dataesr/scientific-paragraphs-categorization huggingface dataset:
For the two periods 2007-2014 and 2018-2022, we sorted the GNSS stations by the total number of missing data points and we chose the 135 stations affected by fewer data gaps as the final subset for our study. We make sure that stations having too high a noise do not appear in this subset. We selected 135 stations since it represents a good compromise between the presence of data and the longest data-gap sequence in a 60-day window. However, we also train and test SSEdetector on 352 stations (the same number used in the study by Michel et al. 12  ) to compare with a setting equivalent to the one from Michel et al. 12. We briefly discuss the results in the section "Sensitivity study". SSEgenerator: generation of realistic noise time series. Raw GNSS data is first detrended at each of the 135 stations, i.
We choose γ = 0. 01. By rewriting the duration as T = t maxt min and solving for β, we obtain: β ¼ 2 T ln 1 γ À 1 : ð6Þ Finally, we generate slow slip events having uniform duration T between 10 and 30 days. We take half of the noise samples (30,000) and we create a positive sample (i. e. , time series containing a slow slip event) as X R + d(t), where d(t) is a matrix containing all the modeled time series d s (t) for each station. We let X R contain missing data. Therefore, we do not add the signal d s (t) where data should not be present. SSEdetector: detailed architecture. SSEdetector is a deep neural network obtained by the combination of a convolutional and a Transformer neural network. The full architecture is shown in Supplementary Fig. 13. The model takes input GNSS time series, which can be grouped as a matrix of shape (N s , N t , N c ), where N s , N t , N c are the number of stations, window length and number of components, respectively.
We implemented the code of SSEdetector in Python using the Tensorflow and Keras libraries  45, 46. We run the training on NVIDIA Tesla A100 Graphics Processing Units (GPUs). The training of SSEdetector takes less than 2 h. The inference on the whole 15-year sequence (2007-2022) takes a few seconds. Calculation of tremor durations. We compute the durations of tremor bursts using the notion of topographic prominence, explained in the following. We rely on the software implementation from the SciPy Python library  47. Given a peak in the curve, the topographic prominence is informally defined as the minimum elevation that needs to be descended to start reaching a higher peak. The procedure is graphically detailed in Supplementary Fig. 15. We first search for peaks in the number of tremors per day by comparison with neighboring values.
, 2022)  plugins in Fiji allow including this additional knowledge in the segmentation. We applied TWS to train a classifier to segment 2D BSE images and SRμCT slices. Using the same classifier for datasets obtained using different methods means that the segmentation is based on the same ground truth and the porosity results can be compared with each other. Labkit is especially designed for big image data, and therefore we used it to train classifiers to segment the HR and LR 3D data. A description of the use of the plugins is given below in section 2. 3. 1. We used a threshold value of 100 to segment the BSE mosaic of the entire polished section. We estimate that our porosity determination using thresholding likely results in an error of ±1. 0 area% since lowering/rising the threshold value by one results in approximately ±0.
Quantification of porosity properties Void diameter can be determined as Feret diameter, which measures the longest line segment that fits the void space, or as equivalent diameter, which demonstrates the largest inscribed sphere that fits within the void space  (Dubetz, 2016). Although Feret diameter is computationally more expensive compared to the equivalent diameter, it is more accurate and capable of handling irregular shapes  (Dubetz, 2016)  and thus the chosen method in this study. We used Dragonfly software version 2022. 2 (Object Research Systems (ORS) Inc. , Montreal, Canada, 2020) to quantify the porosity properties. Before quantification, the region of interest was refined by removing pixels/voxels by count three to avoid quantization of the results and to accurately analyze a discrete void space  (Hanna and Ketcham, 2017; Ketcham and Hildebrandt, 2014).
Pore network modeling in Dragonfly allows the investigation of the inter-relationship between connected components by employing the University of Waterloo's OpenPNM package  (Gostick et al. , 2016). OpenPNM treats a porous medium as a volume-averaged continuum and creates a model of the void space with spheres and lines representing the voids and their connections  (Gostick et al. , 2016). We modeled the void space of the entire sample (LR SRμCT) and Volumes 1-3 (HR SRμCT) and computed the number of connections the voids have with other void spaces from the created connectivity graphs.
