dataesr/scientific-paragraphs-categorization huggingface dataset:
We thus can only assess the role of organized groups or channels, not individuals. This is a limitation that can not be overcome due to the nature of the data but should be accounted for when interpreting the findings. To check whether the National Security Law introduced in July 2020 has significantly affected the activities within the observed Telegram network and thus answer RQ5, we employed Interrupted Time Series Analysis  [51]  applied to the daily rates of edge formation in the observed network. We examined the period from March 2020 to the end of September 2020. The cut-off point for the start of the observation was chosen to isolate the effects of other major events such as the beginning of the COVID-19 pandemic or the protests in Hong Kong in 2019 on the timeseries. In total, the data comprised 214 points (122-pre-law; 92-afterlaw), each corresponding to one day.
Leaderless or not? The changes in the positions of channels by authority score and hub score over time are presented in Fig 8. In these figures, we include only the data on top-50 positions for each month (i. e. , if a channel dropped below top 50 in a given month, its data point for this month is omitted from the figures) to make the figures more illustrative of the dynamics around the top positions. Each line corresponds to one Telegram source (channel or public group). As evident from Fig  8 , there was high turnover in the positions of top sources by authority and hub scores. We suggest that this indicates the absence of specific leaders of the protest, at least in the form of organized groups or channels on Telegram. That being said, as we noted in the methodology section, the nature of the data does not allow testing for the presence of individual leader users who might have been behind different channels/groups who were switching in terms of the importance within the observed network.
We have also analyzed the prevalence of different topics in Telegram content before and after the enactment of the Security Law (Fig  10 ). Most topics follow similar distribution before and after the enactment of the law, with the exception of Discussion and Recon. The evidence suggests that the Telegram users engaged relatively less in discussion about current issues and protest strategy and more in reconnaissance after the introduction of the law. However, it is important to note that the absolute amount of both Discussion and Recon after the Security Law was smaller than the amount before the Security Law. Further analysis when more data points become available would be needed to substantiate the observation. After the enactment of the National Security Law, we also observe discussion about the security measures in response to the risk of arrests, including tips and measures to protect privacy, guidelines on using prepaid sim card, Google Voice, and VPN services to avoid tracking, ways to change Telegram setting to make the account not identifiable by phone number, and how to completely wipe chat record from cloud storage and mobile phone.
The analysis also reveals that the protests were de-facto leaderless, as previously claimed  [21] , with different channels switching in terms of the dominance in the network every month. We would like to reiterate, however, that the nature of the data allows only testing for the presence of sustained leadership (in the context of coordination and mobilization) constituted by group chats and/or public channels. We, thus, can not track the influence of individual users who might have administered specific channels, neither can we follow discussions in private groups. This is a major limitation that, nonetheless, can not be overcome due to the specifics of Telegram functionality, and should be accounted for both, when interpreting the results, and when planning Telegram-focused future studies.
The datasets collected and analysed during the current study are not publicly available due the sensitivity of the data (even though we have used only public Telegram data, we believe that the raw data if released publicly can be misused and potentially result in the repressions against the specific channels and/ or public groups who we identified as being important for the protest mobilization in Hong Kong in 2019). The anonymized versions of the datasets (channel/groups IDs and message identifies that can be rehydrated using Telegram API) are available via FORSBase: https://forsbase. unil. ch/project/study-public-overview/17548/0/. The access to it is managed solely by.
The primary aim of the "Megadata, Linked Data and Data Mining for Wastewater Networks" (MeDo) project (http://webmedo. msem. univ-montp2. fr/?page_id=223&lang=en), is to develop resources for text mining and information extraction in the wastewater domain. We developed a specific Natural Language Processing (NLP) pipeline named WEIR-P (WastewatEr InfoRmation extraction Platform) which allows users to retrieve relevant documents for a given network, process them to extract potentially new information, assess this information also by using an interactive visualization and add it to a pre-existing knowledge base. The system identifies the entities and relations to be extracted from texts, pertaining network information, wastewater treatment, accidents and works, organizations, spatio-temporal information, measures and water quality.
Funding This work was supported by a grant from the  PROCORE-France/Hong Kong Joint Research Scheme  sponsored by the  Research Grants Council  and the  Consulate General of France in Hong Kong  (Reference No. :  F-CityU107/15  and No. 35322RG ); and by a grant (Project No. 9360163 ) from the  Hong Kong Institute  for the Data Science at the  City University of Hong Kong.
