dataesr/scientific-paragraphs-categorization huggingface dataset:
1 by comparing the multimodal network performance against the single input network. This resulted in four pairs of networks, a pair for each dataset (multimodal, single input). In the following, we outline the classification results of the multimodal and the single-input classifications. As previously mentioned in the data collection and pre-processing section, we used the selected data to produce four sub-datasets to compare the impact of varying the number of samples per class on the classification. To more clearly see and interpret the results, we present the evaluation details such as the confusion matrix only for the dataset with 19 classes (300 or more paintings per artist). The limited number of classes enables us to better perceive and discuss these results. We evaluate the performance of our classification by using the following metrics: precision, recall, F1 score and Top-1 Accuracy.
Following our test protocol, we performed our evaluations on the four different sub-datasets. For each dataset, we created two networks (single-input and multimodal input) and split the data into 80% training and 20% testing. To feed the data to the multimodal network, we wrote a custom batch generation function that yields more than one input. We performed five-fold cross-validation on the eight models, mainly due to the limited number of samples. Table  5  outlines the average results for the classification metrics of the trained networks on the validation sets (Top-1 accuracy, precision, recall and F1 score). Figure  11  outlines the confusion matrices of the single-input and multimodal input networks for the >300 dataset. For the other datasets, the confusion matrices would not be clear due to the high number of classes.
The results shown are for the best performing networks on the validation set. We evaluate the performance of our classification by using the following metrics: precision, recall, F1 score and Top-1 Accuracy. Following our test protocol, we performed our evaluations on the four different sub-datasets. For each dataset, we created two networks (single-input and multimodal input) and split the data into 80% training and 20% testing. To feed the data to the multimodal network, we wrote a custom batch generation function that yields more than one input. We performed five-fold cross-validation on the eight models, mainly due to the limited number of samples. Table  5  outlines the average results for the classification metrics of the trained networks on the validation sets (Top-1 accuracy, precision, recall and F1 score).
Results Interpretation Based on the previous results, it can be observed that, in the majority of cases, multimodal classification surpasses the single-input classification in terms of accuracy. Additionally, we can see that increasing the number of samples from a given artist contributes significantly to the classification results. This increase can be explained by the fact that more samples per class help deep-learning-based techniques in the generalization, as the intraclass correlation (features categorizing a specific class) can be learned easily and more effectively when many samples representing that class are used in the training. The largest difference in accuracy between the multimodal and single-input classifications was observed in the dataset with more than 100 paintings per artist, where the Top-1 accuracy of the multimodal network was 75% and that of the single-input network was 60%.
We leveraged visual features of assets, which were learned using a CNN, along with textual features learned using a conventional neural network (multi-layer perceptron). Through this development, we tried several designs and settings, especially for the visual recognition part (CNN base models). We relied mainly on transfer learning and fine-tuning of networks pretrained on the ImageNet challenge. Through several comparisons, we found that residual networks yielded the best performance, a result which validates previous research which considered such networks to be the state-of-the-art in visual recognition  [47]. Our painting classification model, as previously described, is designed to take as input multiple types of data, such as image and textual labels. At the training stage, the model receives the data and learns to predict the labels for two tasks, namely the artist attribution and the creation year estimation.
The use of multiple inputs yields better results since, in addition to the visual capture, the textual labels contribute to the feature encoding of the asset. Moreover, instead of predicting a single output, we used multitask learning with hard parameter sharing to predict several output labels at once, leveraging the features extracted from the data samples. This also conditions the output of the network and helps the model to learn correlations between the output labels. The real-time usability of such a system is straightforward. It is a fact that the system takes considerable time to be trained and validated, but once these steps are completed, the predictions are generated instantly. In our study, the first limiting factor was the dataset. The datasets we collected were not complete regarding the labels required to classify cultural data of other types such as ceramics, weapons, carpets, etc.
