dataesr/scientific-paragraphs-categorization huggingface dataset:
Redetection of GW170814 and GW170817 PyCBC Live in the O3 configuration detects GW170814 in O2 data as a coincidence between the two LIGO detectors, with a LIGO-Virgo network SNR of ≈ 15. With the method described in Section 2. 3, and using 5 hours of lookback background, the candidate is assigned a false-alarm rate of 1 in ≈ 200 yr. The case of GW170817 is more interesting because of the glitch affecting the LIGO-Livingston data seconds before merger  (Abbott et al. 2017a). Although the glitch is automatically gated by PyCBC Live, the surrounding data are still flagged as affected by a glitch by the low-latency data-quality flags, preventing a LIGO double coincidence from taking place. In addition, the small Virgo SNR also prevents a double coincidence between LIGO-Hanford and Virgo. Nevertheless, using the method described in Section 2.
Latency We measure the latency of the analysis by repeatedly replaying a week of O2 data and analyzing it with Py-CBC Live, thus simulating an actual observing run with a realistic computing configuration. The test amounts to a total wall-clock time of approximately 50 days. For each candidate uploaded to GraceDB, we calculate the latency as the difference between the upload time recorded by GraceDB, and the merger time estimated. Cumulative distribution of PyCBC Live's latency, for a period of replayed O2 data analyzed using the O3 configuration. The latency is defined here as the time elapsed between the estimated coalescence time of a candidate and the time of creation of the corresponding GraceDB event. The shaded region is the range of expected latency due to PyCBC Live alone, as described in  Nitz et al.
We are grateful to  Stuart Anderson ,  Juan Barayoga ,  Patrick Brockill  and  Sharon Brunett  for computing assistance during the operation of PyCBC Live in O3. We also thank  Gregory Mendell  and  Alan Weinstein  for reviewing PyCBC Live's O3 configuration, and  Francesco Pannarale  for comments on the manuscript. TDC was supported by an appointment to the  NASA   Postdoctoral Program  at the  Goddard Space Flight Center , administered by  Universities Space Research Association  under contract with  NASA , during part of this work. GSCD, VVO and TD acknowledge financial support from the  Maria de Maeztu Units of Excellence  program  MDM-2016-0692 , from  Xunta de Galicia (Centro singular de investigación de Galicia accreditation   2019-2022 ), and from  European Union ERDF. GSCD and IWH acknowledge the  STFC  for funding through grant  ST/T000333/1.
We also predict Raman spectra, which are readily-observed and provide information about the atomic structure of the glass. Other properties, such as thermal expansion coefficients, can be of interest but were left out of the modeling at this stage. i-Melt is thus a PGNN "multitask" model, which has the ability to predict different features/properties of the same object: a silicate melt/glass. i-Melt was trained on melt and glass compositions in the K 2 O-Na 2 O-Al 2 O 3 -SiO 2 system, for which a fairly complete, albeit sparse, experimental dataset is available. In this system, i-Melt allows systematic exploration of the links between melt/glass composition, structure and properties, as we describe below. Building on this, we are then able to comment on the source of the compositional control on eruptive dynamics observed for rhyolite melts, as well as on the possible importance of the K/(K+Na) ratio in alkaline magmatic series.
Experimental Design The development of the deep learning model requires the collection and compilation of viscosity, density, refractive index data, and Raman spectra for glasses and melts in the K 2 O-Na 2 O-Al 2 O 3 -SiO 2 quaternary system (Fig. 1 ). The viscosity of supercooled melts for peralkaline compositions in this system is not well understood, and we conducted additional experiments to complement the existing dataset. We further compiled existing data as specified below, prior to developing the i-Melt framework in the Python programming language, by using the PyTorch library. All code and data necessary to reproduce this study are available from Github at https://github. com/charlesll/i-melt ; future improvements will be also distributed via this repository.