Fig. 4  shows the area (A) and maximum Feret diameter distribution (B) of the entire polished section. Note the logarithmic scale of relative frequency, which allows the visibility of the large values in the range that have a very low frequency. Voids with diameters ≤26 μm represent 98. 4% of the void space and void areas ≤2800 μm 2 represent 99. 9% of the void space in the section. Fig. 5  shows the distribution of aspect ratios of voids (A) and the relationship between void diameter and aspect ratio of voids (B) in the polished section. The plot B in Fig. 5  is calculated from resampled data. We reduced the dataset using evenly spaced maximum Feret values and mean of the aspect ratio to visualize the ~156,000 datapoints. The computed Pearson's r in Plot B in Fig. 5  describes the value of the Pearson correlation coefficient, which is a measure of the linear trend.
3% of the voids. Fig. 9  shows the aspect ratio distribution of Volumes 1-3. The preferred aspect ratios in all the volumes are between 2 and 3 and their relative frequencies are 50. 9%, 43. 3%, 44. 0%, and 42. 2% in the bulk sample, Volume 1, Volume 2, and Volume 3, respectively. Fig. 10  illustrates the connection between maximum Feret diameter and aspect ratio of voids in Volumes 1-3 and shows weakly or moderately positive correlations between void diameter and aspect ratio. The plots in Fig. 10  are calculated from resampled data. The datasets contain ~120,000-145,000 data points and thus we reduced the data into smaller size with evenly-spaced maximum Feret values. The sampling interval is 0. 8 μm and the sampling method is mean of the aspect ratio. Fig. 11  shows the connectivity histogram and Fig.
Segmentation methods for determining porosity We evaluated several different segmentation methods including automated algorithms in Fiji, such as Otsu, Huang, Yen, and MaxEntropy, which are widely accepted in different studies. All these algorithms overestimated the porosity and therefore were not suitable for our data and needs. Also, finding one single manually selected threshold value for both the SRμCT and BSE images was problematic, and no single value proved to be applicable in all the images. The peak width for the pore spaces of the SRμCT image was frequently larger than that of the BSE image, which results from the poorer contrast resolution of the SRμCT image  (Tsuchiyama et al. , 2005). A manually selected threshold value was applicable only to the BSE mosaic. Our search for a suitable segmenting method led to TWS, which we applied to train an algorithm using SRμCT slices to automatically segment the SRμCT and SEM data of Areas 1-3.
Thus, TWS allowed segmenting data of similar pixel size regardless of the imaging technique, and because the characteristics of Bjurböle are extreme among chondrites, this method applies to chondrites in general. Since computing feature vectors and random forest predictions from the feature vectors requires substantial computational resources, we turned to Labkit to segment the 3D datasets. Labkit is optimized for big image data and can process large datasets of several gigabytes  (Arzt et al. , 2022).
Fig. 14  shows a porosity model considering anisotropy of Volume 3 in Bjurböle. A weakly positive correlation between aspect ratio of voids and void diameter is present in SRμCT slices of Areas 1-3, BSE image of Area 3, and in the polished section. In Volumes 1-3 and the bulk sample, the linear relationship between aspect ratio of voids and void diameter is moderately positive. The positive correlation is stronger in the 3D results, which is likely due to the stereological bias associated to SEM. Thus, larger voids tend to have higher aspect ratios. Note that the plots of the relationship between aspect ratio and void diameter of the 2D polished section and the 3D results are calculated from resampled data, where evenly spaced diameter values and mean of aspect ratio are used to visualize the large datasets with >100,000 data points.