Several improvements could form foundations for future work. The use of future CNN designs as base networks could be beneficial, especially with the recent progress achieved in the ImageNet challenge. Additionally, the inclusion of other data sources and the use of completely annotated data samples to train the models could significantly help diversify this approach to other cultural categories. Furthermore, the inclusion of other annotation techniques for textual labels such as the use of name-entity recognition (NER) to extract tags directly from cultural asset descriptions is highly likely to improve means of creating cultural datasets and thus the generalization of the classification.
Pregnancies and deliveries after treatment Table  III  shows pregnancy and delivery rates (DR) after IVF or ICSI and after FER (after both IVF and ICSI). As numbers of initiated cycles have constantly been incompletely reported, outcome data were calculated per aspiration. Amongst the 39 reporting countries, 35 were able to provide both pregnancy and delivery data per aspiration after IVF (n ¼ 30) and/or ICSI (n ¼ 35). For FER when considering thawing cycles, 29 and 28 countries were able to report pregnancy and delivery rates, respectively. Supplementary Table  SIV  shows the numbers of deliveries for the 21 countries with full coverage of the reporting. Pregnancy and delivery rates (for all types of treatment cycles) varied significantly from one country to another, as in previous years.
Sum of fresh and FER ('cumulative') DR Supplementary Table  SXIX  provides an estimate of a cumulative DR (different from a true cumulative DR, which is based on all transfers resulting from one aspiration). It was calculated as the ratio between the total number of deliveries from fresh embryo transfers and FER performed during a year (numerator) and the number of aspirations during the same year (denominator). The calculation included data from 34 countries (38 countries in 2016) where an overall rate of 30. 8% (29. 6% in 2016) was recorded. The gain taken from additional FER (over DR from fresh embryo transfers) was 12. 3%  (10. 5% in 2016) , with the highest benefits recorded for Ukraine (þ29. 8%), Finland (þ21. 1%), Sweden (þ20. 2%), Armenia (þ19. 7%) and Latvia (þ19. 2%), and the lowest for Serbia (0%), Montenegro (þ3.
Cross-border reproductive care Eight countries reported data on cross-border patients: Albania, Belarus, Denmark, Poland, Portugal, Slovenia, Spain and Switzerland. A total of 16 733 cycles (19 239 in 2016) were reported, 16. 9% (22. 1% in 2016) of which involved IVF/ICSI with the couple's own gametes, 50. 1% (46. 6% in 2016)  were  EDs and 25. 8% (21. 8% in 2016)  were IVF or ICSI with semen donation. Additionally 7298 IUI with sperm donation (7062 in 2016) were registered. Information regarding the countries of origin was very incomplete and not reliable enough to obtain any conclusive information. The main reasons reported by patients were to have access to a technique not legally available in their home country  (41. 9%; 39. 1% in 2016)  or to seek a higher quality treatment  (25. 7%; 23.
5%) when the proportion of single embryo transfers is above 80%. As recorded twin and triplet rates were slightly lower for FER, as in 2016, the increase in FER could also have influenced multiple birth rates. The impact of foetal reductions remains, however, unknown: 61% of the 39 countries in 2017 reported data on foetal reductions, but only a limited number of the countries (17) actually performed them. In the future, it is highly expected that efforts will lead to the ultimate goal of the birth of one healthy child  (Land and Evers, 2003)  per embryo transfer and to a reduction of prematurity associated with multiple births. Looking at the evolution of the proportions of deliveries before 37 weeks according to multiplicity (Fig. 3 ), it is rather clear that besides efforts to reduce triplets, the focus needs to be on reducing twins where preterm births are still as high as 45.
ACKNOWLEDGEMENTS The author wishes to thank  Daniel Demellier , archivist at the  Pasteur Institute in Paris , for help with sources; the anonymous peer reviewers and editors for their suggestions and critiques; and  Kapil Raj ,  Dominique Pestre ,  Daniel Dutra , and  Marcella Souza  for their comments during both research and writing. He is especially grateful to the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)  for funding his full doctoral studies abroad (Processo:  99999. 001241/2015-00 ), of which this article is a product.