Datasets Existing Raman spectra and observations of optical refractive index, density and viscosity of alkali aluminosilicate glasses were selected by hand via a review of the existing literature. Validation of the accuracy of viscosity data across different studies is critical and was checked on melt compositions including Na 2 Si 3 O 7 , NaAlSi 3 O 8 and NaAlSi 2 O 6. We plotted all the literature data for such compositions, and observed the mean trend of these data. Most data fall within 0. 1 log 10 Pa•s, forming a clear general trend. Published data with deviations larger than 0. 1 log 10 Pa•s compared to this general trend were discarded. Density and refractive index come from various publications, in particular from publications reviewed in  Mazurin et al. (1987). Raman spectra were published data from the IPGP and Carnegie Institution for Science laboratories (see below for details).
All the data and their sources are provided in the database available in the software repository. We thus have four different streams of data: -D viscosity , the dataset of melt viscosity measurements, composed of X viscosity chemical composition entries (mole fractions) as well as their associated temperatures (Kelvin) and y viscosity observations ( log 10 Pa•s); -D density , the dataset of glass density measurements, composed of X density chemical composition entries (mole fractions) and y density observations (g cm -3 ); -D Raman , the dataset of glass Raman spectra, composed of X Raman chemical composition entries (mole fractions) and y Raman spectra observations (normalised Raman intensities); -D optical , the glass dataset of optical refractive index, composed of X optical chemical composition entries (mole fractions) as well as their associated wavelength (µm) and y refractive index observations.
Sample synthesis and viscosity-density measurements To extend the viscosity dataset for peralkaline aluminosilicate melts, additional compositions were synthesized at IPGP in Paris from reagent-grade K 2 CO 3 , Na 2 CO 3 , Al 2 O 3 and SiO 2 dried oxide powders, following the protocol described in Le  Losq and Neuville (2013). Viscosity and density measurements follow the protocol used in the Geomaterials laboratory at IPGP  (Neuville, 2006; Le Losq and Neuville, 2013; Le Losq et al. , 2014). Chemical compositions (Table  1 ) have been measured using a Cameca SX50 electron microprobe, with a 30 nA current, U = 30 kV, and 5 seconds of counting. Beam-induced alkali loss was minimized by working with a defocused beam that was moved continuously during the analysis. The mean and standard deviation values reported in Table  1  are calculated from 10-20 individual measurements on each sample.
Raman spectroscopy Raman spectra of silicate and aluminosilicate glasses acquired at IPGP (Paris, France) were recorded using a T64000 Jobin-Yvon ® Raman spectrometer equipped with a confocal system, a 1024x1024 charge-coupled detector (CCD) cooled by liquid nitrogen and an Olympus ® microscope. The optimal spatial resolution allowed by the confocal system is 1-2 μm 2 with a 100× Olympus ® objective, and the spectral resolution is 0. 7 cm -1. A Coherent ® laser 70-C5 Ar + , having a wavelength of 488. 1 or 514. 532 nm, has been used as the excitation line. Unpolarized Raman spectra that were excited with a laser power of 100-150 mW on the sample were acquired between 20 and 1500 cm -1 on pieces of glass from the starting materials. Additional Raman spectra acquired at the Geophysical Laboratory on glasses along the Na 2 Si 4 O 9 -Na 2 (NaAl) 4 O 9 and K 2 Si 4 O 9 -K 2 (KAl) 4 O 9 joins, previously published in  Mysen (1996 Mysen ( , 1999)) , were added to the database.
Data preparation: Train-Validation-Testing split and standardisation If a model performs well when tested against the training dataset, but fails at making reliable predictions on new, unseen datasets, it is said to be 'overfitting'. This is a common problem affecting machine learning models. Here, we deploy several strategies to mitigate it. First, we monitor the phenomenon: the available datasets were split into three different, randomly chosen training, validation and testing subsets using the chemical_splitting rampy package function  (Le Losq et al. , 2019a ) that relies on the scikit-learn  (Pedregosa et al. , 2011)  train_test_split() algorithm. The data were randomly separated by composition to avoid the pitfall of having the same glass/melt composition in the different training, validation and testing subsets, a phenomenon known as 'data leakage'  (Kaufman et al.
