dataesr/scientific-paragraphs-categorization huggingface dataset:
28, 29 0] [81]  ). In the Olla Oil Field, we see Δ  13   (Fig. 3 , Extended Data Table  2 ); these temperatures preclude a thermogenic origin for the CH 4 and are instead consistent with a microbial contribution. Samples O4 and O6 are in internal clumped isotopologue equilibrium at temperatures lower than expected for the thermogenic CH 4 and appear to be approaching equilibrium with current reservoir conditions. The remaining samples from Olla have already reached apparent equilibrium with the current reservoir conditions in Δ 13 CH 3 D but have yet to reach apparent equilibrium in Δ 12 CH 2 D 2 probably due to kinetic effects. The clumped isotopologues at Olla are approaching equilibrium at reservoir temperatures, which would be accounted for by active AOM within the Olla Oil Field. This supports the conclusion that there is active microbial methanogenesis and AOM driving the carbon isotopes to approach equilibrium under current thermal conditions in the Olla reservoirs.
Modelling of the Pannonian Basin The unequivocal identification and quantification of CO 2 -related microbial methanogenesis at Olla has globally significant implications, and warrants the reconsideration of reservoir processes at other sites worldwide. Re-examination of previously published datasets allows the elucidation of the significance of methanogenesis in different reservoir environments, especially in systems that have or had naturally high CO 2 concentrations, such as the Pannonian Basin. A significant proportion of the CO 2 found in natural gas fields within the Pannonian Basin is of mantle origin  20. The initial, CO 2 / 3 He in the Szegholm North, Szegholm South, Ebes and Hajduszoboszlo gas fields was estimated to be 7. 9 ± 5. 4 × 10 9 , consistent with the European subcontinental lithospheric mantle range (0.
Acknowledgements R. L. T. was supported by a  Natural Environment Research Council  studentship (grant reference  NE/L002612/1 ). C. J. B. and P. H. B. acknowledge  A. Regberg  and  B. Meurer  for their support of the project and help with sample collection. C. J. B. was part supported by an  Earth4D   CIFAR  fellowship. P. H. B. was supported by  NSF  awards 1923915 and 2015789. O. W. was supported by  Natural Sciences and Engineering Research Council of Canada Discovery  and Accelerator grants awarded to the  Sherwood Lollar research group  and acknowledges  B. Sherwood Lollar 's support for the project. Z. M. S. acknowledges  J. Biddle  and  G. Christman  for their help in generating the microbial data.
Data availability The geochemical data that support the findings of this study are available in the NERC EDS National Geoscience Data Centre at https:// doi. org/10. 5285/a4070f5d-2064-4caf-a82c-79a786d6af9e. The microbial SRA and biosample data can be found at https://www. ncbi. nlm. nih. gov/bioproject/PRJNA744568. Source data are provided with this paper.
Measurements All measures were collected prospectively beginning on 17 July 2020, after more than five months from subjects' discharge (mean time 169 days, standard deviation (SD) 28 days). We recorded demographic and anthropometric variables, data concerning the hospitalisation, clinical symptoms, and gas-analysis. Lung function and CPET procedures were performed according to international recommendations  [3, 9]. A flow-sensing spirometer connected to a computer for data analysis (Jaeger MasterScreen PFT System) was used to measure lung function. Forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), total lung capacity (TLC), and inspiratory capacity (IC) were recorded. FEV1/FVC ratio and IC/TLC ratio were taken as the index of airflow obstruction and resting hyperinflation, respectively.
Patients were continuously monitored with a 12-lead electrocardiogram (ECG) and a pulse oximeter; blood pressure was measured every two minutes. Stopping criteria consisted of symptoms, such as unsustainable dyspnoea, leg fatigue or chest pain, a significant ST-segment depression at ECG, or a drop in systolic blood pressure or oxygen saturation ≤84%  [3]. Oxygen uptake (VO2) at the peak was expressed in mL/kg/min. The ventilatory response during exercise was expressed as a linear regression function by plotting minute ventilation (VE) against carbon dioxide production (VCO2) obtained every 10 s, excluding data above the ventilatory compensation point, and the slope (VE/VCO2 slope) and Y-intercept (VE/VCO2 intercept) values were obtained from the regression line. We used the regression equation of VE/VCO2 slope for healthy subjects, according to Sun et al.
