dataesr/scientific-paragraphs-categorization huggingface dataset:
Image cutouts and sky background subtraction The unique low surface brightness demands of our tidal stream survey requires bespoke sky-subtraction and co-addition of the calibrated survey images in the vicinity of each target galaxy. Here, we briefly describe our custom analysis, which utilizes large portions of the legacypipe 4  software infrastructure, the dedicated pipeline developed by the DESI Legacy Imaging Surveys team for source detection and model fitting of this survey imaging. One advantage of utilizing legacypipe is that our custom analysis produces a standard and well-documented set of imaging and catalog data products. We begin from the photometrically and astrometrically calibrated CCD images produced by the Legacy Surveys imaging team (see  Dey et al. 2019, for details). Next, for the predefined custom footprint of each galaxy in our sample, we read the set of CCD exposures contributing to that field.
Survey design and sample selection To facilitate comparisons with simulations, we define a straightforward sample of isolated galaxies based only on luminosity and recessional velocity given by the HyperLeda database 5   (Makarov et al. 2014). We consider galaxies with line-of-sight velocities in the Local Group rest frame  (Karachentsev & Makarov 1996)  in the range 2000 < V LG < 7000 km s -1 (30 𝐷 100 Mpc), with a 𝐾-band absolute magnitude M 𝐾 < -19. 6 and within the footprints of the surveys described in Sec. 2. 1. The majority of 𝐾-band magnitudes are taken from the 2MASS survey  (Skrutskie et al. 2006 ) and corrected for foreground extinction using reddening values from  Schlafly & Finkbeiner (2011) , assuming 𝑅 𝑉 = 3. 1. No colour or morphological selections are imposed on our sample.
Applying these criteria to the HyperLeda database yields a total sample of ∼ 3100 target galaxies. The luminosity range of our sample ensures that the target galaxies have a broad range of morphologies, star formation rates and stellar masses. This selection includes ∼ 940 Milky-Way analog systems, which are defined by their K-band luminosity (-24. 6 < 𝑀 𝐾 < -23. 0) and the local environment criteria given in  Geha et al. (2017). Our lower distance bound of ∼ 30 Mpc ensures that the virial radius of a typical galaxy in the sample (∼ 250-300 kpc) can be covered with a field of view (FOV) of 30 × 30 centered on a target (see Sec. 2. 2). Furthermore, the image quality from the DESI Legacy Imaging Surveys is sufficiently high to discover stellar streams even at the upper distance bound of our survey, ∼ 100 Mpc.
Detection of stellar tidal streams Streams are inherently non-symmetric and elongated, making them extremely hard to distinguish from the noise with classical detection methods. Therefore, we use NoiseChisel  (Akhlaghi and Ichikawa 2015)  which is installed as part of GNU Astronomy Utilities 0. 14  Table  1. List of galaxies with stellar streams and other faint features discovered during the proof-of-concept phase of our new survey. All the columns are based on homogenized information collected from the HyperLeda database. (Column 1): Galaxy name; (column 2): Morphological Hubble type of the galaxy; (column 3): Numerical code (T) in the de Vaucouleurs scale defined in RC2; (column 4): Apparent total 𝐵-band magnitude reduced to RC3 system; (column 5): Apparent total 𝐾 -band magnitude mostly based on 2MASS data; (column 6): Radial velocity with respect to the Local Group centroid  (Karachentsev & Makarov 1996) ; (column 7): Redshift distance 𝐷 assuming 𝐻 0 = 73 km s -1 Mpc -1 ; and (column 8): Field of view of the image cutouts showed in Figs.