, 2012). As we wanted a significant amount of data in the testing subset for viscosity (because this is the most difficult property to predict here), D viscosity was separated with 20% of the compositions in the testing subset, 15% in the validation subset, and 65% in the training subset. D optical and D density were separated with 70% of the compositions in training, 15% in validation and 15% in testing. D Raman was divided in only two train and validation subsets due to its small size, with a 85-15 ratio. This is not problematic, because we do not aim at precise predictions of Raman spectra but rather use this dataset as a way to improve the general predictive capacity of the trained neural network through multitask learning. During the training process, the training subset was used for training the model while the validation subset was used for monitoring overfit and to trigger early stopping.
The latter method consists in stopping the training process when the Root-Mean-Square-Error (RMSE), measured between predictions and observations, on the validation data subset stops decreasing and starts diverging from that measured using the training data subset. This allows stopping the training process before the over-fitting phenomenon appears  (Goodfellow et al. , 2016). The final predictive abilities of the trained neural networks were then evaluated using the entirely unseen testing data subset. After train-validation-test splitting, an important step in any machine learning data preprocessing is standardization of the data. In practice, appropriate data scaling is often essential to obtaining good convergence within algorithms  (Goodfellow et al. , 2016). The goal of re-scaling is to promote feature variations near unity and to ensure that all features have comparable numerical ranges: failure to do so tends to lead to instabilities in the gradient backpropagation process that is central to training neural networks.
i-Melt model technical implementation i-Melt is implemented in the Python programming language, using the Pytorch machine learning library  (Paszke et al. , 2019). It takes four inputs: the mole fractions of the SiO 2 , Al 2 O 3 , Na 2 O and K 2 O components. These are fed into to a neural network composed of k hidden layers, each one having a given number of activation units (a. k. a neurons). Here, we chose to keep the same number of activation units in the different hidden layers. Alternative network structures (e. g. a "triangular" architecture with progressively fewer activation units in the deeper hidden layers) were explored, but did not markedly alter results. Following testing of various alternatives, we adopted the now-popular rectifier linear unit, ReLU  (Glorot et al. , 2011) , as the activation function of activation units, so that an activation unit receiving input, x, returns output y = max(0,x).
(  6 ) 𝑛(𝜆) = √ ( 1 + 𝐵 1 × 𝜆 2 𝜆 2 -𝐶 1 + 𝐵 2 × 𝜆 2 𝜆 2 -𝐶 2 + 𝐵 3 × 𝜆 2 𝜆 2 -𝐶 3 ) The artificial neural network allows us, therefore, to input chemical compositions and obtain predictions for: melt viscosity, within five distinct theoretical or empirical frameworks, glass transition temperature, latent variables like configurational entropy and fragility, glass density, glass refractive index as a function of wavelength, and glass Raman spectra. These predictions depend on a large number of tuneable parameters integral to the neural network. During network training, these parameters are optimized, seeking values that enable good average predictive performance when applied to examples in our database of observed glass properties.
Training i-Melt During training, we monitored the root-mean square errors (RMSE) between measurements and predictions for viscosity from eqs. 1 to 5 as well as density, optical refractive index and Raman spectra, on the training and validation data subsets. A loss function was also added for known S conf (T g ) values from the dataset D viscosity. This was necessary because S conf (T g ) is a parameter that is difficult to evaluate because of its strong correlation with B e. This correlation prevents equation 1 from having a non-ambiguous solution. In practice, the neural network can predict a nearly constant value for the first and large variations for the second, and still obtain good predictive results for viscosity. Addition of a loss function for known S conf (T g ) solved this problem.
Two numerical experiments were performed. The first explored how the neural network architecture affected its performance. For this experiment, we randomly varied the number of hidden layers from 1 to 6, that of hidden activation units from 10 to 500 and the dropout parameter p from 0 to 0. 5. 3000 neural networks with different architectures were generated using the random number generator from the python library numpy. The results of this test show that a moderately deep neural network with 3 to 5 layers and 200-300 units per layer provide the best results, with limited overfitting (Supplementary Figure  1 ). Those results indicate that moderately deep neural network generalizes better than shallow ones on this problem with small datasets. The dropout method helps slightly in preventing overfitting, but is not a critical feature in the present case (Supplementary Figure  1 ).