Methodology We report data elicited from three consultants, all native signers of LSF. 1  They were two men, Thomas Lévêque and Yohan Marcelino, and a woman, Laurène Loctin, all aged between 28 and 29. Thomas comes from the South West of France, Yohan from the North West and Laurène from the region of Paris. They all regularly collaborate with our research group as LSF consultants. As QAP are frequent in LSF we started the elicitation by showing examples taken from Internet videos so that our informants could identify the structure we were interested in. After confirming, through acceptability and felicity judgments, that the QAP were not interpreted as questions, we collected data in two steps using various fieldwork techniques. First, we obtained natural baselines through elicitation tasks  (Stegenwallner-Schütz, Haendler, and Adani 2014; Belletti and Guasti 2015)  asking to describe a picture in three ways: through a simple sentence, a QAP and a question.
This study offers a collocate analysis of the modal marker no doubt (ND) in the EEBO, ECCO and EVANS combined corpora using Sketch Engine. The purpose is to determine the diachronic patterns of usage of ND, and secondly to compare results with the conclusions of existing diachronic pragmatic studies of modal markers. The first step identified five patterns of behaviour based on AM score in decreasing order of frequency: 1 -NDB (no doubt but); 2 -TISND (there is no doubt); 3 -MND (make no doubt); 4 -(ND (parenthetical use); 5 -Ndont (no doubt on't). The second step consisting in partitioning of the corpus following Hilpert and Gries (2016) produced 3 distinct periods based on EHBO data (1580-1669, 1670-1759, 1760-1799). The findings showed that the relative usage of ND for each period remained remarkably consistent, especially the persistence of non-grammaticalized behaviours MND and TISN.
This paper is organized as follows. Section 2 deals with the background on modality and diachrony, laying out the issues at stake for this study. Section 3 presents the corpus material, and justifies the method chosen to collect data on no doubt (ND) in the English Historical Book Collection corpus comprising data from the EEBO (English Historical Books Online), EC-CO (Eighteenth Century Collections Online) and EVANS corpus (5007 books published in America 1639-1800). Section 4 then presents the findings and summarizes the patterns found to be significant using a collocate analysis of ND. From there, a careful discussion of timeline partitioning is proposed, pointing out the issues or confounding factors relating to a diachronic approach (Section 5). In Section 6, the findings regarding diachronic patterns are presented, including the discussion of their significance.
Frequency of combination of no + doubt in the English Historical Book Collection (EHBO) Before I begin with a quantitative analysis of ND, let us consider the corpus data for the English Historical Book Collection or  EHBO (1473 EHBO ( -1820)). As mentioned previously, the corpus comprises the data from the EEBO, Early English Books Online, a collection of English books published between 1473 and 1820. It also comprises ECCO Eighteenth Century Collection Online, 2473 titles printed in the United Kingdom between the years 1701 and 1800, and the EVANS corpus based on 5007 books published in America during the years 1639-1800. The corpus contains over 800 million words and over 30,000 texts. The EHBO therefore predates  Traugott's CLEMETEV corpus (2011)  in her study of no doubt and surely which focuses on 1710-1920.
In descending order of frequency in the corpus, four patterns emerge: a) NDB: often coincides with TIND and MND b) TIND: has 3 variations of complementation. BUT is by far the most frequent over  THAT and OF. Traugott (2011: 69)  shows the historical origins of the subordinative BUT, which was gradually replaced as a complementizer by THAT. c) MND: I + MND is by far the most frequent within the class. The use of the personal pronoun I as subject shows the usage is firm stance- The combined patterns allow for a better understanding of the possible alternations that can occur for a given pattern. However, as far as the methodology of the diachronic study is concerned, it seems preferable to avoid reducing the datasets. Given that BUT complementation is by far the most frequent, it will be kept as a separate pattern from the existential formula TIND and the verbal structure MND.