3  and 4  (Gnuastro 6  ). NoiseChisel uses thresholds that are below the sky level to detect extremely faint signals without making assumptions about the shape or surface brightness profile of the sources. It then uses simple mathematical morphology operators (erosion and dilation) to extract a contiguous signal at low surface brightness. For example Akhlaghi (2020) describe a successful application to detect the outer wings of M51 to a surface brightness of 25. 97 mag arcsec -2 (𝑟-band) on a single-exposure (∼ minute exposure) SDSS image. Figure  5 (a) shows an example of NGC 4981 in the 𝑧-band from our dataset 7. All pixels that do not belong to NGC 4981 and its stream have been masked in panel (b), as well as all foreground and/or background sources overlapping the stream. Line-of-sight contamination was identified as clumps over NoiseChisel's detections, using Seg-Chisel was given a Gaussian kernel of FWHM= 3 pixels (default 2 pixels), truncated at 4 times the FWHM.
Automatic detection of stellar streams with Deep Learning algorithms We will complement our visual identification of tidal streams by employing convolutional neural networks (CNN; ). Classification of mergers, in particular, relies on the identification of diffuse features  (Pearson et al. 2019a ), demonstrating that this technique is ideally suited to identifying stellar streams. CNNs also have the advantage of being reproducible: the same classification (stellar stream or no stellar stream, in this case) will be reported every time the image is passed through the network. Walmsley et al. (2019)  presented the first attempt to identify faint tidal features around galaxies from the CFHTLS-Wide Survey using CNNs. The small number of previously identified tidal streams complicates the training of the CNN, which usually require a large training dataset.
OUTLOOK AND COMPARISON WITH COSMOLOGICAL SIMULATIONS In the previous sections we have described the ideas and techniques of our new Stellar Stream Legacy Survey and we have shown proofof-concept results for 24 new stellar streams detected in the local Universe. In this section, we discuss how the depth, coverage and uniformity of the full survey dataset can be exploited to address two key methodological and scientific problems related to stellar streams that we plan to undertake in future: morphological classification and comparison to cosmological simulations.
4). • The current generation of cosmological volume simulations is well matched to the spatial scale, resolution and sample size of our data. Realistic mock images generated from these models will enable quantitative comparisons with the results of our survey. (Sec. 4. 2). The full dataset of the Stellar Stream Legacy Survey will be presented in a forthcoming paper. Our analysis of its images will act as a pathfinder for future surveys with higher spatial resolution and greater depth, including those with Euclid, the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope.
ACKNOWLEDGEMENTS We thank  Eva K. Grebel ,  Facundo A. Gómez ,  Aaron Romanowski  and  Emilio Galvez  for useful comments. DMD acknowledges financial support from the  Talentia Senior Program  (through the incentive  ASE-136 ) from  Secretaría General de Universidades, Investigación y Tecnología, de la Junta de Andalucía. DMD and JR acknowledge funding from the  State Agency for Research of the Spanish MCIU  through the " Center of Excellence Severo Ochoa " award to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). APC is supported by the  Taiwan Ministry of Education (MoE) Yushan Fellowship and Taiwan Ministry of Science and Technology (MoST)  grant  109-2112-M-007-011-MY3. This work made use of HPC facilities at the  National Tsing Hua University Center for Informatics and Computation in Astronomy (CICA) , supported by grants from  MoE and MoST.
This work has also been supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant  AYA2014-56795-P. This work was supported in part by  World Premier International Research Center Initiative (WPI Initiative) ,  MEXT, Japan. We acknowledge the usage of the HyperLeda database (http://leda. univ-lyon1. fr). AP and DMD acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) -Project -ID 138713538 -SFB 881  ("The  Milky Way System ", subprojects A1 and A2). JR acknowledge financial support from the grants  AYA2015-65973-C3-1-R  and  RTI2018-096228-BC31  ( MINECO/FEDER , UE) and support from the  State Research Agency (AEI-MCINN)  of the  Spanish Ministry of Science and Innovation  under the grant " The structure and evolution of galaxies and their central regions " with reference  PID2019-105602GB-I00/10.
