dataesr/scientific-paragraphs-categorization huggingface dataset:
Ricardo et al. [9]  examined the possibility of integrating emotions into the available dataset and then, classifying hate speech using the newly created dataset. Their training dataset is prepared as a vector-based corpus, and each vector contains a few emotional scores, emotional intensities and sentiment polarity scores. The authors used a few ML classifiers and concluded that the emotion features help to increase the performance of the hate speech detection model. FADOHS is a framework implemented by Axel et al. [8]  to detect hate speech using sentiment and emotions. Their experimental results have shown that FADOHS surpasses the performance of the previously proposed hate speech detection models. Also a novel framework that utilizes graph, sentiment, and emotion analysis techniques to automatically detect Facebook pages that promote hate speech in comment sections concerning sensitive topics, is introduced in  [37].
They proposed a multi-task model to integrate sentiment and emotion knowledge and evaluated their model on two hate speech datasets containing Spanish tweets. Firstly, they trained and evaluated the model on the corresponding datasets with the Transformer-based BETO model to obtain a baseline for STL and then used a multi-task setting to compare the model performance with the baseline. Also in  [42] , authors proposed multi-tasking models to evaluate the impact of emotions, sentiment and target classification. They used Ekman's emotion models and binary sentiments of content. The authors concluded that the use of emotions, sentiment and target classification via MTL improved the performance of hate speech detection. However, their analyses focused only on a single hate speech category. Md Rabiul et al.
[43]  proposed another MTL model called AngryBERT to classify hate speech features. AngryBERT is based on sentiment features and a target identification approach. They evaluated the performance of the model compared to several state-ofthe-art hate speech classifier-based models including; Deep-Hate, CNN, LSTM, CNN-GRU etc. The AngryBERT model, which is based on sentiment features, outperformed the other baseline models. Moreover, a deep MTL framework is proposed by  [44]  using related tasks to hate speech such as offensive language identification, racism detection and sexism detection, while in  [45] , authors proposed the first joint model using emotion and abusive language detection in a multi-task setup. To accomplish our objective of the impact of fine-grained emotion in detecting implicit hate speech, we employ a MTL paradigm as well as STL and incorporate relevant knowledge to enhance the performance of hate speech detection on the target implicit dataset.
III. DATASETS As shown in Table  1 , our analysis and experiments rely on the utilization of five distinct labelled datasets, including three hate speech datasets (D1, D2, D3), one fine-grained emotion dataset (D4) and one sentiment dataset (D5). The experiments and the multi-task hate speech detection model evaluation are heavily based on the Latent Hatred dataset (D1) with the support of GoEmotion dataset (D4) and Se-mEval dataset (D5), while the other two hate speech datasets (D2 and D3) are used for the emotion analysis of hateful content to have a wider and more balanced range of data. Latent Hatred dataset (D1): This dataset  [5]  contains about 20K English tweets with not-hate and implicit hate categories. Implicit hate tweets divide into six main categories with the following distributions: Grievance (24.
2%), Incitement (20. 0%), Inferiority (13. 6%), Irony (12. 6%), Stereotypes (17. 9%), Threats (10. 5%) plus Other (1. 2%). The primary purpose of using this dataset is to analyze emotions in hate and non-hate content and perform a more detailed comparison of emotions used in each category to explore tricks used in tweets such as showing threats and abuse, which is more abstract compared to explicit hate. White Supremacy Hate (D2): This dataset  [33]  contains 10K sentences extracted from Stormfront, which are manually labelled into four main labels: hate (11. 29%), noHate (86. 09%), relation (1. 69%) and skip (0. 93%). We used first two labels for our emotion analysis in hate and non-hate data. Offensive Language hate (D3): This dataset  [18]  contains approximately 24K tweets with three main categories: hate speech (5.