'Zhangsan joined a meeting. 'The Chinese Treebank (CTB) is one of the most well-known Chinese constituent treebanks. Its guidelines are modeled after, and heavily influenced by, the Government and Binding theory introduced in (Chomsky 1981) , although the framework is not adopted wholesale (Xue et al. 2000). CTB started at the University of Pennsylvania, before moving to the University of Colorado at Boulder, and is now maintained at Brandeis University. The current version of the treebank contains over 2 million words comprised of data from newswire, broadcast material, web text, and transcribed speech (Xue et al. 2013). The treebank continues to use the guidelines for word segmentation, tagging, and bracketing that were last developed in 2000 during its development at the University of Pennsylvania.
Many scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. Tools such as deep neural networks regularly outperform humans with such large and high-dimensional datasets. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of machine-learning tools to formulate new theoretical hypotheses. Here, we propose a simple and versatile method to help characterize and understand the information used by a classifier to perform its task. The method is inspired by the reverse correlation framework familiar to neuroscientists.
Introduction Applications of machine-learning techniques permeate more and more scientific fields, with rapid and sometimes unexpected success  (LeCun et al. , 2015; Jordan & Mitschell, 2015; Krigeskorte & Douglas, 2018; Richards et al. , 2019). At the same time, it is becoming a widely-acknowledged issue that many of these tools are often used as black boxes, and need to be interpreted  (Molnar, 2020; Doshi-Velez & Kim, 2017). For instance, if a Deep Neural Network (DNN) was used to make lifechanging decisions such as deciding on an intervention based on medical imagery, both the clinicians and patients would have a clear desire to know the rationale that motivated the decision. Also, the power of classifiers to detect useful information in large datasets holds many promises to improve theoretical models, but then, understanding at least to some extent the classifier's operation is crucial  (Zihni et al.
, 2016)  and RISE  (Petsiuk et al. , 2018)  highlight features of specific examples which may or may not be representative of the classification task in general. Also, the method operates in the same representation space used as an input to the classifier, and can be applied to any type of representation (2D images, 1D time series such as audio, higher-dimensional brain imaging data, for instance). The outline of the method is as follows. First, a set of stochastic inputs are generated, by introducing noise on the training dataset when available, or, when unavailable, by generating broadband noise to cover systematically the input space. The noise takes two forms: additive noise, as is classically the case, but also multiplicative low-pass noise known as "bubbles"  (Gosselin & Shyns, 2001 , 2002)  to focus the exploration on sub-spaces of the input representation.
A textual description is provided as well as a software repository written in Python programming language (https://github. com/EtienneTho/proise) and a schematic illustration (Figure  1 ). For each pass (gray box in Fig. 1 ): 1. A bubble mask is generated. This consists of a mask in the input space, of dimension N, consisting of randomly positioned N-dimensional Gaussian windows (see Figure  2 ). The number of bubbles, nbBubbles, as well as the size of the bubbles in terms of the Gaussian standard deviations can be arbitrarily chosen and are parameters of the algorithm. In practice, an input array of dimension N populated by zeroes except for nbBubbles unit values is convolved with N-dimensional Gaussian windows. The resulting mask is denoted BubbleMask. 2. The probing data is generated. For variant a), the probing data is one exemplar of the training dataset, randomly chosen.
For variant b), the probing data is an N-dimensional activation noise (see section 2. 1. 2 for details). The probing data is denoted ProbingData. 3. The probing sample is obtained by multiplying the bubble mask with the probing noise: ProbingSample = BubbleMask * ProbingData. 4. The probing sample is fed to the classifier and the output class is recorded. The probing sample is labeled C+ if it classified in the target class, C-otherwise.
Procedure To build the canonical map, the whole input space is randomly perturbed, without bubbles, so the search is not focused on any subspace. The aim is to probe the whole feature space. Then, all probes classified as members of the same category are averaged, in a direct adaptation of the classic reverse correlation method. However, we introduce here two differences, though. First, for generality, we do not separate correct classifications from false positives or false negatives. This would require to know the training dataset or to have a large labeled testing dataset. Second, a normalization of the feature map is introduced, using standard deviations estimates at each point of the map. This facultative step serves to display units similar to z-scores and not arbitrary input values. Again, we propose two sub-variants of the algorithm depending on the availability or not of the training dataset: a) broadband noise in the input space is added to the training set; b) broadband noise is generated in the input space.
We now describe the algorithm, jointly for a) and b). A textual description is provided as well as the scripts (https://github. com/EtienneTho/proise) and a schematic description (Figure  1 ). For each pass: 1. The probing sample is generated. For a), the probing sample is one randomly chosen exemplar of the training dataset, with noise added. The goal is to perturb the input to introduce variability, so that only the most salient information (to the classifier) remains in the reverse correlation average. For b), the probing sample is an N-dimensional activation noise. The probing sample is denoted ProbingSample. 2. The probing sample is fed to the classifier and the output class is recorded. The probing sample is labeled C+ if it classified in the target class, C-otherwise.
