dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , we represent inputs as a set of 12 scores whereas FlauBERT uses 1024-dimensional opaque vectors. We have also built and released the lexicon containing the 10K most frequent French nouns of the frWaC and their corresponding embeddings. We hope that this resource can be complementary to existing embeddings and lexical semantic resources. The lexicon, along with the seed lists, predictions and evaluation data are freely available. 13 We have applied our method to nouns only, and our embeddings are 6-dimensional (one per coarse supersense), certainly lacking expressive power to cover the full range of semantic distinctions. In theory, there is nothing that prevents us from increasing the number of dimensions (e. g. , to cover the traditional Wordnet supersenses), and to experiment with other parts of speech (e.
Significant peaks are found in the 250 -500 µHz frequency range, where the pulsations were indeed expected, while nothing above a 4-σ detection threshold emerges elsewhere in the spectrum. We extracted the periodic modulations in Feige 46 by following a similar approach to  Latour et al. (2019b) , using a standard Fourier analysis and prewhitening techniques (see, e. g. Billéres et al. 2000). This was accomplished efficiently with our dedicated time-series analysis software, FELIX  (Charpinet et al. 2010; Zong et al. 2016). The entire pre-whitening procedure is illustrated in Fig. 2 , and the extracted mode parameters are listed in Table  1. Most peaks, except the largest amplitude one, are easily reproduced by fitting a pure sinusoidal component to the time series, leaving no residual behind in Fourier space.
Spectroscopic observations We obtained four VLT/UVES spectra of Feige 46 in February 2020 with a total exposure time of 5920 s (ID 0104. D-0206(A)). These spectra have a resolution of R ≈ 41000 and cover the spectral range from 3305 Å to 6645 Å, with gaps at 4525 -4620 Å and 5599 -5678 Å. The individual spectra were stacked after   3. Comparison of smoothed final synthetic spectrum of LS IV-14 • 116 (grey line) with photometric data. The two black data points labelled 'box' are binned fluxes from an IUE spectrum (LWP10814LL, magenta line,  Wamsteker et al. 2000). Filter-averaged fluxes are shown as coloured data points that were converted from observed magnitudes (the dashed horizontal lines indicate filter widths). The residual panels at the bottom and on the right sides, respectively, show the differences between synthetic and observed magnitudes/colours.
Acknowledgements. We thank  Simon Kreuzer  for the development of the the photometry query tool and  Ingrid Pelisoli  for very helpful comments on TESS. We thank the referee,  Suzanna Randall , for useful suggestions that improved this paper. M. L. acknowledges funding from the  Deutsche Forschungsgemeinschaft  (grant  DR 281/35-1 ). S. C. acknowledges financial support from the  Centre National d'Études Spatiales  ( CNES, France ) and from the  Agence Nationale de la Recherche  ( ANR, France)  under grant  ANR-17-CE31-0018. Based on observations collected at the  European Southern Observatory  under  ESO  programmes  0104. D-0206 (A),  087. D-0950 (A), and  095. D-0733 (A). This paper includes data collected by the TESS mission, which are publicly available from the  Mikulski Archive for Space Telescopes (MAST).
Materials and Methods Subjects include one male (27 years old) and one female (25 years old). Both subjects are native speakers of French with no speech or hearing problems. Subjects were asked to repeat /tata/ (with the last syllable stressed) periodically with the period of 2 s. The choice of the target utterance is also motivated by the fact that variability of the articulation for the sound /t/ is very small  [38]. In case of this syllable, the tongue contacts the hard palate in the alveolar ridge region and then goes down, so that its trajectory corresponds to back and forth motion. The data were acquired on a Siemens Prisma 3T scanner (Siemens, Erlangen, Germany) installed in Nancy Central Regional University Hospital under the approved ethics protocol "METHODO" (ClinicalTrials. gov Identifier: NCT02887053).