Conclusions We applied SRμCT and SEM at sub-micron resolution to characterize the porosity of L/LL4 ordinary chondrite Bjurböle in 2D and 3D. Trainable Weka Segmentation is a convenient tool to automatically segment data of comparable pixel size obtained using SRμCT and SEM. Labkit is efficient in automated segmentation of large SRμCT datasets. These tools combine visual qualitative examination and machine learning algorithms that can be applied to both SRμCT and SEM data to obtain satisfactory segmentation results for the porosity analyses. At similar resolution, SRμCT and SEM produce comparable 2D results. Although higher resolution can be reached with SEM, it causes stereological bias. When comparing the 2D and 3D results, the effect of this bias was especially obvious in the results of the aspect ratios of voids.
3D SRμCT eliminates this concern and should produce an estimation that is closer to the truth. The comparison between SRμCT and SEM led to the conclusion that they are powerful as standalone tools as well as supplementary tools to add to other studies allowing more complete and detailed investigations of porosity. Because the characteristics of Bjurböle are exceptional among chondrites, these means can be applied to chondrites in general to achieve credible results of their porosity properties. The Bjurböle sample in this study is a complex, highly porous, and friable medium. Imaging reveals the heterogeneous distribution of Fig. 14. A 3D model of anisotropy of porosity in Volume 3 of Bjurböle constructed using Bone Analysis module in Dragonfly software. The box in light blue colour shows the outlines of the total volume.
Acknowledgments We thank  Dominik Hezel  and an anonymous reviewer for critically reviewing the manuscript and proposing valuable improvements. We acknowledge the  European Synchrotron Radiation Facility  for the provision of synchrotron radiation facilities, and we would like to thank  Ludovic Broche  and  Alexander Rack  for their assistance in using beamline ID19. We would also like to thank  Katie Doig  and  Adam Abersteiner  for their help in preparing the polished section and for constructive discussions and assistance on the SEM. This is publication #0009 of the Geophysical, environmental, and mineralogical  laboratories (HelLabs)  of the  University of Helsinki.
Data availability SRμCT datasets related to this article can be found at doi. org/10. 15151/ESRF-DC-1163775534  (Soini et al. 2023) , an online data repository hosted at ESRF. BSE images and elemental maps that support the findings of this work are openly available at https://doi. org/10. 5281/zenodo. 6504816  (Soini et al. 2022) , an online data repository hosted at Zenodo.
This paper describes our approach to Subtask 1 "News Genre Categorization" of SemEval-2023 Task 3 "Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", which aims to determine whether a given news article is an opinion piece, an objective report, or satirical. We fine-tuned the domain-specific language model POLITICS, which was pre-trained on a large-scale dataset of more than 3. 6M English political news articles following ideologydriven pre-training objectives. In order to use it in the multilingual setup of the task, we added as a pre-processing step the translation of all documents into English. Our system ranked among the top systems overall in most language, and ranked 1st on the English dataset.
Introduction The News Genre Categorization subtask of SemEval-2023 Task 3  (Piskorski et al. , 2023 ) is a classification task that aims to identify the genre of a news article by considering three classes: opinion, reporting, satire. News genre categorization is important because it helps readers to quickly identify the type of content they are reading. It also helps to ensure that readers are not misled by the content they are consuming. The organizers provided articles in six languages for the training phase (English, French, German, Italian, Polish, and Russian) and three surprise languages were revealed for the evaluation on the test sets (Spanish, Greek and Georgian). It is therefore a multilingual task that includes small datasets during training and zero-shot classification for surprise languages.
, 2020)  or longsequence transformers  (Beltagy et al. , 2020). We ranked 1st on the English test set with a Macro-F 1 score of 78. 43 (+16. 8 points above the second ranked system), but were less successful on the other languages with an average of 6th place (out of about 20 systems, depending on languages). We draw several observations from our participation in this task: -the dominance of large-scale pre-trained and domain-specific language models over more adapted but not massively pre-trained approaches; -the need to further develop multilingual language models and datasets rather than having to rely on low performing translation systems for complex tasks; -the importance of considering the whole text in the case of long documents, whereas most language models are limited to the first 512 subtokens.