13039/501100011033. MA acknowledges support from the  Spanish Ministry of Economy and Competi-tiveness (MINECO)  under grant number  AYA2016-76219-P  and EU's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement No  721463  to the SUNDIAL ITN. W. J. P. has been supported by the  Polish National Science Center  project  UMO-2020/37/B/ST9/00466. CSF acknowledges support by the  European Research Council (ERC)  through  Advanced Investigator grant DMI-DAS  ( GA 786910 ) and by the  Science and Technology Facilities Council (STFC)  consolidated grant  ST/P000541/1. This work used the  DiRAC@Durham facility  managed by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). The equipment was funded by  BEIS  capital funding via  STFC  capital grants  ST/K00042X/1 ,  ST/P002293/1 ,  ST/R002371/1  and  ST/S002502/1 ,  Durham University  and  STFC  operations grant  ST/R000832/1.
Support for this work was provided by  NASA  through the  NASA   Hubble Fellowship  grant # HST-HF2-51466. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. This work was partly done using  GNU Astronomy Utilities  (Gnuastro, ascl. net/1801. 009) version 0. 14. Work on Gnuastro has been funded by the  Japanese MEXT scholarship  and its  Grant-in-Aid for Scientific Research  ( 21244012 ,  24253003 ), the  European Research Council (ERC)  advanced grant  339659-MUSICOS , and from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant number  AYA2016-76219-P.
Symptom checker diagnosis Prior to their rheumatology visit, patients completed two SC, bechterew-check (BC; www. becht erew-check. de) and Ada (www. ada. com). BC is an axSpA-specific online questionnaire based on the ASAS criteria, consisting of 16 questions, classifying answers as likely or unlikely for axSpA. Ada is a freely-available medical app not limited to rheumatology. The artificial intelligence-driven chatbot questions are dynamically chosen, and the total number varies depending on the previous answers given. Ada provides a top (D1) and up to five disease suggestions (D5), their respective probability and urgency advice. Disease suggestions were compared to the final diagnosis reported on the discharge summary report. Patient acceptance of symptom checkers was measured using the net promoter score  [20]  (NPS), which is based on a 11-point numeric rating scale (0-10).
Despite its small size and its monocentric study nature, this study adds important evidence on telemedicine in rheumatology, as they were requested by EULAR  [10]. Preliminary data of this study was presented at the American College of Rheumatology congress 2022  [32]. Our results have to be confirmed in larger studies in axSpA Final diagnosis reported on the discharge summary report served as the gold standard (GS). Based on this, the sensitivity, specificity and diagnostic accuracy of the two SC bechterew-check (BC) and Ada (ADA1 = top1 diagnosis, ADA5 = top5 diagnoses) were determined. Students 1 + 2 (STUD1 + 2) and physician 1-3 (PHYS1-3) decided asynchronously based on SC results, results for CRP and HLA-B27 and imaging, without ever having actually seen the patient. The mean time (T) for telehealth diagnosis per patient case vignette is listed in seconds (s).
The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users' comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeaware precision (TaP) as an early detection metric to asses the performance of the presented methods.
We consider the results previously obtained in  [6]  using the fixed, threshold, and dual early detection models and standard syntactic and semantic features (i. e. , term frequencyinverse document frequency and latent Dirichlet allocation) for cyberbullying as baselines, and we explore several alternatives to improve the outcome. For this purpose, we extend our experiments to two different datasets, and the features extracted are limited to the comments (without considering user or media features). Moreover, we propose a new set of features (i. e. , Doc2Vec) and we provide two improvements to the baseline models. In particular, we identify and address the following research questions: • RQ1: Can Doc2Vec features significantly improve performance for the early detection models? • RQ2: Can multiple instance learning improve results for the early detection models? Our contributions can be summarised as follows.
They also include images and caption features, concluding that they can serve as a powerful predictor for future cyberbullying. Other authors use support vector machines (SVM) and a lexical syntactical feature to predict offensive language, achieving high precision values  [10]. Also interesting is the work presented in  [11] , which focuses on network-based features and shows how they can be very helpful for the detection of aggressive user behaviour. Rafiq et al. provide a dataset for the Vine social network and explore the detection of cyberbullying using several machine learning models, achieving the best results with AdaBoost, closely followed by random forest  [12, 13]. Following this work, Hosseinmardi et al. [14]  used an Instagram dataset to further explore cyberbullying detection by incorporating multimodal text and image features as well as media session data.
