dataesr/scientific-paragraphs-categorization huggingface dataset:
The Biomedicine Agency annual data did not either provide the indications of surgical sperm extractions (before ART or before cancer treatment). Consequently, we could not include cancer patients whose fertility was preserved by banking of surgically retrieved sperm (onco-testicular sperm extraction, onco-TESE), which could induce and underestimation of MFP activity before cancer treatment. Another limitation is that the report of the CECOS network did not provide the number of patients referred to the CECOS for whom there was no banking of ejaculated sperm (collection failure, azoospermia, onco-TESE). The data from the CECOS network did not either specify the patients' age; consequently, we could not exclude sperm banking performed in patients aged less than 15 years or more than 49 years.
Nevertheless, the incidence of these two types of cancer is reduced in these age brackets; however, this could also generate an overestimation of the MFP rate in cancer men aged 15 to 49 years, and we could not analyse the relations between cancer patient age and the MFP rate. Due to the suboptimal precision of national French data on TC and L incidence and on sperm banking activity for cancer, our assessment could overestimate the rate of men with TC or L who banked sperm before cancer treatment. The failure to provide information on MFP to patients receiving cancer treatment could lead to impossibility of biological parenthood in patients with lasting posttreatment azoospermia; if the man was not given information before treatment, the oncologist could be held responsible. It would have been very interesting to compare referral rates between the various French regions, and also to include overseas regions in our study; unfortunately, the data from the National Cancer Institute (INCa) were not comprehensive, several centres of the CECOS network were in a region with no cancer data available and INCa data did not include overseas regions.
While Mapping the Daco-Romanian Dialects: Gustav Weigand The first study on the language of any Rudar community came as a bi-product of mapping the Daco-Romanian1 language. The first Atlas2 of the geographic distribution of dialects was worked out by Leipzig professor Gustav Weigand, a member of the Romanian Academy. Starting in 1883 he visited Romania every year for three months of data collection (Rădulescu-Codin 1900: II). He established the Institute of Romanian Language at Leipzig University that amounted to holding seminars in which texts on various dialects were read and interpreted with students and invited guest researchers and professors. The outcomes of these seminars were published in a series of annual reports of which two addressed the subject of the Rudari, the fourth volume (Weigand 1897) and the fourteenth  (Weigand 1908).
Other destinations included Transylvania, Moldavia10 and the south of Danube. Often, after many years, they returned to Wallachia; trends we can see by referring to their onomastics recorded in MapRom Database:  Moldoveanu, Turcitu, etc. By 1838, we find most of the Rudari living in the sub-Carpathian region, along rivers and forests, which enabled combining gold-panning with wood working. Importantly, by 1838 some still belonged to Cozia Monastery, and half were State Gypsies. Very few Rudari belonged to boyars, and in those cases their Rudar heritage was evident only from nicknames like Rudaru or Zlătaru. Nowadays no one knows the gold-washing occupation anymore; few recollect such a past. Not only that, but very few still produce wooden household items. The Rudari no longer have a specific occupation, they generally live in impoverished conditions and are prone to migrate to Western countries.
Further Mapping: Atlas of Romanian Language II, Emil Petrovici and Ion Gheție The Romanian Academy made a second Atlas of Romanian Language (ALR II)  (Petrovici 1938). One part of the data collection concerned Romanian speakers in Yugoslavian Banat and Eastern Serbia. Weigand researched in 1937 the village of Čokešina, near the river Drina. The Yugoslavian official statistical data listed 400 individuals speaking Romanian here. They called themselves Rumâni, when asked if they are Țigani, and Coritari (< Serb. kòrito 'tub'). Their main occupation was carving wooden spoons, and a few were diblárs 'musicians' or farmers. The Serbs called them Karavlaški Cigani 'Black Wallachian Gypsies'. Petrovici selected a set of frequently used words from the basic vocabulary and established isoglosses based on the ALR II maps.