Audio was recorded simultaneously with imaging at a sampling frequency of 16 kHz by using an optoacoustics fibre-optic microphone (FOMRI III, Optoacoustics Ltd. , Mazor, Israel) placed in the scanner. To remove acoustic noise of the MRI machine, we used the denoising algorithm proposed in  [41]. In order to obtain clear samples of the speech, the subject was asked to repeat the target utterance 2-3 times before and after the imaging sequence was played. Further sound processing, such as calculation of the sound level and the spectrogram, was done in MATLAB using standard functions. In order to compare velocity curves with the sound, it was necessary to define specific points. The natural choice was: onset of the first vowel (q 1 ), offset of the first vowel (q 2 ), onset of the second vowel (q 3 ) and offset of the second vowel (q 4 ) since they are clearly visible from both the acoustic signal and the spectrogram.
We applied it to record timestamps of the sequence endings and of the beep and flash generator signal. The latter was also recorded into the TTL channel of the opto-acoustic system. The first step of the alignment was the detection of the sequence noise endings in audio recordings that can be easily done by thresholding. Then, knowing the duration of the MRI sequence, it was easy to calculate the starting point. Final adjustment was done also automatically using comparison of timestamps of the generator with those recorded into the TTL channel. For evaluation of the tongue tip velocity from images in the mid-sagittal plane, a tool was developed in MATLAB. For the pre-processing of an image I(x, y) before the tracking, we used the filtering algorithm proposed in  [42]  with the following parameters: we assumed the background was 10% of the maximal intensity I max = max(I(x, y)) for the operation of background subtraction: I (x, y) = I(x, y) -0.
The displacement fields are presented with vectors with length N = N x N y N t , and the gradients thus represent sparse matrices of size N × N. The resulting nonlinear least-squares optimization problem was solved using a multiresolution Gauss-Newton scheme. Tongue tip was selected manually by the user in the first frame, and propagated to the next frames using the displacement fields. The illustration of the tongue tip tracking procedure is shown in Figure  2. To enable the comparison with another method, the tongue tip position was tracked fully manually for the whole non-denoised temporal set of the data acquired in the mid-sagittal plane. Since during the fast phases of the tongue motion real-time MRI images exhibit motion blurring, there is some ambiguity in finding the exact position of the tongue tip.
X-ray diffraction Urotropine was purchased from Sigma Aldrich and single crystals suitable for X-ray diffraction were obtained by sublimation at reduced pressure and freshly used for the investigations. Urotropine single crystals were loaded together with ruby spheres into diamond anvil cells (DACs). Diamond anvils with 600-m culets and stainless-steel gaskets preindented to a thickness of 75 m with 300-m diameter holes were used. Diffraction data were collected with a MAR 165 CCD detector at the ID27 beamline ( = 0. 3738 Å) and with a Mar555 flat-panel detector at the ID15B beamline ( = 0. 4110 Å) of the European Synchrotron Radiation Facility (Grenoble, France). Pressure was determined by the ruby fluorescence shift method. 33, 34 Detector distance was calibrated using CeO2 and Si powder standards on ID27 and ID15B beamlines, respectively, whereas other instrument model parameters were determined from single-crystal diffraction experiments on enstatite crystals enclosed in DACs.
Two runs with He used as the PTM were carried out on both beamlines and one run with Ne used as the PTM only on ID15B. The hydrostatic limits of He and Ne are specified in the SI. DACs were rotated from -32 to +32º and diffraction images were collected every 1º with an exposure time of 10 s at ID27 and every 0. 5º with an exposure time of 1 s at the ID15B beamline. Additionally, wide scans were taken at some pressures with DACs rotated in the same angular range. These data and single-crystal diffraction were processed and analyzed using Dioptas and CrysAlis PRO software, respectively. 35, 36 8] [39]  Hydrogen atoms were placed at calculated positions and were refined as riding atoms. CCDC 1982556-1982593 records contain the supplementary crystallographic data for this paper. These data can be obtained free of charge from the joint CCDC's and FIZ Karlsruhe's service to view and retrieve structures via https://www.
ccdc. cam. ac. uk/structures/. The wide scans integrated with Dioptas were analysed using GSAS-II. 40 2] [43]  Def2-TZVP basis set and restricted Kohn-Sham Hamiltonian along with BLYP exchange-correlation functional were employed. 44, 45 Clusters of charges around central molecules were not generated. Hydrogen anisotropic displacement parameters were refined while extinction was not accounted for. CCDC 2005102-2005128 records contain the supplementary crystallographic data for successful HARs presented in this article.