8%), offensive language (77. 4%), and those with neither (16. 8%). In their results, tweets related to racist and homophobic classified as hate speech, and sexist tweets are generally classified as offensive. However, as explained in Subsection IV-B, we consider both offensive language and hate speech as hateful content in our analysis. GoEmotion dataset (D4): A human-annotated corpus consists of 58k Reddit English-language comments labelled with 27 classes including 12 positives, 11 negatives, and 4 ambiguous emotion categories described in Table  2. This dataset splits into a train set with a size of 43,410, and a test and validation set with a size of 5,427 and 5,426 respectively  [34]. We used this dataset in our MTL experiment for the task of fine-grained emotion detection. SemEval 2017 dataset (D5): This dataset  [35]  was released for Task 4 (subtask A) in SemEval 2017 which is a message polarity classification with positive, negative, or neutral sentiment.
B. EMOTIONAL CONTRASTS IN HATE VS. NON-HATE CONTENT This section mainly focuses on analyzing how emotional values are varied from hateful content to non-hateful content. This further helps us to gain insights into the types of emotional tendencies associate with hate speech in general and that certainly leads to tracking and monitoring implicit hate speech with certain emotional values. As stated in Section III, D1, D2, and D3 hate speech datasets have their own hate categories. In our analysis of emotions, the term 'hateful content' refers to the texts that fall under six hate categories: Grievance, Incitement, Inferiority, Irony, Stereotypes, and Threats, as identified in D1. Additionally, it includes texts categorized under the Hate category in D2, as well as those classified under hate speech and offensive language categories in D3.
To evaluate and gain a visual understanding of the emotion value variations in both hateful vs non-hateful content, we generated Figure  2  utilizing datasets. For both content, Figure  2  maps each sentiment level to the correlated Ekman level, and from Ekman level to its fine-grained emotion sub-categories. As illustrated in Figure  2. b, non-hateful content includes more positive sentiments than negative sentiment, whereas hateful content (Figure  2. a) includes higher negative content. In the analysis of non-hateful content, there is a relatively small difference between the occurrence of negative sentiments and ambiguous sentiments, with percentages of 30. 65% and 26. 63% respectively. However, in hateful content, the majority of sentiment portion is accounted for the negative label with over 65% of the total distribution, which is nearly doubled compared to non-hateful.
C. EMOTION ANALYSIS ON IMPLICIT HATE CATEGORIES In Section IV-B, the analysis is mainly focused on hate speech in general, but this section focuses on the analysis of fine-grained emotions within implicit hatred content. We used the Latent Hatred dataset (D1) for this analysis targeting six hate categories: incitement, inferiority, irony, stereotypical, threatening, and white grievance. Table  3  shows six main hate categories with their definition proposed by  [5]  and samples tweets and their respective emotion classed by the GoEmotion model. We analyzed sentiment and related Ekman emotions on hateful and non-hateful content in the D1. After that, emotion frequencies in each category were analyzed. Although implicit hate detection is more challenging than explicit hate, we found different emotion patterns in each implicit hate category.
A. EXPERIMENTAL SETUP The system depicted in Figure  5  contains several stages: Input: The input for our system consists of three datasets (D1, D4 and D5) that are utilized for learning features related to hate speech detection, emotion detection, and sentiment analysis tasks. Feature Extraction Module: We analyze the feature importance by doing the ablation study; we conduct an ablation study that compares the model's performance across five feature levels: i) text-only features  [5] ; ii) sentiment features; iii) Ekman-level emotion features; iv) fine-grained level emotion features; v) combination of sentiment and emotion features with text-only features. We acquire the emotion features as follows: for each sample in the dataset, we run the emotion model mentioned in Section IV-A and generate a fine-grained emotion vector v ∈ R n where n is the number of emotion categories.
Here, we use the original GoEmotion taxonomy and take n = 28 for the emotion features in both binary and multiclass classification. The emotion vector v contains fine-grained emotion information about the text. As shown in Figure  5 , after acquiring text features and emotion or sentiment features, we pass these features along with concatenated features (Concatenation Module) to models. The concatenation module performs simple splicing of the embeddings, for which several methods can be employed, including simple vector concatenation, gating, attention weighting etc. In our experiment, we choose the simple concatenation method with the best performance. Single-task Setup: STL focuses on training a model to perform a specific task using a dedicated dataset to optimize performance on a single task.