4). The article shows the contribution of the Roms to the language of their surrounding population and the promotion of Romanian language outside Romania. Kahl analyses the language spoken by the "Băieși and Rudari" in order to "provide an estimate over their geographical spreading and comparative research on the status of their language, of their identity and culture in different countries"  (Kahl 2016: 265). Kahl surveys the research of Chelcea19, Petrovici, Calotă, Saramandu, and also a handbook of Romanian dialectology. Petrovici wrote about the Coritari from Serbia while working on the "Atlas of Romanian Language II" project which published data on this sub-dialect and shows on the map the isoglosses and the place of origins, namely southwestern Wallachia. Kahl's investigation is important for many reasons.
These appellations are regional professionyms with ethnic connotations. Since all speak Daco-Romanian, the linguists unanimously point to Romania as the place of origin for the Rudari who then emigrated in various historical periods. The Rudari are bilingual; besides archaic Daco-Romanian, they speak the language of the surrounding population. Their spoken language becomes their identification tag21; and they are better studied outside the borders of Romania. 22 Inside Romania, their speech has evolved through diglossia. So far, the identification of Rudari communities has been accomplished empirically by Calotă, or by Chelcea through reference to Hungarian maps dealing with Transylvanian Băieși. The current database MapRom may set the investigation on Rudari on professional grounds, playing catch up on one century of progress or more in Hungarian field of research23.
Other intruders;  The landform they prefer is the valley […] Sometimes the valley they inhabit takes the name of Valley of the Rudari (Jugur). In this respect, they have a toponymy of their own38  (1983: 11). We reproduce here the list of the villages that contain Rudar settlements nearby, complemented with statistical data from the 1838 Census, and adding the information on the place of origins, as recorded by Chelcea. Table  1  Continuity of Rudari settlements from early nineteenth century to mid twentieth century. (Sources:  Chelcea 1934 Chelcea , 1942 Chelcea , 1943 Chelcea , 1944a;; Demidoff 1841; MapRom Database; Stahl 1990 ).
The Rudari are woodworkers. The Ursari are so named after the bears with which they walk around the country for livelihood; they are also farriers. The Lăieți work brass and practice all kinds of simple crafts. Vătrași or those who live in houses and live in villages, are exempt from taxes and work for their masters. As for the princely Gypsies, the Rudari pay a tax in gold for the right to pan in the rivers, and the others pay a fixed annual tax. The Gypsies [belonging to] private individuals are at the disposal of those who own them, they almost all live in tents and change their places and homes for better livelihoods and to be able to pay their debts. 56 53 Source MapRom Database. 54 Călători străini 2000: X, 1, 116-117. 55 Friedrich Wilhelm von Bauer (1731-1783) was a German in the service of the Russian army.
The information from the 1838 census gives further indication of the changing circumstances of this group and how it differs from those others classified as "Gypsies". The 800 Rudar households in our database6 contained 3,096 persons living in 70 different communities, some of which were quite large. In 1838 they lived mainly in places close to the slopes of the Carpathian Mountains, specifically they were concentrated beside the rivers flowing down from the mountains. They congregated in places where the rivers made a bend where gold dust and flakes accumulated in the sand. Even after their transition to wood-carving, such locations would be important, given that soft-wood trees suitable for carving thrived along riversides. The geographical distribution by counties is shown in Table  5. The biggest Rudari communities were located along the Buzău, Dâmboviţa and Ialomiţa rivers in north-eastern Wallachia.
Number of the land cultivated by the Țigan spoon-makers in 1838 (Source: ANIC Catagrafia 1838, retrieved from MapRom Database) Some had domestic draft animals necessary for travelling with many items to distant markets. Twenty-two Rudar spoon-maker families in Lapoşul Nou kept a pair of oxen, probably for pulling wagons. Only eleven percent of the Rudar spoon-makers cultivated land, with the usual extent being around one acre. A larger number of Țigan spoon-makers cultivated land (28 percent of them), and the average holding was larger than for the Rudari. The richest spoon-maker was seventy-year-old Călin Tudor a Rudar of Măgura village whose household cultivated 7 acres of land and kept 3 horses, 4 oxen, 6 cows, 20 goats, and 10 pigs. The largest number of Rudar spoon-makers belonged to the state, while those in Slam Râmnic were the slaves of Cozia Monastery (15 families) or the Govora Monastery (3 families), as shown in table 13.