Following this test, we selected a reference architecture with 4 layers, 300 neurons per layer, and a dropout of 0. 01. The datasets are thus large enough for training efficiently the networks. The only limit is for Raman spectra, with training D Raman having only 48 compositions (total 58). As already explicitly presented, we thus do not expect high performance for Raman spectra predictions. To make the final predictions and further limit the overfitting problems, we trained 100 candidate neural networks with the reference architecture, and selected the 10 best ones. All reported predictions by i-Melt are calculated from the average of those from the 10 best neural networks, following the bagging method  (Breiman, 1996)  that promotes generalization (good predictions on new samples) of machine learning algorithms.
Melt and glass property predictions Using the unseen samples from the testing data subsets, it is possible to test whether i-Melt can provide good predictions despite limited experimental datasets. Trans-theoretical predictions of η (Fig. 3 , see also Supplementary Figure  2 ) are possible with good precision. The RMSE values are equal to 0. 4 log 10 Pa•s on the testing data subset (Table  3 ). For comparison, the RMSE of the best empirical magma viscosity models typically are higher than, or equal to 0. 6 log 10 Pa•s (e. g. Giordano et al. , 2008). Eqs. 1 to 5 all yield similar values (Supplementary Figure  2 , Table  3 ). In detail, predictions in the supercooled temperature domain are affected by larger errors than predictions in the sub-liquidus to super-liquidus domain: testing RMSE values (all equations considered) are in the range 0.
Structural information through Raman spectra predictions In addition to physical and thermodynamic properties, i-Melt has the ability to predict structure-dependent features such as Raman spectra of glass. Considering the very small experimental Raman dataset (Fig. 1b ), global variations of Raman signals have been wellcaptured. The 400-1250 cm -1 portion of the glass Raman spectra can be predicted within a relative error of ~25 % (average relative RMSE between observed and predicted spectra from the validation data subset). For instance, Figure  4a  shows examples of the prediction of the Raman spectra of glasses along the Na 2 O-SiO 2 binary join. Structural information can be extracted from i-Melt Raman spectra predictions. Figure  4b  shows, for instance, the large decrease in the signals assigned to bending/stretching of Q n -Q n intertetrahedral vibrations in the glass network  (Bell et al.
, 1982; Neuville and Mysen, 1996). There is a substantial data gap for R raman between ~2 and ~4 (the value corresponding to that of silica glass) because there are very few Raman spectra in the dataset at SiO 2 concentrations above ~ 90 mol%, and R raman varies strongly with silica content at this level. This data gap originates from the difficulty to obtaining samples above ~ 90 mol% SiO 2 , and only one spectrum (that of silica) constrains the model at very high R Raman values. At such high silica concentrations, unmixing can happen during quench for Na 2 O-SiO 2 compositions (e. g. ,  Jarry and Richet, 2001). In addition, high liquidus temperatures make the synthesis of glasses difficult for Al-bearing compositions  (Schairer and Bowen, 1955, 1956). Nevertheless, information from new experiments in silica-rich melts could be important to bring information to the model about melt/glass structural behavior between an extreme composition like SiO 2 and multicomponent melts/glasses.
Figure1). In practice, this represents many more data points, as there are multiple viscosity measurements for each composition (on average, ~10 observations per composition). For the system considered here, we find satisfactory performance once the dataset reaches around 70 distinct compositions. With more than 70 compositions in the training dataset, RMSE values between model predictions and measurements become lower than 0. 6 log 10 Pa•s. Furthermore, the training and validation subsets exhibit similar RMSE values, indicating that any overfitting problem is limited (Supplementary Figure 1). With the chosen data splitting (see above), we have 113 different compositions in training D viscosity (over a total of 173), and 160 (over a total of 223) and 139 individual compositions (total 205) in the training D optical and D density , respectively.
range of their training dataset. Here, we tested how the model generally behaves when performing such extrapolations by (i) removing some density and optical refractive index data along the SiO 2 -Al 2 O 3 join and (ii) trying to predict a value for a composition very far from those included in its training dataset, the T g of Al 2 O 3. Regarding (i), the model tends to provide a constant value for density or optical refractive index when asked to make predictions for compositions beyond those included in its training set. This situation is both good and bad. It is good because it indicates that the model does not "explode", i. e. starts to predict small or large values very different from the mean ones when extrapolating (this is what happens traditionally with polynomial functions, for instance).