The pre-training allows BERT to learn a general understanding of language and context. Fine-tuning BERT on specific downstream tasks enables it to provide highly accurate predictions, which in our work, it can obtain a better result for implicit hate detection. In our experiment, we use fully connected layers as the fusion module followed by another fully connected layer as the classification head. The layer number is set to one, with the input size equaling the concatenated feature number. Multi-task Setup: MTL involves training a model to simultaneously learn multiple related tasks using a shared dataset to capture shared information and leverage it across multiple tasks, benefiting from the similarities and correlations between tasks  [46] ,  [47]. In our experiments, we use two auxiliary tasks for hate speech detection, namely, emotion detection task and sentiment analysis task.
C. EXPERIMENT TYPES We designed two types of experiments to support our objective: i) We conduct an experiment with several STL methods for implicit hate speech classification. In this experiment, we evaluate different text representations using sentiment, Ekman-level emotion and fine-grained emotion features along with text-only features. Due to the different modalities of text features and emotion features, we design a multimodal model to concatenate text features with emotion and sentiment features and selected the best-performed STL method for further comparisons. ii) We evaluate the Latent Hatred dataset D1 in an MTL approach with related tasks to check the performance in comparison with our STL baseline model. In order to reach the optimum approach, we consider three configurations. In all of them, the model trains and evaluates the corresponding dataset (D1) along with 1) SemEval 2017 dataset (D5) for the sentiment analysis task (MTL_sentiment) 2) GoEmotion dataset (D4) for the fine-grained emotion detection task (MTL_FG-emotions) 3) All mentioned datasets (D1, D4 and D5) combined tasks (MTL_all).
A 3-dimension sentiment vector containing binary sentiment information is generated for each sample in the implicit hate speech dataset. As shown in the results in Table  4 , the emotion features demonstrate good discrimination ability for implicit hate speech and improve the performance of the baseline text-only model. Using emotion and sentiment knowledge as additional features achieve improved classification performances. We observe that binary-polarity sentiment features also help the classification and improve the classifier's performance by a small margin (around 2% on average in macro F1 score) compared to text-only features as reported in  [5]. In contrast, finer-grained emotion features consistently outperform sentiment features and bring around 4% improvement in the F1 score, showing a better ability to capture the latent sentimental information in the text, which helps to identify implicit hate speech, and combining sentiment features and emotion features brings slight improvement over emotion features alone.
Fundamental thermal niches We de ned the fundamental thermal niche as a 1-dimensional Grinellian niche 38 with limits set by experimental estimates of species' upper and lower thermal tolerance limits. We retrieved thermal tolerance data from the Supplementary Material of Sunday et al. 2019  39  , which represents a curated subset of the Globtherm database  40  that contains an estimate of a critical or lethal upper or lower (or both) thermal tolerance limit per species. We subset data to include only subtidal marine, intertidal marine, and terrestrial ectothermic animal species (we excluded species from freshwater locations as we lack appropriate global temperature data for freshwater environments). We included terrestrial species with a freshwater larval stage so long as the thermal limit assays were performed on adult organisms.
Realized ranges and traits For species retained from Globtherm, we extracted geographic range maps in the form of polygons (extents of occurrence) from two sources: the International Union for the Conservation of Nature (IUCN) spatial data portal  42  (n = 318), and the Global Assessment of Reptile Distributions (GARD) data repository  43  (n = 51). When a range map was available from both sources, we used the IUCN range map in analyses. Additional range maps for species retained from Globtherm were inferred by tting convex hulls to carefully ltered occurrence records obtained from the Global Biodiversity Information Facility  44  (n = 225, see Supplementary Methods), following IUCN methods (iucnredlist. org/resources/mappingstandards). For each species, we then converted range map polygons to 1° × 1° resolution presence/absence grids and searched the literature for a suite of traits chosen in accordance with our hypotheses (table  S1 , see Supplementary Methods) to include as predictor variables in models.
