follows: Details on the performed observations and data reduction are reported in Sect. 2. In Sect. 3, we present the analysis of spectropolarimetric data obtained using the VLT and the archival spectroscopic data for each source.
Observations and data reduction Our spectropolarimetric observations were performed with the FORS2 instrument mounted on the UT1 telescope of the 8. 2 m ESO VLT. The observations were taken using the GRISM-300I in combination with the blocking filter OG590. The spectral range of our data is 6000-10 000 Å. The 0. 7-arcsec wide slit was oriented along the parallactic angle, and the multi-object spectropolarimetry observations were performed with a 2048 × 2048 pixel CCD with a spatial resolution of 0. 126 arcsec pixel -1. The spectropolarimetric observation of Mrk 1044 was accomplished on 2016 October 27. The source SDSS J080101. 41+184840. 7 was observed on 2016 December 26, 2016 December 30, and 2017 January 2. Finally, the IRAS 04416+1215 was observed on 2016 November 6. For all observational runs, we used the  Patat & Romaniello (2006)  prescription of observing with four quarter-wave plate angles  (0, 22.
RA Dec P χ Q U [J2000] [J2000] [%] [ • ] [%] [%] HD 64299 (unPol. ) 07:52:25. 4 -23:17:47. 5 0. 02 ± 0. 02 89 ± 66 -0. 017 ± 0. 009 0. 006 ± 0. 007 Ve 6-23 (Pol. ) 09:06:00. 01 -47:18:58. 2 7. 12 ± 0. 03 170 ± 1 6. 84 ± 0. 06 -1. 99 ± 0. 04 Notes. The star type is indicated as "Pol. " if the star is polarized or as "unPol. " if it is unpolarized, following  Cikota et al. (2017). Columns show the name of the source, the coordinates (taken from the SIMBAD Astronomical Database: https://simbad. cds. unistra. fr/simbad/), the averaged degree of polarization (P), the averaged polarization angle (χ), and the averaged Stokes parameters: Q and U. is small, and the standard measurements were reproduced within errors, in particular the angle that depends on the half-wave plate (HWP) achromaticity.
Middle and right columns: fit for the total flux for each source corresponding to the minimum χ 2 and the second-to-minimum χ 2. polarization. In general, a higher value for continuum polarization  (Goodrich 1989 ) may be due to a wavelength range that is different from our measurements, as we do not have data for the rising blue side of the spectra. Goodrich (1989)  reports spectropolarimetric observations of Mrk 1044 and the general effect of degree of polarization rising toward the blue side. We did not notice this effect, but our observations have different wavelength ranges (for the Mrk 1044 observation in  Goodrich (1989) , it is 4436-7210 Å, whereas our measurements start from ∼6000 Å). The mean degree of polarization reported for Mrk 1044 in our work is 0. 15% ± 0. 01%, whereas in  Goodrich (1989) , the mean degree of polarization is 0.
9 from their paper). Finally, we calculated R FeII from EW(Fe ii) and EW(Hβ) BC from the catalog of  Shen et al. (2011)  for each source from the sample of  Capetti et al. (2021). For Fairall 9,  Jiang et al. (2021)  determined the viewing angle of the source as 50 • based on the polarization level and polarization angle fit to the data. For our sources, we determined this viewing angle from a simple comparison of the FWHMs in natural and polarized light, following Eq. (  6 ). To achieve consistency with our sample and to gain insight into the accuracy of the viewing angle determination, we used the data from Jiang et al. (  2021 ) and adopted our method, based on Eq. (  6 ). In this case, we obtained the value ∼27 • for the viewing angle, which is smaller compared to the original determination by  Jiang et al.
Searching for the driver with principal component analysis Principal component analysis (hereafter, PCA) is a "linear" dimensionality reduction technique where the dataset is projected onto a set of orthogonal axes that explain the maximum amount of variability in the dataset. The PCA technique works by initially finding the principal axis along which the variance in the multidimensional space (corresponding to all recorded properties) is maximized. This axis is known as "eigenvector 1". Subsequent orthogonal eigenvectors, in order of decreasing variance along their respective directions, are found, until the entire parameter space is spanned (see, for example,  Boroson & Green 1992; Kuraszkiewicz et al. 2009; Wildy et al. 2019; Martínez-Aldama et al. 2021). The PCA method is particularly useful when the variables within a dataset are highly correlated.