Identifying potential core outcomes We will conduct a systematic review to collect all reported outcomes in the literature. When designing our search strategy, we will carefully consider our methodological decisions taking into account the experiences of other core outcome set developers  [19, 20]. We will search the Cochrane Central Register of Controlled Trials (CENTRAL), the EMBASE and the MEDLINE databases for trials and observational studies reporting on peri-and neonatal outcomes for perinatal interventions for CDH. We will create a search strategy consisting of relevant MeSH terms and free-text words using key words for CDH and all possible interventions. Randomised controlled trials and observational studies will be included. Reviews, case reports (< 3 cases), incomplete reports, book chapters, conference abstracts, letters to the editors and comments will be excluded.
No language limits will be applied. Studies identified by the search will be screened on title and abstract by two reviewers independently (SV and FDB). If inclusion cannot be agreed upon based on the abstract, or if no abstract is available, we will search the full text of the article. All full-text articles will be reviewed by two researchers (SV and FDB) for possible inclusion. If consensus cannot be reached, arbitration will be sought by a third reviewer (JB). Using a standardised data-extraction file (Microsoft Excel, Microsoft Corp, Redmond, WA, USA), we aim to collect the following information: year of study, journal, study design, sample size, participant characteristics, side of the defect, type of intervention(s), funding source and all reported pre-and neonatal outcomes. All identified outcomes will be entered into an outcome inventory and organised into the following categories: foetal outcomes, obstetric outcomes, neonatal outcomes, maternal outcomes, prenatal intervention outcomes and postnatal intervention outcomes.
Such thematic organisation has been successfully used in other COS development studies  [2, 3, 21]. All outcomes will be reviewed and discussed by the ISG with particular emphasis on reducing duplication of outcomes caused by varying terminologies and grouping very similar outcomes together to make the final outcome inventory clear and succinct. Following the agreement, the inventory will be entered into the modified Delphi survey using Delphi survey software (DelphiManager, University of Liverpool, Liverpool, United Kingdom). The wording of the outcomes will be agreed upon after consultation of the patient organisation representatives.
Determining core outcomes The core outcomes will be determined using a modified Delphi method, an established tool for achieving consensus by gathering data from respondents with expert knowledge of the subject under study. The Delphi method allows consensus-building by using a series of questionnaires to extract opinions from participants  [16, 22]. The ISG will suggest potential participants to be invited for filling out the questionnaires. Additionally, we aim to identify potential participants by advertising the study by newsletters and/or online media of national and international patient organisations, including CDH UK, PlatformCHD, the European Reference Network ERNICA, and other relevant professional organisations. There is no robust method for calculating the required sample size but typically groups have included 13-222 participants  [22].
Delphi survey Next, we will send instructions written in plain language by email to the stakeholders, inviting them to fill out the questionnaires, further referred to as the "Delphi survey". This survey includes demographic details, as well as information relating to stakeholders' experiences with or expertise on CDH. The survey will be semianonymous: all participants of the Delphi-survey will be allocated a unique identifier which does not include information that can identify the participant (name, address or telephone number). Only members of the study management group can link the unique identifier to the contact information of the participants in order to contact them to complete the survey or to request feedback on their reasons for not doing so. A Delphi survey consists of at least two rounds  [1].
No consensus (do not classify as a core outcome): Anything else in between the criteria for 1 and 2. The results of the second round will be reviewed by the ISG to decide if another round is indicated e. g. due to a lack of consensus. Each round will close after 4 weeks but this timeframe will be extended in case of insufficient response. Results will be analysed using SPSS (v. 26; IBM Software, Inc. , Armonk, NY, USA) and displayed as median with interquartile range in frequency tables.