Estimating body temperatures We used climate data to estimate the warmest and coolest extreme body temperatures each species would experience in each grid cell. For marine and intertidal species, we used coarse-grained macroclimatic data to characterise the span of daily body temperatures a species would experience across the globe. We calculated daily maximum and minimum air surface temperature climatologies at a 1° × 1° grid resolution over the period 1950-2000 from Berkeley Earth land datasets (berkeleyearth. org/data/; accessed on 14 October 2020). We obtained daily maximum and minimum mean sea surface temperature climatologies over the period 1982-2020 from the NOAA Optimum Interpolation (OI) Sea Surface Temperature V2 High Resolution Dataset (psl. noaa. gov/data/gridded/data. noaa. oisst.
v2. highres. html; accessed on 15 June 2021) and converted the higher resolution grid to a 1° × 1° resolution by aggregating cells, selecting the maximum or minimum value of aggregates. To account for the fact that intertidal species often experience both wet and dry temperatures, we considered both air and sea surface temperatures when de ning their experienced temperatures. Temperature estimates from coarse-grained macroclimatic data are not the actual temperatures terrestrial organisms experience  45. To better characterise the extreme body temperature that terrestrial organisms experience, we used species traits and microclimatic data from NicheMapR 46 to simulate species-speci c hourly operative temperatures in the sun and shade across a year in each grid cell (see Supplementary Methods).
Measuring thermal niche lling In environmental space, we calculated warm and cool lling of the potential thermal niche as the difference between the potential and realized warm and cool thermal niche extremes, respectively. This metric is in units of degrees Celsius. Negative values indicate a species is under lling its cool or warm potential thermal niche, whereas positive values indicate underprediction, and a value of zero indicates the species' perfectly lls its thermal niche. Some species did not have temperature data available across their realized range (n = 24), allowing us to measure niche lling in thermal space for 436 species. For species with available body temperature data and both warm and cool thermal limits (n = 186), we calculated both warm and cool thermal niche lling, while only one niche lling value was calculated for species with available body temperature data and only one thermal limit (warm niche lling only: n = 206; cool niche lling only: n = 44).
We then performed model averaging rather than selecting a single top model so as to avoid introducing uncertainty through the model selection process. We used the MuMIn package  50  to run all candidate models, which included all possible combinations of terms. We performed multi-model averaging with maximum likelihood estimation to identify the con dence set, or the models comprising the top 95% of model weight. We report full averages of coe cients. To ensure the assumption of normality was met, model residuals were visually inspected. To ensure the exclusion of certain explanatory variables from some models did not dramatically affect the estimation of other model parameters (one possible problem introduced by model averaging, ref  51  ), parameter estimates from models in the con dence set were visually compared to each other and to the model average (see g.
Acclimatisation While our main analysis assumes that a single upper and lower thermal tolerance de nes a species' fundamental thermal niche across its range, we used reduced datasets (warm niche lling: n = 212; cool niche lling: n = 134; range lling: n = 90) to simulate the potential plasticity of fundamental thermal limits to local seasonal temperatures. We compiled data on acclimation response ratios from published  52-55 (47-50)  databases and augmented these with individual studies with a new literature search (see Supplementary Methods). We used these to simulate the fundamental thermal niche limits of eld-acclimatised individuals based on recently-experienced temperatures in each grid cell (see Supplementary Methods, g. S9). We then inferred species' potential thermal niches by comparing their extreme body temperatures in a grid cell to their locally-acclimatised fundamental thermal niche limits.