2000b; Shen & Ho 2014; Marziani et al. 2018; Panda et al. 2018 Panda et al. , 2019a,c),c). This choice of using only the direct observables allowed us to remove redundancies in the PCA that enter when the parameters that are derived (e. g. , viewing angle, black hole mass, and Eddington ratio) using the direct observables are incorporated within the analysis (see  Martínez-Aldama et al. 2021 , for a detailed study on this issue). Thus, in our PCA, we used 25 sources with five properties. Similar to  Wildy et al. (2019) ,  Martínez-Aldama et al. (2021) , we used the prcomp module in the R statistical programming software. In addition to prcomp, we used the factoextra 10 10 https://cloud. r-project. org/web/packages/ factoextra/index. html package to visualize the multivariate data at hand and especially to extract and visualize the eigenvalues and variances of the dimensions.
There is no well-accepted way to decide how many principal components are enough, we used the "scree plot" method to evaluate the number of principal components that best describe the variance in our dataset. A scree plot shows the variances (in percentages) against the numbered principal component and allows the number of significant principal components in the data to be visualized. The number of components is determined at the point beyond which the remaining eigenvalues are all relatively small and of comparable size  (Peres-Neto et al. 2005; Jolliffe 2011 ). The top panel in Fig. 11  shows the scree plot obtained from our PCA. It can be seen that the first four principal components explain 96. 7% of the total variance in our dataset, with the first principal component (PC1) contributing 41.
Acknowledgements. We are very grateful to the Referee for very helpful and constructive remarks which help to improve considerably the content of the manuscript. M. S. acknowledges support from  Polish Funding Agency National Science Centre , project  2021/41/N/ST9/02280 (PRELUDIUM 20 ), the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  950533 ) and from the  Israel Science Foundation , (grant  1849/19 ). This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. [ 951549 ]). M. S. is grateful to  S. Bagnulo  for the discussion about data reduction and all tutors and organizers of Torun Summer School 2019: Polarimetry as a diagnostic tool in astronomy for providing a review of methods and techniques developed for the polarimetry data.
We also thank organizers and participants of the conference AGN and Polarimetry conference: Towards a panchromatic understanding of the polarization of Active Galactic Nuclei for discussion which helps to improve this work. The project was partially supported by the  Polish Funding Agency National Science Centre , project  2017/26/A/ST9/00756  ( MAESTRO 9 ), and  MNiSW  grant  DIR/WK/2018/12. S. P. acknowledges the financial support of the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  Fellowships  164753/2020-6  and  300936/2023-0. Ð. S. acknowledges support by the  Ministry of Education, Science and Technological Development of the Republic of Serbia  through contract no. 451-03-9/2021-14/200002  and by the  Science Fund of the Republic of Serbia ,  PROMIS 6060916 , BOWIE.
M. L. M. A. acknowledges financial support from  Millenium Nucleus   NCN19-058  (TITANs). L. Č. P. is supported by the  Ministry of Education, Science and Technological Development of Serbia , the number of the contract is  451-03-68/2020-14/200002  and L. Č. P. acknowledges the support by the  Chinese Academy of Sciences President's International Fellowship Initiative (PIFI)  for visiting scientist. Funding for the  Sloan Digital Sky Survey  (SDSS) has been provided by the  Alfred P. Sloan Foundation , the  Participating Institutions , the  National Aeronautics and Space Administration , the  National Science Foundation , the  US Department of Energy , the  Japanese Monbukagakusho , and the  Max Planck Society. The SDSS website is http://www. sdss. org/. The SDSS is managed by the  Astrophysical Research Consortium (ARC)  for the Participating Institutions.
Study population and data collection The EPIC cohort is a multicenter prospective study including 521 323 men and women, designed to investigate the associations between nutritional, lifestyle, metabolic, and genetic risk factors, and cancer risk. Participants were enrolled between 1992 and 1999, mostly aged 30e70 years, from the general population of 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden and United Kingdom). Characteristics of the study population and baseline data collection methods have been described previously  [44]. All participants gave written informed consent, and the study was approved by the Ethics Review Committee of the International Agency for Research on Cancer (IARC) and by the local ethical committees of the individual EPIC centers.