Structural studies Urotropine crystallizes in the cubic space group ‫4ܫ‬ ത 3݉ (no. 217) with two molecules exhibiting the symmetry of 4 ത 3݉ (Td) point group per unit cell, with the molecular center of gravity placed at the 2a Wyckoff position. Urotropine represents a peculiar case where the molecular symmetry is preserved in the site and the factor group (one molecule for primitive cell). The amine undergoes monotonic compression up to 19 GPa both in helium and in neon and the space group symmetry of the crystal is preserved up to the highest pressure (see Figure  1 ). The 3 rd order Vinet equation of state (EoS) fitted to the (V, p) data points collected at ID15B with He used as PTM yields V0 = 347. 71(13) Å 3 , B0 = 8. 53 (16) GPa and B0' = 9. 29(12) (V0 was not fixed in the fitting procedure and its uncertainty comes from the least square fit).
54, 55 One of the reasons we carried out the experiments was to determine whether He permeates urotropine crystal structure in a similar manner to arsenolite. The fact that the unit cells determined for urotropine in Ne and He fall on the same EoS line indicates that He does not permeate it. and Ne (triangles) used as PTM. For all the experimental data, the uncertainties on volumes and pressures are smaller than the symbol size. The experimental points are compared to DFT results denoted B3LYP-D* (small green filled squares). Markers are empty and filled for pressure increase and decrease, respectively. The black line corresponds to a 3 rd order Vinet EoS fitted to the data collected at the ID15B beamline with He as PTM. The fit was performed up to 13 GPa and the dashed line above this pressure serves as a guide for the eye.
A microscopic description of the DCE reaction is currently being developed: the nuclear structure part makes use of the Distorted Wave Born Approximation (DWBA), or of Coupled Reaction Channel (CRC) cross sections, in which the reaction part is described in a full quantum scattering picture. From the experimental standpoint, a systematic set of data will have to face the challenges of the low cross sections and the subsequent requirements of high sensitivity and resolution, in order to pick out the DCE signal in the huge background. The measurement of DCE cross sections foreseen in NUMEN could provide a significant input to the nuclear structure theories of NDBD. Moreover, NUMEN can provide useful information on the relative weight of the NMEs between different candidate isotopes of interest for the NDBD; indeed, the ratio between the measured cross sections is probably a model-independent way to compare the sensitivity of half-life experiments to different candidate nuclei.
Electronics and Data Acquisition The primary task of the NUMEN front end and data acquisition system (DAQ) is to cope with the huge analog data rate coming from the detector subsystems. Then, data have to be converted to digital and finally transferred to a storage system via large bandwidth channels. Apparently, this sounds like a standard procedure; however, there are a few constraints that make it difficult, if not challenging. Due to the intrinsic nature of the MAGNEX magnetic spectrometer, the tracking algorithm is quite complex and consequently slow. Indeed, it requires to sample the signals from many strips in the tracker and to recognize those that belong to a track, performing weighted averages of signals from neighboring wires and matching them to signals from telescopes in the PID detector system.
The overall data output rate depends on the NUMEN running mode. Two main modes are foreseen: inclusive and exclusive. The inclusive mode, for shorter periods at high beam current (nominal 10 13 pps), does not consider coincident GDA data whose rate would be prohibitive. The exclusive mode, in coincidence with the GDA, is only feasible with lower beam current (nominal ≈10 12 pps). In both cases the maximum expected output data rate is of the order of 50-60 MBytes/s.