Acknowledgments: We are indebted to all medical and nursing colleagues for their assistance and cooperation in this study. The RESPICOVID study investigators are:  Ernesto Crisafulli  (Principal Investigator) and in alphabetical order  Maria Chiara Ambrosetti ,  Marco Bodini ,  Francesco Bordin ,  Silvia Bozzetti ,  Michele Braggio ,  Fabiana Busti ,  Marco Caminati ,  Luca Dalle Carbonare ,  Gianluigi Dorelli ,  Angela Federico ,  Marcello Ferrari ,  Sergio Ferrari ,  Armando Fiore ,  Daniele Gabbiani ,  Domenico Girelli ,  Lucia Guidolin ,  Elettra Libener ,  Andrea Lobba ,  Giulia Magnani ,  Giancarlo Mansueto ,  Elisa Mantovani ,  Giacomo Marchi ,  Sara Mariotto ,  Alessio Maroccia ,  Claudio Micheletto ,  Francesca Nalin ,  Laura Sagramoni ,  Giulia Sartori ,  Gianenrico Senna ,  Francesca Stefani ,  Stefano Tamburin ,  Virginia Trevisan ,  Rachele Vaia ,  Maria Teresa Valenti ,  Alice Vianello ,  Alessandro Vignola ,  Donato Zipeto  from the  University of Verona  and  Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy.
135 For children and adolescents, a close relation exists between QOL and cognitive impairment, particularly attention deficit. 82, 136 In adults, specific data are scarce but point to the same direction,  137  particularly the relation with cognitive impairment and treatment adverse events. 41 In patients older than 50 with drug resistant temporal epilepsy, an impaired cognitive profile (verbal memory performance) was assessed in half of patients by neuropsychological examinations. 138  specific period with higher risk for these complications is the transition between adolescence and adulthood. Patients and their families face several issues, including the change from childhood neurologist to adult neurologist. Over the past years, several studies have emphasized the importance of adequately manage this critical period, especially with dedicated transitions clinics in the most difficult situations.
When calculating the initial geometry of the molecules, the equilibrium interatomic distances were taken from the PubChem database  [25] [26] [27]. The total energies of the considered molecules were determined for their ground states with singlet multi-ISSN 2071-0194. Ukr. J. Phys. 2021. Vol. 66, No. 9 plicity and for their singly charged positive ions with doublet one. Since the ionization energy is defined as the difference between the corresponding total energies of the atom and its positive ion, the relative contribution of the molecular vibrational energy is insignificant. Therefore, to simplify the calculations, the vibrational energy of atoms in the molecules was neglected. It is known that, under certain temperature conditions, molecules in the gas phase can be in various excited ( * ) and even ionized ( + ) states  [15].
Secondary formation of marine-derived WSOC and implications It is possible that the aging of marine primary OA (POA) and subsequent formation of more oxidized OA significantly contributed to the observed WSOC mass. Mallet et al. (2018)  presented an 8-year satellite dataset of the distribution and variability of marine aerosols over the southern IO, which included the current aerosol sampling site. They suggested that aerosols are mainly confined below 2 km above sea level and are dominated by sea salt over the southern IO. However, the mass fraction of sea salt in the submicrometer particles observed at the Maïdo observatory (2160 m a. s. l. ) was insignificant, which resulted in a significantly lower contribution from marine primary aerosol (PA) (Figs. 8  and 9 ). Therefore, the current results indicate that the aging of marine POA is insignificant, and the contribution of oxidation of VOCs from the sea surface to the WSOC mass up to the lower FT is likely more important.