Digits classification In this first example, we classify visual samples of handwritten digits from the MNIST database  (Deng, 2012). This is a standard database for evaluating image classification algorithms in the machine-learning community. It is composed of handwritten digits, from 0 to 9, with 60000 samples in the training set and 10000 samples in the test set. Each sample is a two-dimensional greyscale image with pixels values between 0 and 1. Many algorithms can now successfully perform this classification task. Here we trained a Convolutional Neural Network (CNN) to discriminate between digits, with the following architecture: 2D-convolutional layer (3, 3), Max Polling layer (2, 2), 2D-convolution layer (3, 3), flattening layer, dense layer with 10 outputs and a softmax activation. Three epochs were run and, as expected, a high classification accuracy of 97% was obtained on the test set.
The availability of training data is expected to provide faster and more robust convergence towards the features of interest. For each case, 60000 probing samples and 10 bubbles with standard deviation of 4 pixels were used. In the case of Method 1b), a uniform random noise was first tested but only lead to categorization in 5 digits categories, so a pseudo random noise obtained from the inversion of a PCA was instead used generated to probe the CNN. This new noise led to decisions covering the 10 categories. The discriminative information obtained in the two cases correlate strongly (r =. 92 (SD =. 01), df = 783, p < 10 -3 ), showing that the methods' sub-variants with or without the training dataset converge toward the same masks. Figure  4  shows the canonical features obtained with Methods 2a) and 2b), with or without th training set available.
Speech vs. music In this second example, we classified audio samples in a speech versus music task. We used the GTZAN database composed of 132 excerpts of speech and music  (Tzanetakis & Cook, 2002). The database was preprocessed to create samples with a fixed duration of 5 seconds, leading to a datase of 768 samples. Those samples were randomly separated into a training set (691 excerpts) and a tes set (77 excerpts, 10% of the dataset). Following  Patil et al. (2012) , who performed an automatic classification of the musical timbre o short audio samples, sounds were first processed by an auditory model  (Chi et al. , 2005). The idea i to cast the input space into a representation that is interpretable in terms of auditory processing, unlike the raw waveform representation. Briefly, a filterbank corresponding to cochlear tonotopy is e input sound  (Chi et al.
Summary The method presented in this paper used a reverse correlation framework to visualize the input features discovered by an automatic classifier to reach its decisions. When the classifier is successful, such features should provide insights about the structure of the input dataset. Over two examples using different kinds of classifiers (a CNN and an SVM with RBF) and using different kinds of input representations (2-D visual images and 1-D audio samples converted to a 4-D auditory model), we illustrated how the method could highlight relevant aspects of a classifier's operation. Moreover, by combining standard noise perturbation techniques with so-called bubbles  (Gosselin & Shyns, 2002) , we showed that the probing method can be focused either on discriminative features, related to the decision strategy of the classifier, or on canonical features, related to the output classes' main characteristics.
, 2016). Applying it to interpret. CC-BY-ND 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted June 23, 2020. ; https://doi. org/10. 1101/2020. 06. 22. 165688 doi: bioRxiv preprint classifiers amounts to translating and applying a familiar toolbox to another, conceptually similar problem of characterizing a black-box system. Second, the method is by design fully agnostic by design. It operates on the input space of the classifier, whatever this space might be. It does not make assumptions on the classifier's architecture or inner operations. Focusing on the input space rather than the classifier's architecture is especially desirable in situations where the classifier is not the main interest of study, but rather, the structure of the input dataset is.
Third, it can be applied to classifiers that have not been designed by the user, as it does not even require the availability of the training dataset. Access to labeled input data is helpful in improving the efficiency of the method, for instance by allowing to shape the perturbation noise, but this is a mild constraint: there are no interesting situations we can think of for which both the classifier and the type of data to classify would be unknown. Finally, the output of the method is a visualization (with statistical evaluation if required) in the input space. Such a representation should make intuitive sense to the user of the method, and the features discovered can be interpreted a posteriori in terms of attributes of the stimuli. If the representation does not make intuitive sense, then one possible benefit of the method is to help recast the input space into a more meaningful representation, as was done here in the audio example for which the waveform samples were pre-processed with an auditory model.