In fact, most works focus on improving performance regarding how successfully a cyberbullying post or session is identified, and therefore, standard evaluation metrics are used, such as accuracy, precision, recall, F-measure, or area under the curve  [31] [32] [33]. Among those works that provide a time-aware evaluation, we find  [34] , where the authors focus on an early detection of cyberbullying to minimise the damage caused to victims. A basic time-aware evaluation is defined by dividing the original dataset in 10 independent subsets (or chunks), which are sequentially processed by the models. Performance is measured using F1-score at each chunk, but models are not penalised for late detections, and best scores are obtained mainly after processing at least half the dataset. One of the research questions in  [35]  refers explicitly to the capacity of the proposed method, named HENIN, to provide early detection of cyberbullying.
Features Previous works, as seen, for example, earlier in  [6] , have used specific social media features in their models, but this is precisely what we intend to avoid. Therefore, we did not take into consideration any number of features regarding users' followers, media session features such as number of likes or sharing, nor specific media features such as content or emotions extracted from different multimedia (e. g. , pictures or videos). On the contrary, our main and only focus refers to users' comments on the media provided by the different social networks. Therefore, from the comments for both Instagram and Vine datasets, we considered the following groups of features based on previous works:.
• Impolite comments: Following  [14, 39] , we identified profane words for each post, where profane words are obtained from a dictionary. For each comment, we calculated its percentage of profane words, and we defined a comment as rude if it contained at least one profane word. For each session, we computed the percentage of rude comments. • Sentiment analysis: Following  [13] , we measured polarity (positive and negative) and subjectivity for each comment. For each session, we calculated average values and the percentage of negative and subjective comments. • Tf-idf: For each comment, we built a Tf-idf (term frequency-inverse document frequency) representation, following  [6]. We performed a grid search for optimal hyperparameters using 10% of the Instagram dataset. Stopwords were removed and unigrams, bigrams, and trigrams were considered.
Datasets In this research, we used two datasets tagged for cyberbullying from two different social networks: Instagram and Vine. The Instagram social network is a media-based social network mainly focused on images, where users can post and comment on them. The Vine social network is based on a mobile application that allows users to record and edit short videos to publish on their profiles, while other users can watch and comment on these videos. The Instagram dataset was collected and described in detail by Hosseinmardi et al. [14, 39]. The authors started with a sample of 25 thousand Instagram users with public profiles, and from there, a collection of more than 3 million media sessions was extracted. Sessions were limited to those with at least 15 comments, and to achieve a more balanced dataset, only sessions with at least one profane word in their comments were considered.
As a result, 2218 media sessions (with their comments) were labelled for cyberbullying using human contributors. Each contributor received a level of trust based on a series of tests and quizzes. A weighted version of the majority voting method was applied, where a minimum trust of 60% is required to keep the session in the dataset, leaving a total of Instagram sessions. A similar approach was followed to create the Vine dataset  [12, 13]. The sample started with more than 650 thousand media sessions, which, after removing those sessions with fewer than 15 comments, was reduced to 436 thousand sessions. Then, the sessions were grouped in terms of the percentage of comments including profane words, and a representative subsample for all groups was considered for labelling, obtaining a total of 969 media sessions.
Each one of these sessions was tagged by 5 reviewers, and sessions with a confidence level below 60% were discarded from the dataset, obtaining a final number of 747 Vine sessions. Table  1  presents the main statistics for both datasets. The Instagram dataset is bigger, both in terms of sessions and comments, while the Vine dataset provides a higher rate of comments per session than Instagram. We also analysed the main language used on the different social media networks. It is interesting to note that Vine sessions show a higher variability of languages, while most Instagram comments are in English. We consider that this could be motivated by the smaller size of the comments in the Vine dataset (see Figure  1 ), which points towards the use of more abbreviations and slang. Figure  1  shows the distribution of the number of words per comment for both datasets, whereas Figure  2  shows the distribution of profane words.