Number of the land cultivated by the Zlătari in 1838 (Source: ANIC Catagrafia 1838, retrieved from MapRom Database)Judging by their socio-economic profile, it seems unlikely that the Zlătari were closely related to the Rudari in 1838, although this does not rule out that they may have shared a common past far back in time. Basically, Rudari and Zlătari lived in separate settlements and in different geographic regions, had different access to natural resources, and as rob slaves they belonged to completely different types of owners. If there was a transition from Rudari to Zlătari it must have gone through several stages: 1) an occupational change from gold-washer to goldworker (metallurgist, smelter) and/or to goldsmith (metalworker, jeweller) 2) a legal change from state ownership to boyar private ownership,.
has funded for three years the project " Mapping the Roma Community on Nineteenth Century Wallachia " ( 2017-2021 ) during which the MapRom database has been created. The authors are very thankful to  Professor Joakim Ekman  for his administrative support in the implementation of the project. The  Södertörn University generously supported. Țara Românească DRH C Documenta Romaniae Historica. C. Transilvania DRA Documente privind relațiile agrare în veacul al XVIII Hurmuzaki Documente privitoare la Istoria românilor ,  Colecţia Hurmuzaki Buda Lexicon Lesicon  romanescu-latinescu-ungurescu-nemcescu MDG.
In other works, recognition performance is used as a proxy to evaluate reconstruction  [10, 29]. Sometimes, additional reconstruction-only metrics such as the Root Mean Squared Error (RMSE) or the Dynamic Time Warping (DTW) are used  [4]. This work presents a novel handwriting trajectory reconstruction pipeline from IMU sensors. Our main contributions are: • preprocessing including an alignment strategy between input and ground-truth time series for training, as well as a learning pipeline based on touching strokes, • a neural network architecture inspired by Temporal Convolutional Networks (TCN) to cope with noisy signals from the inertial sensors, • a database that will serve as a benchmark to help advance research, • an evaluation protocol based on the Fréchet distance to assess the quality of the reconstructed trajectories.
In practice, only a limited number of works have focused on OH trajectory reconstruction from IMU signals. The works of Wehbi et al. ,  [29]  (multi-writer approach) and Ott et al,  [21]  (monowriter non-generic approach) used deep learning techniques to reconstruct the OH trajectories from IMU signals as a step towards the OH recognition task. Earlier works such as  [23]  used more traditional approaches (Hidden Markov models  [24] , pen-tip acceleration and angular velocity inference to multi-level writing plans  [3] ) for the same task. Bu et al. [3]  introduces an IMU system based on accelerometer and gyroscope data to reconstruct handwriting. The method infers the tip trace displacements (captured by the IMU) into several planes of OH trajectories using principal component analysis (PCA) and a set of empirically chosen parameters.
Linear Discriminant Analysis (LDA) is used to detect movements which are estimated mathematically. Recently, Wehbi et al. [29]  have proposed an approach that deals with the Stabilo Digipen, on its earlier version 5. 0. They create a first dataset by using the Digipen on a tablet via a Stabilo mobile application. This allows to acquire simultaneously input signals from the pen and OH trajectories from a tablet. The gap between sampling rates coming from the pen and the tablet makes mandatory to align the two time series to get one label for each time frame. It is done through a preprocessing that will be discuss more in details in Section 3. 3. To do so, Wehbi et al. [29]  linearly interpolate the OH trajectory signal from the tablet. However, the linear interpolation may re-distribute the points equally in time, which result in the loss of the online writing dynamics of speeds and acceleration.
The authors observe that the linear interpolation on the whole output signal leads to erroneous alignments due to the variability of the transmission time delay. By applying the linear interpolation on strokes rather than on the whole label, better alignments are obtained at the cost of dropping out from the dataset any sample whose input and output signals have unequal numbers of strokes. The number of strokes is deduced from the force sensor signal (pen side) and the pressure signal (tablet side). Then, they use a Convolutional Neural Network (CNN) made of 3 convolutional layers with batch normalization layers in between to reconstruct the handwriting trajectory.