One suggested heuristic was to try and cover the output classes in a balanced manner with the probing set. However, even though statistical tests of the resulting features are available, we do not provide any fitness criterion, i. e. a way to quantify the efficiency of the method for a given set of parameters, for the features obtained with the method. Rather, we would argue that the iterative process for parameter tuning can be part of the interpretation process since finding the right probing structure provides some information on the structure of the dataset. Also, assessing whether the discovered features make intuitive sense relies mostly on the knowledge and goals of the user. Thus, it may not be easily formalized into a fitness criterion. If more formally defined methods are needed, either from the outset or after a first exploration of the classifier with reverse correlation, other classifier-specific tools exist (e.
In return, the expert's intuition may also help improve the classifier, for instance by simplifying its input representation through pre-processing, and so hopefully making it less brittle to irrelevant variations in input that may have been picked up by overfitting during training  (Goodfellow et al. , 2015). The discriminative features could be particularly useful to reduce the complexity of a classifier. Based on the discriminative features map, it may be possible to select a subset of important and intelligible features, which can then be used to build a more computationally efficient classifier, for very large dataset and/or for real-time processing.
Acknowledgements: Research supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and the Excellence Initiative of Aix-Marseille University (A*MIDEX) (ET), ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL (DP). TA was supported by the Human Frontier Science Program (LT000362/2018-L).
Corpus and methodology The corpus, which is managed on the HUMANUM platform, is composed of a survey submitted to 2 class groups of 13 year old pupils (22 and 14 pupils in june 2019). Since there were only 36 pupils in this study, we consider this an exploratory research for hypotheses building. Other data collected consist of skills assessments, data about class atmosphere as conveyed by school reports, teacher preparation sheets for the different scenarios (2018-2019), recorded interviews with the teachers and the pupils, and in-world and classroom scenario ethnographic observations. Several scenarios were implemented on the two classes observed. Some of them lasted one hour and were repeated several times (Mathematics) and three scenarios lasted several months during the school year. Of these three scenarios, the first was interdisciplinary and involved the French teacher, the English teacher and the mathematics teacher around the pedagogical project of the "seven wonders of the ancient world".
We might ask in which way this is related to specific configurations or whether it is a general tendency, and if so, why. This is the reason for the choice we have made for the 20-21 data collection, focusing on pupils with academic difficulties, in order to document these tendencies further. We wish to further investigate the link between bodily aspects in the Virtual World and the place of the body for pupils who are struggling from an academic point of view, under the double domination of French school elitism and social structures that endow in a very unevenly way children with the predisposition for learning at school thank to the family invisible pedagogy  (Bernstein, 1975; Beaudelot and Establet, 2009) , We are currently questioning to what extent school form can be deconstructed in the Virtual World in order to open up opportunities for pupils who struggle under academic norms and power struggles.
Acknowledgements It is a pleasure to thank the  Institut Henri Poincaré (Sorbonne University  and  CNRS, Paris) , whose Fall Trimester 2019 on "The  Mathematics of Climate and the Environment " gave the authors the possibility to advance substantially the work on this paper. Chris Jones kindly pointed out to us two recent papers relevant to R-tipping in excitable systems 32,33. The authors wish to thank two anonymous reviewers, whose constructive comments helped improve the manuscript. The present paper is TiPES contribution #  48 ; the  TiPES (Tipping Points in the Earth System ) project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 820970. Work on this paper has also been supported by the  EIT.
Phones are elementary sounds the speech is made of, on which syllables and words are built. The transcription of texts from their orthographic form into a phonetic alphabet is an important requirement in various applications related to speech and language processing, for instance for text to speech systems. Phonemizer is a Python package addressing precisely this issue: it transcribes a text from its orthographic representation into a phonetic one. The package is user-friendly and exposes a single high-level phonemize function, a lower lovel API, and is also available as a command-line interface. It supports about a hundred different languages and provides end-user functionalities such as punctuation preservation, phones accentuation, tokenization at phone/syllable/word levels, as well as parallel processing of large input texts.
Statement of Need Whereas the high-level features introduced above are implemented directly by phonemiz er, the phonetic transcription itself is delegated to third party backends, wrapped in an homogeneous interface by the package. The default backend used by phonemizer is eSpeak  (Dunn & Vitolins, 2019) , a text to speech software built on linguistic expertise and hand written transcription rules. It transcribes text into the International Phonetic Alphabet and supports more than a hundred languages. Using MBROLA voices  (Tits & Vitolins, 2019) , available for about 35 languages, the eSpeak backend transcribes text in the SAMPA computer readable phonetic alphabet. Festival  (Black et al. , 2014)  is another text to speech software used as a backend for phonemizer. It is available for American English only, and uses a non standard phoneset for transcription, but this backend is the only one to meet the requirement of some applications by preserving syllable boundaries of transcribed texts.