Acknowledgements. We would like to thank  Jean-Marc Metzger  for his support in the deployment of the aerosol samplers. We also thank  Nur Hussain  for his help in making the global map of marine primary productivity. The authors acknowledge OPAR ( Observatoire de Physique de l'Atmosphère à La Réunion ), funded by  CNRS-INSU  and  Université de La Réunion  and managed by  OSU-R (Observatoire des Sciences de l'Univers à La Réunion ,  UMS 3365 ). Henning Finkenzeller  and  Rainer Volkamer  acknowledge  NSF  awards  AGS-1620530  and  AGS-2027252. Financial support. This research has been supported by  JSPS KAKENHI  (grant nos. JP16H02931  and  JP19H04233 ) of the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan , and by the  Belgian Federal Science Policy Office  (grant no.
Data availability. The measurement data of the aerosol samples are provided in the supplement. The other data are available upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-17017-2021-supplement. Author contributions. YM designed the research. The overall measurements and analysis of the aerosol samples were performed by SAS, ET, and YM. SAS and YM wrote the manuscript. JB, RV, and TS coordinated the project and the aerosol samplings at the research sites. HF, JB, AC, OM, and YM made aerosol samplings. FLEXPART simulations were performed by BV, SE, and JB.
Preliminaries and Related Work Consider a dataset of m-dimensional observations D = {U i } N i=1. Given prior p(θ) for a d-dimensional set of parameters, and a likelihood model p(D|θ), the posterior is obtained by means of Bayes theorem as follows: p(θ|D) = p(D|θ) p(θ) p(D) (1) where p(D) is also known as the model evidence, defined as the integral p(D) = p(D|θ) p(θ)dθ. Except when the prior and the likelihood function are conjugate, Equation (  1 ) is analytically intractable  [13]. However, the joint likelihood term in the numerator is typically not hard to compute; this is a key element of many MCMC algorithms, since the normalization constant p(D) does not affect the shape of the distribution in any way other than scaling. The posterior distribution is necessary to obtain predictive distributions for new test observations U * , as: p(U * |D) = p(U * |θ)p(θ|D)dθ (2) We focus in particular on Monte Carlo methods to obtain an estimate of this predictive distribution, by averaging over N MC samples obtained from the posterior over θ, i.
e. , θ (i) ∼ p(θ|D) p(U * |D) ≈ 1 N MC N MC ∑ i=1 p(U * |θ (i) ) (3) We develop our work by working with an unnormalized version of the logarithm of the posterior density, by expressing the negative logarithm of the joint distribution of the dataset D and parameters θ as: -f (θ) = N ∑ i=1 log p(U i |θ) + log p(θ). (4) For computational efficiency, we use a minibatch stochastic gradient g(θ), which guarantees that the estimated gradient is an unbiased estimate of the true gradient ∇ f (θ), and we assume that the randomness due to the minibatch introduces a Gaussian noise: g(θ) ∼ N(∇ f (θ), 2B(θ)), (5) where the matrix B(θ) denotes the SG noise covariance, which depends on the parametric model, the data distribution and the minibatch size. A survey of algorithms to sample from the posterior using SG methods can be found in Ma et al.
[5]. In Appendix A we report some well-known facts which are relevant for the derivations in our paper. As shown in the literature  [10, 14] , there are structural similarities between SG-MCMC algorithms and stochastic optimization methods, and both can be used to draw samples from posterior distributions. Notice that the original goal of stochastic optimization is to find the minimum of a given cost function, and the stochasticity is introduced by sub-sampling the dataset to scale. SG-MCMC methods instead aim at sampling from a given distribution, i. e. , collecting multiple values, and the stochasticity is necessary explore the whole landscape. In what follows, we use a unified notation to compare many existing algorithms in light of the role played by their noise components. It is well-known  [15] [16] [17]  that stochastic gradient descent (SGD), with and without momentum, can be studied through the following stochastic differential equation (SDE), when the learning rate η is small enough (In this work we do not consider discretization errors.