Lifestyle questionnaires were used to collect data on sociodemographic characteristics, tobacco smoking, physical activity, education, medical history, and reproductive history  [44]. Anthropometric variables were measured according to standardized protocols  [44]. The usual diet over the previous year was assessed at recruitment using a validated country/center-specific dietary questionnaire  [44, 45]. In most countries, extensive quantitative food frequency questionnaires (FFQs) or semi quantitative FFQs were used. The standardized EPIC Nutrient Database was used to estimate total energy and nutrient intakes  [46]. About 386 000 participants also provided a blood sample at recruitment, collected according to a standardized protocol in France, Germany, Greece, Italy, the Netherlands, Norway, Spain and the United Kingdom  [44].
Selection of covariates for these five biomarkers was also tested for each score based on the stepwise method with the adjustment covariates previously mentioned forced in the model. Normalization was performed using R software (v3. 5. 2), and statistical analyses were performed using SAS Enterprise Guide software (v7. 1, Cary, NC, USA). All tests were two-sided and P value < 0$05 was considered significant. The Pipeline for normalization is available at: https://code. iarc. fr/viallonv/pipeline_ biocrates.
Furthermore, in mutually adjusted models, TNFa only remained significantly associated with the ISD, the DII and the E-DII r but not with E-DII, and was also the only parameter selected in the stepwise regression. The differences between the scores computation were previously detailed  [43]. The DII and ISD are not adjusted for energy intake and they differ by the reference population used to standardize the dietary intake and for the food parameters included. The E-DIIs are adjusted for energy intake but differ by the way energy was taken into account: for the E-DII r , the intakes were adjusted for energy intake using the residual method  [61]  while for the E-DII the density method was used, and the intakes were converted to an amount per 1000 kcal of energy intake and used an energy-adjusted global comparative database.
Acknowledgements We are grateful to all the participants who have been part of the project and to the many members of the study teams at the  University of Cambridge  who have enabled this research. The authors thank all participants in the EPIC cohort for their invaluable contribution to the study. The authors also thank  Bertrand H emon  ( IARC ) for his precious help with the EPIC database. We acknowledge the use of data and biological samples from the  EPIC-Oxford cohort, PI Tim Key , from the  EPIC-Utrecht  cohort, PI  Roel Vermeulen , from the  EPIC-Aarhus cohort, PI Kim Overvad. The authors thank the  Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , the  Netherlands , for their contribution and ongoing support to the  EPIC Study.
The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods.
For example, a comparison between reader and writer annotations shows that they disagree 25% of the time  [17]. To avoid this problem, experience sampling can be used to generate self-annotated emotion labels. For example,  [18]  gathered anxiety scores at the time when individuals posted tweets and compared self-reported anxiety with emotion text analysis. The results are correlations of at most 0. 24, calling for studies that can leverage large datasets to identify emotional states more accurately. One must note, however, that we cannot assume that self-reported emotion labels are perfectly predictable from social media text, with only the natural language processing models as the missing piece. The upper limit on the performance of an emotion identification method is likely to be below 100%, as for example, well-being indicators correlate with each other correlations of approximately 0.
84, which can be achieved with modern language models  [19]. New platforms to share emotional experiences with other users offer the possibility to gather large-scale datasets with emotion self-annotations. Vent is an example that offers a particularly good source of self-annotated data, as the dataset available for researchers has millions of posts  [20]  and the design of the platform is precisely to share emotions rather than a smaller functionality as in other platforms. Recent research on Vent has shown the difficulty to predict Vent precise mood labels from text  [21] , but it is still left to explore how Vent can be used to infer more coarse emotion labels that can match discrete emotion classes from psychological research. In this work, we focus on a subset of Vent tags that can be mapped to standard emotional states, with the goal of training a better and more robust emotion detection model that can be applied to other text sources, especially from other social media.
Related work Emotion classification models mainly follow feature-based or neural approaches. Featurebased methods  [22]  employ handcrafted features built from resources such as emotion lexica. Neural approaches often rely on pre-trained representations such as word embeddings and contextual language models (LMs). The use of transformer-based LMs has been shown to yield state-of-the-art performance on natural language processing benchmarks. For emotion classification, recent research works have achieved better performance using pre-trained LMs  [23] [24] [25]. Learning representations for affect A number of existing works learn representations for affective tasks. DeepMoji  [26]  is a neural network trained for predicting emoji in tweets using a large distant-labeled dataset considering 64 emojis as labels.