Datasets The input data for this task are news articles in plain text format, with their title being on the first line. During the first stage, a training split and a development split were provided for each of the 6 known languages: English (en), French (fr), German (ge), Italian (it), Polish (po), and Russian (ru). The annotations were given only for the training sets at first, then also for the development sets when the test sets were released. For the evaluation on the test sets, 3 surprise languages were revealed which involves zero-shot classification (no train or dev sets): Spanish (es), Greek (gr) and Georgian (ka). Given the limited number of news articles available per language, this is a kind of few-shot learning task. This problem is also characterized by a strong class imbalance, especially for the satire genre which is represented only a few dozen times across the datasets.
Related Work Substantial efforts have been made to address the problem of media analysis on a multitude of aspects such as bias analysis  (Hamborg et al. , 2019) , summarization  (Eyal et al. , 2019) , text categorization  (Pérez-Rosas et al. , 2018; Karimi and Tang, 2019)  or propaganda techniques  (Da San Martino et al. , 2020). Recently,  Liu et al. (2022)  proposed POLI-TICS, a pre-trained language model over RoBERTa  (Liu et al. , 2019) , fine-tuned on a large corpus of English news to address both political ideology prediction and stance detection. Their approach has proven to be the most efficient on a wide range of well known datasets for these tasks such as Hyperpartisan  (Kiesel et al. , 2019) , Allsides 1   (Baly et al. , 2020)  or BASIL  (Fan et al. , 2019).
System Overview Our main strategy is based on Liu et al. (  2022 )'s domain-specific pre-trained language model POLI-TICS which has recently demonstrated great success on a variety of news articles ideology and stance prediction datasets. It has several advantages for this task: (i) it was massively pre-trained on more than 3. 6M English news articles, (ii) it relies on the comparison of articles on the same story written by media of different ideologies, (iii) it demonstrated its robustness in few-shot learning scenarios. Although predicting political ideology is not the same as detecting the genre of an article, our assumption is that these two notions overlap as in both cases there is a linguistic shift in the way information is conveyed. We can also observe similarities between the genre reporting and articles from the Center political class, or between the genre opinion and Left or Right leaning articles.
Data Translation POLITICS was trained solely on articles from English-language media, whereas in the multilingual configuration proposed for this task, we have to classify articles in 9 languages, 3 of which were unknown during training. Because of this strong constraint and the impossibility of re-training POL-ITICS in a multilingual configuration, we decided to resort to translation into English. By translating all texts into English, we end up with an augmented English training set that can be used with POLI-TICS. This solution represents an additional cost due to the pre-processing step, and a loss of information that depends on the quality of the translation system. Several translation models were compared based on performance, language coverage, and accessibility, including GoogleTranslate, 2  DeepL 3  and OPUS-MT  (Tiedemann and Thottingal, 2020) , resulting in choosing GoogleTranslate as the most appropriate one.
Fine-tuning For the classification model, we fine-tuned POLI-TICS on the English dataset augmented with translations. most language models, is limited with respect to the size of the input text, here at most 512 subtokens. News articles are on average much longer (Figure  1 ), thus truncating the first 512 subtokens would result in a significant loss of information. Rather than truncating the text, we use a sliding window of size 512 with an overlap of size 64, and aggregated the information by mean pooling. That is, we encode the first 512 tokens of the document, then the next 512 with an overlap of 64, and so on, until we reach the end of the document, then we calculate the mean of all these representations to obtain the final representation of the document.
Alternative Approaches Other approaches of interest for this task were also considered, but proved to be less successful during the training phase on the development split. In particular, on English, POLITICS showed much higher performance than the other models considered and was on average better on the other languages. Thus, we have favored this model for the evaluation although a posteriori we can see that it is less efficient in certain cases (e. g. on surprise languages or on French/Russian, Table  3 ). (Conneau et al. , 2020)  The multilingual version of RoBERTa  (Liu et al. , 2019) , a state-of-the-art model in natural language processing, pre-trained on 2. 5TB of CommonCrawl data containing 100 languages and including the 9 ones covered by this task. Similar to POLITICS, we used a sliding window to consider the entire Table  3 : Macro-F 1 scores on the test sets for each language and different approaches.