Experiments The empirical analysis of our method, and its comparison to alternative approaches from the literature, is organized as follows. First, we proceed with a validation of I-SGD using the standard UCI datasets  [26]  and a shallow neural network. Then we move to the case of deeper models: we begin with a simple CNN used on the MNIST  [27]  dataset, then move to the standard RESNET-18  [28]  deep network using the CIFAR-10 [29] dataset. We compare I-SGD to other Bayesian sampling methods such as SGHMC  [4] , SGLD  [2] , and to alternative approaches to approximate Bayesian inference, including MCD  [12] , SWAG  [9]  and VSGD  [10]. In general, our result indicates that: (1) I-SGD achieves similar or superior performance regarding competitors, when measuring uncertainty quantification, even with simple datasets and models; (2) I-SGD is simple to tune, when compared to alternatives; (3) I-SGD is competitive when used for deep Bayesian modeling, even when compared to standard methods used in the literature.
Regression Tasks, with Simple Models We consider several regression tasks defined on the UCI datasets. We use a simple neural network configuration with two fully connected layers and a ReLU activation function; the hidden layer includes 50 units. In this set of experiments, we use the following metrics: the root mean square error (RMSE) to judge the model predictive performance and the mean negative log-likelihood (MNLL) as a proxy for uncertainty quantification. We note that the task of tuning our competitors was far from trivial. We used our own version of SGHMC, based on  [11] , to ensure a proper understanding of the implementation internals, and we proceeded with a tuning process to find appropriate values for the numerous hyperparameters. In this set of experiments, we omit results for SWAG, which we keep for more involved scenarios.
Tables  1  and 2  report a complete overview of our results, for a selection of UCI datasets. For each method and each dataset, we also included how many out of the 10 splits considered failed to converge, indicated as F =. As explained in Appendix C we implemented a temperature scaled version of VSGD. A clear picture emerges from this first set of experiments: while for the RMSE the performance is similar for different methods, for the MNLL averaging over multiple samples clearly improves the uncertainty quantification capabilities. SGHMC is in many cases better than alternatives, considering however the standard deviation of the results it is difficult to claim clear superiority of one method over the others.
Classification Tasks, with Deeper Models Next, we compare I-SGD against competitors on image classification tasks. First, we use the MNIST dataset, and a simple LENET-5 CNN  [30]. All methods are compared based on the test accuracy ACC,MNLL and the expected calibration error (ECE,  [31] ). Additionally, at test time, we carry out predictions on both MNIST and NOT-MNIST; the latter is a dataset equivalent to MNIST , but it represents letters rather than numbers. (http://yaroslavvb. blogspot. com/2011/09/notmnist-dataset. html, accessed on 24 October 2021) This experimental setup is often used to check whether the entropy of the predictions on NOT-MNIST is higher than the entropy of the predictions on MNIST (the entropy of the output of an N cl classes classifier, represented by the vector p, is defined as - N cl ∑ i=1 p i log p i ).
For this set of experiments the best performing is BASELINE R. As can be appreciated by comparing Table  3  and Figure  1 , while it is possible to increase the classical metrics, I-SGD (and other methods) still outperform by a large margin the baselines in terms of detection of out of distribution samples. We now move on to a classical image classification problem with deep convolutional networks, whereby we use the CIFAR10 dataset, and the RESNET-18 network architecture. For this set of experiments, we compare I-SGD, SGHMC, SWAG, and VSGD using again test accuracy and MNLL, which we report in Table  4. As usual, we compare the results against the baseline of the individual network resulting from the pre-training phase. Results are obtained averaging over three independent seeds. Notice, as expanded in Appendix C that for SWAG we do consider two variants: the Bayesian correct one (SWAG) and a second variant that has better performance (SWAG wd).
Conclusions SG methods allowed Bayesian posterior sampling algorithms, such as MCMC, to regain relevance in an age when datasets have reached extremely large sizes. However, despite mathematical elegance and promising results, current approaches from the literature are restricted to simple models. Indeed, the sampling properties of these algorithms are determined by simplifying assumptions on the loss landscape, which do not hold for the kind of complex models which are popular these days, such as deep models. Meanwhile, SG-MCMC algorithms require vanishing learning rates, which force practitioners to develop creative annealing schedules that are often model specific and difficult to justify. We have attempted to target these weaknesses by suggesting a simpler algorithm that relies on fewer parameters and less strict assumptions compared to the literature on SG-MCMC.