Column 2 provides the total number of tokens and words to the total number of words for each century, while Column 3 gives the corresponding proportion each century represents for the entire corpus. Line 1 represents the overall EHBO data for the period ranging from 1473-1499, Line 3 represents the overall EHBO data for the period 1500-1599, and so on. Lines 6, 7 and 8 represent the total number of tokens and words in the EEBO Phase 1, the ECCO and EVANS projects, while line 9 quantifies the concordance results for ND corpus: this subcorpus represents 54% of the overall EHBO corpus. What Table  8  clearly shows is the extreme data variation from one century to the next. The discrepancies of usage per century are considerable: only the period ranging from 1500-1800 provides non-negligible data (i.
44. In the EHBO data, NDont and parenthetical (ND were shown to be the least consistent overall. NDont was found to be significant only for period 2, (ND peaking in period 1. The findings do not show evidence of definitive linear shift in patterns of ND. Several factors could explain this: 1) evidence of shift, i. e. evidence of grammaticalization, may occur later than the corpus timeline (post 1800) or 2) the corpus data may not be sufficiently reliable to carry out a statistical analysis or 3) the history of ND may not follow usual pathways as per  Davidse et al. (2011). To test the validity and significance of the results, a comparison with another historical corpus of Early Modern English such as the Helsinki Middle English corpus, or the Penn Historical Corpus, would enable us to consider different parameters between corpora which might account for varying results.
ACKNOWLEDGMENTS The project leading to this publication has received funding from the  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir " project. Support was also provided by the  Institut Universitaire de France  ( CLIMSORIENT  project), the project  MISTRALS (Mediterranean Integrated Studies at Regional and Local Scales) Paleomex-Envimed-GEOISRAEL , and by the  Hatter Laboratory, Recanati Institute for Maritime Studies, University of Haifa. This work was undertaken within the framework of  MG's PhD (Aix-Marseille Université ,  École Doctorale   355 ). MG acknowledges  MOPP-Medflood (INQUA CMP  project  1603P ) for fruitful discussions. We thank the.
We estimated effects in the statistical model by a Hamiltonian Markov-chain Monte Carlo method, implemented in the library rstan version 2. 15. 1  [87]  of the statistical computing language R version 3. 4. 1  [88]. We allowed a warmup of 500 iterations for each of two chains and subsequently generated 500 parameter samples per chain, to obtain a total of 1000 posterior samples for inference. We examined trace plots of model parameters and used convergence checks implemented in rstan to ensure mixing was adequate for inference. We examined scaled and squared Mahalanobis distances between observations and their predicted values to check goodness of fit. These distances were compared to theoretical quantiles of the F-distribution  [89]  by use of a quantile-quantile plot (S1 Fig).
Acknowledgments This research was conducted as part of the first author's PhD dissertation with equipment and facilities provided by the  Prehistoire et Technologie   UMR 7055  and the  Maison Archeologie et Ethnologie ,  Rene-Ginouvès  (MAE), the  Department of Human Evolution  at the  Max Planck Institute for Evolutionary Anthropology , the  Max Planck Weizmann Center for Integrative Archaeology and Anthropology , and the  Department of Anthropology, University of California, Davis (UC Davis ). We are grateful to  Isabell Eichardt of Coltène/Whaledent AG  for providing dental mold materials for this experiment, to  Sarah Freidline-Stimman  ( MPI-EVA ) for training on the use of dental mold materials and the confocal microscope, to  Sylvain Laporte  ( MAE, USR 3225 ) for building the exterior platform for the rocking machine, to  Alexandra Legrand-Pineau  ( MAE ,  USR 3225 ),  Marie Soressi  ( Leiden University ), and  Anna Goldfield  ( UC Davis ) for conversations and valuable input, and to  Matthew Espe  ( UC Davis ) for help with optimizing the rstan code.