Target Cooling System In order to study the target cooling system and validate its concept, a smaller scale prototype was built using a preexisting cryo-refrigerator. A target prototype, a HOPG graphite foil 10-μm thick, was screwed to the cold finger of the refrigerator that was installed on the bottom of a spherical vacuum chamber. An infrared laser diode was employed as heating source, and its light was shone onto the target by means of a suitable optical fiber. The target temperature was monitored The overall data output rate depends on the NUMEN running mode. Two main modes are foreseen: inclusive and exclusive. The inclusive mode, for shorter periods at high beam current (nominal 10 13 pps), does not consider coincident GDA data whose rate would be prohibitive. The exclusive mode, in coincidence with the GDA, is only feasible with lower beam current (nominal ≈10 12 pps).
Availability, reliability and durability of hydrogenerators are key features that have driven electrical utilities to implement monitoring and diagnostic methods in order to evolve toward Condition Based Maintenance (CBM). More than ten years ago, Hydro-Quebec has implemented a web-based application, MIDA (Methodology for Integrated Diagnostic of hydrogenerators  [2] ), which was developed at its research institute. MIDA is an integrated diagnostic system for hydrogenerators and is based on the aggregation of individual health indexes from seven diagnostic tools  [2]. MIDA gives a ranking of more than 300 hydrogenerators and thus helps maintenance engineers to better plan maintenance actions. The MIDA centralized database contains all the data from every diagnostic measurement performed on each hydrogenerator since the 1990's.
A large number of these failure mechanisms involve the presence of PD  [3]. At Hydro-Quebec, PD measurements on hydrogenerators have been carried out for the past 30 years and this extensive PD database is integrated in MIDA. As of December 2019, MIDA has more than 33 000 unlabeled PD measurement files. Each of these files must be analyzed and classified by the experts of Hydro-Quebec for PD source recognition which is very time-consuming and can only be done for PD files easily recognizable by the expert. A structured analysis of this huge amount of data is of paramount importance to understand the behavior and evolution of the PD activity. The implementation of an automatic recognition based on an intelligent classification of all PD sources would significantly improve diagnostic and prognostic models for hydrogenerators.
Some of these algorithms are for example visual explanations from CNNs via gradient-based localization  [17] ; a visualization technique of the input stimuli that excite individual feature maps at any layer in a CNN model  [18]  or; a deep Taylor decomposition method for interpreting generic multilayer NNs by decomposing the network classification decision into contributions of its input elements  [19]. When the input data are not images, as for industrial data, the interpretability of the hidden layer's activities is less obvious. Some visualization techniques, as the t-distributed stochastic neighbor embedding projection (t-SNE)  [20] , converts a high-dimensional data set into a 2D-matrix of pairwise similarities. Feature maps of the model are then obtained. The t-SNE method has been used and reported in many research publications such as  [8] ,  [14] ,  [21] -  [25].
The main constraint of this method is the lack of repeatability due to the minimization of the Kullback-Leibler divergence between the input space distribution and the embedding space distribution  [22]. In this paper, a new method based on a visual data analysis to build a PD source classifier with a minimum of labeled data files is proposed. The framework of this methodology for PD source classification is given in Fig. 2. The encoder, part of a Convolutional Variational Autoencoder (CVAE), is used for data projection in a 2D-visualization latent space. The input vectors are encoded and displayed into this 2Dspace, which helps the expert to visually analyze the spatial distribution of the training data set. At the beginning, few data files from the huge unlabeled database are selected from the 2D-latent space.
III. PARTIAL DISCHARGE ANALYSIS PD are minute sparks that occur within voids inside high voltage insulation or in the air around the insulating system. Each PD event does not cause immediate failure, but it will slowly erode the insulation system and will lead to breakdown in years to decades  [43] ,  [44]. The impulses can be detected on-line from sensors connected to hydrogenerators, which is a major advantage, because diagnosis of upcoming problems is possible while the machine is running and generating power. Over the past 30 years, Hydro-Québec has gathered an extensive PD database using two types of commercial measurement instruments. One of the instruments used is a 2D Partial Discharge Analyzer (PDA), which displays the rate of discharge pulses as a function of their amplitude. Up to now, over 33 000 measurement files have been recorded yearly by plant personnel.