Procedures 2. 2. 1. Data Collection Instruments All families and/or their interventionist completed a demographic form, which included information such as the caregivers' level of education, age of hearing loss identification, and amount and type of intervention. Audiologic records were reviewed by study personnel to determine a child's degree of hearing loss. At each assessment, the participants' expressive vocabulary (signed and/or spoken) was measured based on caregiver report using the MacArthur-Bates Communicative Development Inventories (Mac:CDI, Brookes, Baltimore, MD, USA)  [14]. This norm-referenced assessment has been validated with typically developing children  [15, 16]  as well as those with hearing loss  [17, 18]. Expressive vocabulary was selected as the outcome variable because vocabulary size and rate of word learning are important predictors of later language and academic skills  [19, 20].
Data Analysis: Structural Equation Model In order to test the causal direction between the frequency of intervention and expressive vocabulary, primary analyses used a three-wave, cross-lagged panel design structural equation model, with cross-lagged effects between the number of sessions reported and the child's language score. Full information maximum likelihood estimation was used in order to address missing data. Maternal level of education, degree of hearing loss (moderate-severe to profound vs. mild/moderate), and EHDI 1-3-6 status were included as control variables, and all three were allowed to correlate. All three were tested as predictors of both Time 1 vocabulary and the number of sessions at Time 1. In addition, maternal level of education was evaluated as a predictor of the Time 2 and Time 3 language scores.
Descriptive Statistics Data for this analysis were based on assessments that occurred at regularly spaced intervals for each child, with approximately 9 months elapsing between assessments. Three assessment periods were identified. The first (Time 1) was when the children were between 9 and 16 months of age (mean = 13. 3; SD = 2. 04). The second assessment (Time 2) occurred between 17 and 26 months of age (mean = 22. 3 months; SD = 2. 54), and the third assessment (Time 3) was between 27 and 36 months of age (mean = 31. 5; SD = 2. 33). All participants were assessed on at least two occasions with all 210 children receiving an assessment at Time 1, 164 assessed at Time 2, and 130 with an assessment at Time 3. Some children were unable to be assessed at all three time points depending on the date of their enrollment in this study and when this study concluded.
Data Collection We retrospectively compiled data from patients' electronic medical records and then standardized it in a report file. The collected data included epidemiological, clinical and biochemical parameters. Symptom onset date was recorded. Patients' current treatments and medical history, including cardiovascular diseases, diabetes, pre-existing renal failure, cancer and hematological diseases, were also collected. In addition, the autonomy of each patient was recorded using the Knaus score  [19]. In this study, the severity of disease was defined by admission to the Intensive Care Unit (ICU). Obesity was defined by a body mass index superior to 30 kg/m 2. Standard biochemical parameters, such as creatinine, Creactive protein (CRP), total leukocytes and lymphocytes, were also collected.
Concerning circulating eosinophils, their normal rate stands between 100 and 400/mm 3 , eosinopenia is considered profound when the rate is inferior to 50/mm 3 and undetectable when it equals 0/mm 3  [7] [8] [9] [10] [11] [12]. Lastly, we measured eosinophil count variation, delta, the difference between circulating eosinophils rate at admission in the ED, and after 24 hours of ED management (H-24). All collected data are summarized in the results sections.
a b s t r a c t The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a microspectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R)  [1]  , but could be used more broadly to test new chemometrics methods.
For example, the data could be used to evaluate different transfer functions between spectroscopic systems  [2]. This dataset enabled the research work reported by  Mallet et al. 2021   [3]. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Value of the Data • Near infrared spectroscopy provides a fast and non-destructive methodology to characterize solid organic waste substrates involved in bioprocesses such as anaerobic digestion or composting. • This unique dataset allows to compare the analytical performance of different compact spectroscopic systems for organic waste characterization including a handheld micro spectrometer, and two potential at-line systems with an immersed probe or polarized light system  [4]. These spectroscopic systems can be compared to a laboratory spectrometer considered as the standard instrument of reference. • The dataset could be used as well for instrument standardization  [2]  to test different strategies for building transfer models between instruments such as piecewise direct standardization (PDS)  [5]  , or transfer by orthogonal projection (TOP)  [6].