Acknowledgments: We are grateful for contributions to geographic range validation by  Rafael Moreira Lima. Funding: This paper is a joint effort of the working group sWEEP supported by  sDiv , the  Synthesis Centre of iDiv  ( DFG FZT 118 ,  202548816 ). N. A. M. and J. M. S. were supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  and N. A. M. was also supported by the  Quebec Centre for Biodiversity Science  and the  Rubin Gruber   Science Undergraduate Research  Award. I. M-C. acknowledges funding from the  Spanish Ministry for Science and Innovation  (grant no. PID2019-109711RJ-I00  to I. M-C. ) as well as from  Comunidad de Madrid and University of Alcalá  (funders of  IFW  through grant  CM/BG/2021-003  to I. M-C. ). F. V. was supported by  CONACyT and INECOL, Mexico.
Data All data were retrospectively collected from patient medical records and pseud mized before transferring to the principal investigators. The following data wer lected: patient demographics, prior treatments, preoperative clinical presentation, d and type(s) of surgical interventions, length of hospital stay counting from the day o For the posterior approach, a lazy S-shaped incision is made before dissecting the popliteal fascia (Figure  1E ). Commonly, the involvement of the hamstring tendons coincides with tumor located in a Baker's cyst (Figure  1F, G ). After deeper dissection and retraction of the gastrocnemius muscle, posterior tumor in the subgastrocnemius recess appears (Figure  1H ). Additional necessary approaches can be made medial to the semimembranosus, between the semimembranosus and the popliteal vessel and tibial nerve, and between the popliteal vessels and peroneal nerve for tibial-fibular joint involvement.
Data All data were retrospectively collected from patient medical records and pseudonymized before transferring to the principal investigators. The following data were collected: patient demographics, prior treatments, preoperative clinical presentation, date(s) and type(s) of surgical interventions, length of hospital stay counting from the day of surgery till the day of discharge, postoperative range of motion up to one year, the need of walking aids, surgery-related complications, radiological progression, and subsequent treatments. For two-stage synovectomies, the length of hospital stay and surgical duration of the two separate surgeries were added together. In addition, radiological progression was measured from the date of the second intervention to the date of progression for two-stage synovectomies to avoid immortal time bias.
Statistical Analysis Continuous data were described by medians and ranges, and categorical data by the number of observations and percentages (%). Rates were calculated for the available data in individual categories. For all data, patients were stratified by undergoing a oneor two-stage synovectomy. Chi-square, Mann-Whitney U, or Kruskal-Wallis tests were performed to compare independent variables between the groups. Finally, we performed subgroup analyses comparing only open one-stage and twostage synovectomies. Due to the low incidence of TGCTs, no formal sample size calculation was performed, and all patients that fulfilled the inclusion criteria were included. IBM Statistical Package for Social Statistics 25 (Chicago, IL, USA) was used for analysis.
Rutile grains U-Pb analyses were performed at the Institute of Geological Sciences (University of Bern) using a RESOlution Laser System coupled to an Agilent 7900 quadrupole ICP-MS. A He-H2 gas mixture was used as the aerosol transport gas. Analyses were performed with a laser beam diameter of 40 μm with a 5 Hz frequency and energy densities on the sample 4. 0 J. cm -2. Sample analyses were calibrated using the primary rutile standard R10  [Luvizotto et al. , 2019]  and secondary rutile standard R632  [Axelsson et al. 2018 ] and NIST SRM 610 and 612  [Jochum et al. 2011]  were employed to monitor accuracy. Data reduction was performed using the software Iolite  [Hellstrom et al. 2008; Paton et al. 2011]. Individual ages for the analysis with neglegible common Pb, concordant in the TW plot, were calculated using IsoplotR  (Vermeesch, 2018).
Compositional mapping followed the procedure described in  [Lanari et al. , 2013]  using 15 KeV accelerating voltage, 100 nA beam current and dwell times of 200 ms. Point analyses were measured on the same area and were used as internal standards  [De Andrade et al. , 2006]. The compositional maps were classified and converted into concentration maps of oxide weight percentage using the software XMapTools 4 (build 220512). Local bulk compositions used in the thermodynamic modelling were approximated from combined oxide weight percentage maps using the export built-in function of XMapTools by integrating the pixel compositions of particular domains after a density correction  [Mészaros, et al. , 2016; Lanari and Engi, 2017].