Using IMU sensors for handwriting recognition As discussed before, only few works perform handwriting reconstruction from IMU sensors. On the other hand, the task of OH recognition from IMU sensor data has been tackled through the use of: i) the pen-tip trajectory signal given by a touchscreen device  [16] ; ii) signals coming from IMU sensors  [3, 19, 21, 28, 29]. Recently,  [19]  introduced a benchmark study that compares several neural networks architectures trained to recognize characters, symbols, words and equations from IMU signals. A CNN/BLSTM architecture proposed in  [22]  obtains the best results. Other works intend to address both OH trajectory reconstruction and character recognition. For instance, Wehbi et al. and the remote device (e. g. tablet)  [12]  or explore domain adaptation  [11, 20]  and explainability  [1]  in the context of OH recognition from the Digipen.
In this section, we present the protocol applied for collecting a new benchmark IRISA-KIHT dataset. We detail the data acquisition process and the protocol to generate the ground truth. For this, Stabilo 1 created the Digipen, a pen ball digital pen with a Wacom tip instead of an ink ball. Several versions of such digital pen exist, such as Digipen kids version 5. 0 that works at 100Hz sampling rates and Digipen basic version 6. 0 that works at 400Hz (the one used in this work).
Data acquisition challenges To create the IRISA-KIHT dataset the acquisition process is quite challenging due to several difficulties. First, the stylus and the tablet have different sampling rates, so the selection of both of them is an important step. As discussed before, we select the Samsung S7 FE tablet and Digipen v6 for data collection. Second, an important degree of freedom comes from the pen orientation that directly impacts pen sensor values. The Digipen design includes grips to naturally position the fingers properly with the stylus on the same orientation. Another challenge concerns the recording of the hovering (pen up) movements. When the pen gets too far from the tablet, the tablet stops recording a trace. This results to parts of the data coming from the stylus that not match any ground truth from the tablet.
It was proved that TCN architecture is most suitable to extract relevant spatial and temporal features for a sequence of frames describing an action compared to an LSTM recurrent network  [17]. Indeed, recurrent architectures are known to suffer from vanishing gradient problems in very long sequences, such as those produced by IMU sensors. Thus, TCN based systems outperforms their LSTM counterparts in different fields of application such as anomaly detection  [7]  or skeleton-based action recognition  [17]. Our model receives 10 of 13 channels of the sensor data. Magnetometer data are neglected due to the interference of the tablet magnetic field with the signal. Figure  5  illustrates our network architecture. It is composed of TCN-like layers, a dense layer followed by a batch-normalization layer before the last dense layer that produces the output of the network (the two (∆x, ∆y) channels).
Evaluation protocol and metric Some works perform trajectory reconstruction as a step towards recognition. Thus, final recognition performance can be used as an evaluation criterion  [29]  and, as discussed before, it cannot reflect well the quality of the reconstruction. Here, the end goal is the trajectory reconstruc- In fact, due to the limited tracking capacity of the tablet over 15mm above the tablet screen, the handwriting signal is partially captured on hovering. For that, we only consider the touching parts (pen-down) for which we can compare a trajectory prediction to the handwriting trajectory from the tablet and compute the Fréchet distance. We used the FAU-EINNS dataset, our IRISA-KIHT dataset, and a subset of it, IRISA-KIHT-S, to compare our processing pipeline to  [29]  IRISA-KIHT and IRISA-KIHT-S contain 38 and 30 writers' recordings, respectively.
Receptive field effect To Table  1  shows that the TCN model with the smallest receptive field size (49) performs better than the ones with greater receptive fields on FAU-EINNS (100Hz) dataset. On our IRISA-KIHT (400Hz) dataset, the TCN model with the greatest receptive field (373) generally outperforms the other sizes of receptive field as seen in Table  2. With a big receptive field, a large context can be exploited to make the prediction as well as the signal noise of long hovering can be absorbed, with the cost of higher number of learnable parameters. Table  3   results on word groups. These two models are quite similar in terms of performance (Figure  6 ). As one of our next goal is to embed the model inside the Digipen, we decided to keep the TCN-49 model to get a good trade-off between the model performance and the number of parameters.