This means that two different classifiers C i and C j with i = j will definitely have two opposite responses for the same input data k: i (k) = j (k) ( 13 ) where i (k) and j (k) are respectively the output class obtained by the classifiers C i and C j for the input sample k. To identify these conflict zones, several classifiers C i were trained with the same training data set. Subsequently, all the trained classifiers have been tested over the entire MIDA database. For each input sample k of the MIDA database, if two classifiers have two opposite responses, the input sample k is then considered as a conflict sample. To reduce the proportion of false predictions, the size of the conflict zone must be reduced. For this, experts will choose new learning data files in these conflict areas in order to adjust the learning of the classifier (Fig.
V. RESULTS This section presents the experimental results and is split into four parts. The first part details the way the training data set has been built. By applying the procedure of the algorithm 3, three training rounds were sufficient to adjust the conflict zone between classes. Then, in the second part, the effect of the selected features on the classification performances was visually evaluated. Several combinations of features S i have been tested and applied on the overall MIDA database. In the third part, the effect of the dimension reduction obtained by the convolutional encoder was further studied and evaluated. For this, we have compared the model used in this work, called ''convolutional encoder classifier'' against a classical classifier, called ''traditional classifier'' (i.
The reduction of the conflict zone when new data files were added to the training set is shown for each round in Fig. 10 (D, E  and F ). It is important to note that the conflict zone is the boundary between classes, obtained by the classifier during the training process. This boundary is less confrontational when new learning data files are wisely chosen. A total of 127 data files, which represent 0. 38% of the whole MIDA database, were used for the training process during the third round R 3.
B. EFFECT OF THE SELECTED FEATURES Fig. 11  shows boundaries (i. e. , the conflict zones) obtained on the overall MIDA database by the convolutional encoder classifier according to the feature combinations presented earlier in Fig. 9. The smallest boundaries and the best defined clusters for each PD source were obtained when all features S 1 , S 2 , S 3 , S 4 were used. All other combinations are worse and as it can be seen in Fig. 11. B, when only the feature S 1 is considered, the largest boundary size with the least defined clusters is obtained. In fact, the size of the boundary zone can be used as an indicator of the quality of classification. For instance, during our three rounds of training process the boundary zone decreased from 6 % to 3 % of the latent space.
C. EFFECT OF THE DIMENSION REDUCTION It can be argued that the use of an additional processing function, such as the encoder function of the CVAE, cause loss of information and deterioration of the classification performances. To assess the effect of the additional use of this encoding function, the output classification results obtained by the architecture used (i. e. , the encoder part of the CVAE used jointly with a classifier, which is shown in Fig. 7 ) was confronted with the output classification results obtained without the use of the convolutional encoder. Fig. 12  shows the confusion matrices for these confrontation tests obtained over the entire MIDA database. Several combinations of features S 1 , S 2 , S 3 , S 4 have been tested. At each test, the classifier used without VAE was trained on the same data set obtained after round R 3 of the learning process (see table  5 ).
All the challenge is then to extract the expert knowledge and to find the right formalization of the expert rules to build the input characteristic vector processed by the NN classifier. Many studies in the literature claim in recent years that DNNs, particularly the CNNs, are able to extract characteristic features themselves from raw data provided from a big dataset. However, the results presented in this article show quite the opposite. Indeed, we have seen that the more we put knowledge in the input vector, the more the data space becomes discriminant, thus making the classification easier. We have seen that the separation between classes is transformed from a non-linear separability with high overlapping clusters in the space feature S 1 (Fig. 11. B), into a pseudolinear one in the space feature S 1 , S 2 , S 3 , S 4 (Fig.
In this example, only 0. 38 % of all available data was used for training; so even with more rounds, it should stay below 1 %. To improve the performances of the proposed method, other metrics than the reduction of the conflict area must be defined and used to evaluate the performance of such model during the building process. These metrics should give some answers to these non-exhaustive questions: • How to evaluate the quality of the spatial distribution of an input training dataset? • How to evaluate the features (expert knowledge) which are closely related to the quality of the input vector? We have observed that overlapping of opposite classes decreases when more expert rules are injected to the input vector. • How to evaluate the performances of the CVAE, especially the encoder function? • A more fundamental question concerns the minimum size of the conflict area tolerated by the expert.