* indicates surprise languages (zero-shot). "512" means that only the first 512 subtokens of the inputs (no sliding window) were used to train the model. All results, except for the main strategy, were obtained when the submission platform reopened after the official submission deadline. We added the rank of the main system with respect to that score, according to the leaderboard published by the organisers. Note that the baseline and XLM-RoBERTa are the only models that have been trained on the original data (not translated).
XLM-RoBERTa article. Longformer  (Beltagy et al. , 2020)  Transformerbased model designed to process longer sequences, up to 4096 tokens, making it suitable for news article classification, and pre-trained on a variety of large-scale generic datasets, including Wikipedia, BooksCorpus, and CommonCrawl. No sliding window was introduced with this model, as the input length covers the vast majority of texts, cf.
Experimental Setup First, the train, dev and test split articles for each language are translated using Google Translate. Next, we aggregate the train and dev splits for each language to create an augmented English dataset containing 1234 articles in the training split and 358 in the development split, on which we train a single English model. We built on  Liu et al. (2022) 's implementation, 4 and the HuggingFace transformers library  (Wolf et al. , 2020). Hyperparameters were set using grid search on the augmented development split for each of the models considered (Table  2 ). The hyperparameters for POLITICS and XLM-RoBERTa were found to be the same. We also conducted a control experiment on the sliding window to assess its effectiveness by evaluating the same model trained only on the first 512 tokens of the articles.
Control experiments and alternative approaches (see table  3  4 https://github. com/launchnlp/POLITICS were performed on the same set of hyperparameters. Since there is a strong class imbalance in the training set, we weighted the cross entropy on the class distribution, which turned out to give us better performance. Regarding alternative approaches, we fine-tuned xlm-roberta-large 5  using a sliding window on the same set of hyperparameters as POLI-TICS (Table  2 ). Longformer was trained using longformer-base-4096 6  and the hyperparameters given in Table  2. The official evaluation measure used is Macro-F 1. Micro-F 1 is also given as a secondary evaluation measure on the official leaderboard.
Regarding the alternative approaches, the results obtained by Longformer confirm the previous observations on the importance of considering the whole article, with results close to or even better than POLITICS outside English and without any in-domain pre-training. Furthermore, the XLM-RoBERTa multilingual model performs well against POLITICS without the need for translation, but the lack of in-domain pre-training results in a significant performance drop, especially for English. These results should be taken with a grain of salt for the following reasons: test sets are small (30-70 instances) hence the huge variance between models and across languages. This problem is compounded by the chosen evaluation : Macro-F 1 scores equalize the contributions of all classes to the final scores, meaning that one instance from the minority class classified one way or the other could swing the evaluation disproportionately.
Acknowledgements Nicolas Devatine 's work is supported by the  SLANT  project ( ANR-19-CE23-0022 ). This work was partially supported by the  ANR  ( ANR-19-PI3A-0004 ) through the  AI Interdisciplinary Institute ,  ANITI , as a part of  France's "Investing for the Future -PIA3" program. This work is also partially supported by the  AnDiaMO  project ( ANR-21-CE23-0020 ). Chloé Braud  and  Philippe Muller  are part of the programme DesCartes and are also supported by the  National Research Foundation, Prime Minister's Office, Singapore  under its  Campus for Research Excellence and Technological Enterprise (CREATE)   programme.
Ekström et al. 2012). As this paradigm is constructed for one dimension, several assumptions have been made regarding the relative strengths of the horizontal and vertical diffusion, and the validity domain of these assumptions is currently unknown, except that they should apply in the limit of slow rotation. Attempts at solving the stellar structure of rotating stars in more than one dimension began in the sixties, but it only recently became possible to compute the stellar structure in a self-consistent manner in two dimensions, that is, with the ESTER 1 code  (Espinosa Lara & Rieutord 2013; Rieutord et al. 2016 ). These models have been restricted to a steady state, where the hydrogen mass fraction profile is modelled as a step function, namely a constant value in the convective core and a constant value in the radiative envelope (see e.