14 However there is still a lack of real-life data on patients treated with PP in Europe. The Long Acting Outcomes Study in Schizophrenia (LAOS) was a multicenter observational retrospective cohort study conducted in 43 centers in France. It included schizophrenic patients who initiated a treatment by PP or RLAI during the initial hospitalization. The primary objective of the study was to evaluate and compare the impact of being treated by PP or long-acting risperidone injection on the length of stay on initial hospitalization of schizophrenic patients. Secondary objectives included evaluation and comparison of risk of rehospitalization and treatment duration. Our hypothesis is that PP is associated with better patient compliance and reduced disease management costs through a decrease in health resource use compared with long-acting risperidone injection.
Monitoring Plan Data were directly collected in electronic case report form, with automatic controls and alerts when potential incorrect data were entered (outlier data check), by investigators. To ensure the quality of data collected in this study, on-site monitoring visits were carried out by independent clinical research assistants trained in the study protocol. A first monitoring visit was performed, after the sixth patient, to check that patients were informed by investigators about the collection of their medical data and were not opposed to it, to control that all required data entered into the electronic case report form were in accordance with data from medical records and to check that sites performed the study in compliance with the study protocol and current regulations. A total of 60 monitoring visits were carried out and allowed to monitor 308 patients (75% of the patients included) by direct access to medical data records or by investigator interview (depending on hospital's practices on medical records access).
Data Collection For each patient, a patient case report form was filled retrospectively by participating psychiatrists, in providing information on patient attributes. These related to demographics, type of schizophrenia, disease history and severity, initial full-time hospitalization (admission and discharge dates, reason having led to hospitalization, antipsychotic treatment at the time of admission to hospital, treatment compliance issues, treatment resistance, treatments related to the disorder prescribed and administered during the hospital stay (with start and end dates, dosage, and administration route), other nonmedical therapeutic measures during hospitalization and other concurrent disorders). At the end of the initial hospitalization and throughout the follow-up period, were also been collected all the types of hospitalizations, all the consultations as an outpatient, all the visits to an emergency unit, taking part in activities organized by parttime therapeutic reception centers, the number of suicide attempts and forced hospitalizations, all the other nonmedical therapeutic measures and the hospitalization alternatives.
Treatments related to the disorder prescribed and administered during the hospital stay, with start and end dates, dosage, and administration route were also collected. Finally, date and circumstances of the last contact, initiated treatment (PP or RLAI) continued or discontinued (with discontinuation data and reason). Patients were informed about the study and were not opposed to the use of their data. They also signed a consent form to authorize the access to their medical data. The study protocol was approved by an independent scientific committee and in accordance with French law, the Ethics Committee's approval was not required as the protocol was strictly observational and usual practice was unchanged. However, the study protocol was approved by the Advisory Committee on Information Processing in Research in the Field of Health and by the French Data Protection Authority.
Moreover, the study design was also associated with some limitations. Indeed, owing to the retrospective design, data collected can be biased (unreliable recorded data). Therefore, to ensure the quality of data collected, on-site monitoring visits were conducted by independent clinical research assistants. The different periods of data collection for PP and RLAI could also lead to differences between the 2 groups (e. g. , changes in patient's profile or current practice). To account for differences in individual patient characteristics, the propensity score weighting method was used. Finally, the launch date of PP was January 2013, and PP patients included in the study were patients who initiated PP from July 2013. The period between the launch date of PP and the first date of inclusion of PP patients was relatively short.
This contribution is based on data drawn from the Corpus of Contemporary American English (COCA) and consists of a qualitative analysis of the adjectives occurring in the sequences feel ADJECTIVE and HAVE a(n) ADJECTIVE feel. The hypothesis defended here is that the verb is connected with subjective experience anchored in a particular situation whereas the substantive is at one remove from subjective experience, and refers to codified sensations and emotions. The first section of this contribution deals with the state of the question; secondly, the paper takes stock of the evolution of the verb and the noun feel and shows that the meanings under scrutiny are relatively recent. Thirdly, examples of the two constructions extracted from the COCA are analyzed; finally, the last section compares HAVE a(n) ADJECTIVE feel and HAVE a(n) ADJECTIVE feel to it.