Comparison with related work We compare the performance of our approach against the one of Wehbi et al. [29]  on both FAU-EINNS dataset and our own dataset, called IRISA-KIHT. We also provide results on the subset of the latter called IRISA-KIHT-S which is made publicly available and can hence be used as benchmark for future works. We applied the same evaluation protocol on both models and processing pipelines. Due to the comparatively smaller size of IRISA-KIHT-S, a 3 fold cross validation is used when this dataset is at stake, as shown in    Wehbi et al. [29]  approach (CNN model and linear interpolation alignment) on every dataset, both quantitatively as seen in Table  5  on the average Fréchet distance and qualitatively as illustrated in Figure  7  (Wehbi et al. [29]  appears on the second line of the figure while our approach appears on the bottom line).
Alignment methods and models In the following, we compare two effective pro-   6 , we observe that each step of our approach outperforms the counterpart of the Wehbi et al. [29]  method on both datasets. The results show that our alignment is better whatever the model on all the datasets. In contrast to the linear alignment, our alignment based on the DTW algorithm keeps the writing dynamic, which seems to be essential to reach quality trajectory reconstruction. the pen is hovering. Figure  8  illustrates the effect of over hovering on the alignment process. As illustrated in Figure  8 , we distinguish two   tracking field). However, for sentences, the user needs to space words and he may raise the pen higher while searching where to put the pen on the screen again. To avoid the untracked hoverings in the training datasets, we used the touching data only to train our model.
g. "a") or shape (e. g. "rectangle"). It is also better with double strokes characters (e. g. "û", "ô") where a single tracked hovering can be observed. Similarly, we observe that our model reconstructs quite good the touching parts of the word groups but it fails to find where to start the reconstruction of the next stroke. This may happen due to untracked hoverings (that represents movement hesitations about where to put the pen again on the screen) like in "a -bed" where there is a long untracked hovering between "a" and "bed". A new dataset has been collected using a mobile application, a tablet and a Digipen and we provide benchmark results on it. To tackle with the sampling rate differences between the Digipen and the tablet, as well as the synchronization problem, we introduced a DTW based alignment approach.
Conclusion & perspectives We show through experiments on several datasets that the TCN neural network architecture outperforms the CNN one proposed in Wehbi et al. [29]  as well as the DTW based alignment approach that enhances the performance of both CNN and TCN models. We also show that the model predicts better the touching and tracked hovering parts, when the model is trained on the touching strokes only. The major limitation of our approach is the modelling of the untracked / complex hovering trajectories that is still an open research problem. This constitutes a first important track for future works and could be solved using a semisupervised or unsupervised deep learning framework. Moreover, in this paper, we only evaluated the shape of the reconstruction. We could extend our evaluation to the dynamics of the trajectory reconstruction such as speed, acceleration, etc.
design the TCN model architecture, we pay attention to the sampling rate of the input signal of sensors. It is obvious that the TCN based neural network may have the capacity to absorb the noise of such low quality and noisy signals, depending on the size of its receptive field. In order to evaluate the receptive field effect of our model, we trained and evaluated the model with different sizes of receptive fields equal to 49, 85, 168 and 373 on both FAU-EINNS (100Hz) and IRISA-KIHT (400Hz) datasets.
z), accelerometer rear (x, y, z), gyroscope (x, y, z), magnetometer (x, y, z), and force signals. Tablet signal files contain milliseconds, position coordinates (x, y, z), and pressure force signals. The transcription (labels) file contains labels and the start and stop time-stamps for every sample. Additional files concerning the sensor calibration and recording meta data are provided. The dataset website describes the format and meta data.
5% of writers (8. 3% of samples). Figure 10 shows some samples of the IRISA-KIHT datasets. The IRISA-KIHT-S dataset is a subset of the IRISA-KIHT dataset which is available on request 3. This dataset is composed of 30 record- ings and it is writer balanced as there is one recording per writer. Table 8 presents the IRISA- KIHT and IRISA-KIHT-S statistical description. Every 34-sample recording session generates files from the data acquisition mobile app. The sensor signals file has 15 columns and N rows, where N is the number of IMU signals, times- tamps, and sensor values. The table has 13 columns: milliseconds, accelerometer front (x, y, 3  Available free of charge for research community upon demand for research purposes only.