This approach is similar to those proposed in  [33]  and provides a further theoretical basis as well as empirical validation. Linear probing is a non-destructive and computationally cheap approach that freezes the parameters of the base model and only updates the parameters of the prediction head during training. In this work, we consider this strategy in the context of text classification for the identification of emotion. Emotion classification datasets Supervised models are trained and evaluated against emotion text datasets that are either constructed by manual labeling or automatically by using additional data sources and structures. Manually-labeled datasets are usually comparatively small while automatically-constructed datasets are built by identifying emotion-bearing patterns of expression such as hashtags in the case of Twitter.
The annotation of emotion datasets can also be divided into reader-labeled and writer-labeled datasets. Reader-labeled datasets are assigned labels by the annotators post-hoc based on their perception of the emotions expressed by a given content. On the other hand, writerlabeled datasets are usually self-annotated by the writer of the message to reflect their emotion. Most of the existing work on emotion classification has drawn on manually annotated, automatically constructed, and reader-labeled datasets. Recently, large-scale writerlabeled datasets have been introduced  [20, 34]  and they are yet to become part of the benchmarks of emotion detection tasks. A notable example is the Vent dataset  [20] , which is produced by a specialized social media platform with the goal of encouraging people to write about their feelings and provide a tag.
The quality of the self-annotated emotion data drawn from Vent was examined and led to the conclusion that the tagged emotional expressions are indicative of emotional content  [35]. Furthermore, the distinction between reader-labeled and writer-labeled datasets was analyzed in  [21]  with the findings indicating that classifying the emotion labels of these datasets is a hard task when considering all available labels in the platform. As supervised methods tend to perform better than unsupervised ones and gathering manual annotations is time-consuming and expensive, this kind of self-annotated datasets offers a potential alternative beyond indirect self-annotations within the text as in Twitter hashtags.
Datasets The Vent dataset consists of 33 Million posts from the Vent social media app  [20]. Each post is annotated by its author with an emotion tag as a way to express their emotional state to others. While the dataset has 705 emotion tags, many are temporary tags about seasonal events that do not express a clear emotional state and the most frequent tags  are used on the vast majority of posts. Since Vent was designed to provide a nuanced expression of emotions rather than text classification, we mapped Vent emotion tags to a list of emotional states consistent with individual emotions from the affective science literature  [36]. This way, we map emotion tags with words close in dimensional models of emotion  [37]  into the same label, for example, mapping the tags angry and annoyed into the same label of Anger.
The precise mapping can be found in Table  1. Four of these emotion labels map to linguistic classes that have been consistently identified in emotional expression in text  [38] : Sadness, Anger, Fear, and Happiness. We added a fifth category Affection, which occurs more frequently than Happiness and shows a social orientation of the expression of positive emotions on social media. We pre-process the Vent dataset to generate a cleaner dataset of posts in English that were labeled by their authors with one of the tags of Table  1. We remove non-English posts using three language identification tools. 1,2,3 For a post to be included in our analysis, at least two out of the three methods had to agree on detecting it as in English. After that, we remove duplicates and tag memes (invitations for a challenge to answer a question), following the approach in  [35].
We remove posts with less than three words, excluding placeholders for links and user mentions in the word count. We also normalize the text by replacing multiple whitespaces with a single occurrence. We remove tab, new line and carriage return characters as well as Hypertext Markup Language codes. The resulting dataset contains more than nine million posts with metadata including the emotion labels, pseudonymized user ids, and timestamps when the post was written. In-domain evaluation datasets An overview of this study can be seen in Fig. 1 , including data sources and data splits for in-domain evaluation. We split the pre-processed Vent dataset into a training/development/test split with three disjoint test datasets to assess the capability of the model to generalize emotion identification.
The random test set contains a uniformly random selection of 10% of all posts in the Vent dataset. The user test set consists of all posts written by a random sample of 10% of the users. This way, no post in the training set has been written by any of the users in the user test set. The temporal test set contains the last 10% of the posts according to their timestamp, thus allowing us to evaluate the model with future data with respect to its training set. We additionally extracted another 10% random set from the remaining posts as a development set to guide model design before the final run of all tests. All these subsets are disjoint and the three tests allow us to evaluate if and how the model generalizes across posts, users, and time. The resulting exact counts of posts and emotion labels in all splits can be found in Table  2.