From the graphs, we observe that Instagram comments are much longer than those on Vine. With an average of nearly 10 words per comment on Instagram, and a long queue with posts reaching nearly 400 words, a relatively high standard deviation of 17. 46 is produced. On the other hand, Vine averages 5. 7 words per comment (maximum is around 30 words) and a standard deviation of 5. 41, all in all indicating a much smaller post size for this social network. Moreover, Figure  1a  shows a slight tendency to use more words on bullying comments on Instagram, which is just minorly present at the tail of the word distribution for the Vine dataset (Figure  1b ).
Baseline As early detection baseline models, we consider those described in previous works  [6, 44, 45]  that have reported relevant results when evaluated with early detection metrics: fixed, threshold, and dual. To the best of our knowledge, no other previous results using time-aware metrics such as TaP as evaluation metrics for early detection models have been presented. As introduced, there are few cyberbullying early detection studies employing early detection metrics solely using comment data. On account of the state-of-the-art results, we can cite the following research using the Instagram and Vine datasets. The authors of  [27]  obtained the best results for the Instagram dataset, using linear regression with an F1 of 0. 64 ± 0. 03 (precision 0. 79 ± 0. 03 and recall 0. 55 ± 0. 03).
This is also shown in  [29]  with an F1 value around 0. 65 (precision 0. 873 ± 0. 04 and recall 0. 517 ± 0. 05). In the case of the Vine dataset, an F1 of 0. 59 ± 0. 04 was also reached with linear regression (precision 0. 62 ± 0. 05 and recall 0. 57 ± 0. 05). These values, although not directly comparable, can give a reference through the values obtained in  [6]  for the Vine dataset. For all our experiments, the following machine learning models were used for fixed, threshold, and dual: AdaBoost (ADA), extra trees (ET), logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM).
Baseline In this section, we present the baseline results using the TaP metric, as introduced in  [43]  for all three models used on the experiments: the fixed model, threshold model, and dual model. Firstly, in Table  2 , it must be noticed that even if the general values differ, the best point of decision for each fixed model is almost the same for all the models and datasets. This can be explained by the fact that the amount of information processed is almost the same for all models and both datasets (i. e. , point 10, in eight cases out of ten), which leads to finding the optimal point of decision related to the penalisation applied by the metric. On Instagram, the best baseline performance is obtained with LDA for naïve Bayes (at point 10) reaching 0. 3755. On the other hand, for the Vine dataset, the best baseline performance combines Tf-Idf and LDA features for naïve Bayes (also at point 10), obtaining TaP = 0.
3794. 3  shows the results for the threshold model, where more differences can be seen, as variation in thresholds does not correlate directly with the amount of items processed to make a decision. This contrasts with the results shown previously, but can be explained due to the fact that a higher threshold could mean a lower probability of making the wrong choice, but also could increment the number of items processed to take the final decision. As for the results, focusing on Instagram, the best baseline score is 0. 7561, obtained with Tf-idf features, using naïve Bayes, and the threshold early detection model parameters are th+ = 0. 5 and th-= 0. 9. For the Vine dataset, LDA features are used, also using the naïve Bayes model, and the threshold parameters are also th+ = 0. 5 and th-= 0.
9. The performance obtained for the best baseline features in this dataset is TaP = 0. 399. Finally, in Table  4 , even taking into account the difficulty in the interpretation of the results due to the complexity of the dual model (comprising one model for positive cases and other for negative cases, each one with its own features and thresholds), it can be seen that the same pattern applies, with slight differences in the configuration of the models, as stated for the fixed and threshold baselines. For instance, the same values are obtained for all combinations of negative features in the same models, as is the case of NB for both the Instagram and Vine datasets. For these results, different threshold configurations were tested, using low values for the positive threshold and high values for the negative threshold, as in the threshold early detection model.